[House Hearing, 111 Congress]
[From the U.S. Government Publishing Office]


 
                    THE RISKS OF FINANCIAL MODELING:
                     VAR AND THE ECONOMIC MELTDOWN

=======================================================================

                                HEARING

                               BEFORE THE

                   SUBCOMMITTEE ON INVESTIGATIONS AND
                               OVERSIGHT

                  COMMITTEE ON SCIENCE AND TECHNOLOGY
                        HOUSE OF REPRESENTATIVES

                     ONE HUNDRED ELEVENTH CONGRESS

                             FIRST SESSION

                               __________

                           SEPTEMBER 10, 2009

                               __________

                           Serial No. 111-48

                               __________

     Printed for the use of the Committee on Science and Technology


     Available via the World Wide Web: http://www.science.house.gov


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                                 ______

                  COMMITTEE ON SCIENCE AND TECHNOLOGY

                   HON. BART GORDON, Tennessee, Chair
JERRY F. COSTELLO, Illinois          RALPH M. HALL, Texas
EDDIE BERNICE JOHNSON, Texas         F. JAMES SENSENBRENNER JR., 
LYNN C. WOOLSEY, California              Wisconsin
DAVID WU, Oregon                     LAMAR S. SMITH, Texas
BRIAN BAIRD, Washington              DANA ROHRABACHER, California
BRAD MILLER, North Carolina          ROSCOE G. BARTLETT, Maryland
DANIEL LIPINSKI, Illinois            VERNON J. EHLERS, Michigan
GABRIELLE GIFFORDS, Arizona          FRANK D. LUCAS, Oklahoma
DONNA F. EDWARDS, Maryland           JUDY BIGGERT, Illinois
MARCIA L. FUDGE, Ohio                W. TODD AKIN, Missouri
BEN R. LUJAN, New Mexico             RANDY NEUGEBAUER, Texas
PAUL D. TONKO, New York              BOB INGLIS, South Carolina
PARKER GRIFFITH, Alabama             MICHAEL T. MCCAUL, Texas
STEVEN R. ROTHMAN, New Jersey        MARIO DIAZ-BALART, Florida
JIM MATHESON, Utah                   BRIAN P. BILBRAY, California
LINCOLN DAVIS, Tennessee             ADRIAN SMITH, Nebraska
BEN CHANDLER, Kentucky               PAUL C. BROUN, Georgia
RUSS CARNAHAN, Missouri              PETE OLSON, Texas
BARON P. HILL, Indiana
HARRY E. MITCHELL, Arizona
CHARLES A. WILSON, Ohio
KATHLEEN DAHLKEMPER, Pennsylvania
ALAN GRAYSON, Florida
SUZANNE M. KOSMAS, Florida
GARY C. PETERS, Michigan
VACANCY
                                 ------                                

              Subcommittee on Investigations and Oversight

                HON. BRAD MILLER, North Carolina, Chair
STEVEN R. ROTHMAN, New Jersey        PAUL C. BROUN, Georgia
LINCOLN DAVIS, Tennessee             BRIAN P. BILBRAY, California
CHARLES A. WILSON, Ohio              VACANCY
KATHY DAHLKEMPER, Pennsylvania         
ALAN GRAYSON, Florida                    
BART GORDON, Tennessee               RALPH M. HALL, Texas
                DAN PEARSON Subcommittee Staff Director
                  EDITH HOLLEMAN Subcommittee Counsel
            JAMES PAUL Democratic Professional Staff Member
       DOUGLAS S. PASTERNAK Democratic Professional Staff Member
           KEN JACOBSON Democratic Professional Staff Member
            TOM HAMMOND Republican Professional Staff Member
                   MOLLY O'ROURKE Research Assistant
                    ALEX MATTHEWS Research Assistant


                            C O N T E N T S

                           September 10, 2009

                                                                   Page
Witness List.....................................................     2

Hearing Charter..................................................     3

                           Opening Statements

Statement by Representative Brad Miller, Chairman, Subcommittee 
  on Investigations and Oversight, Committee on Science and 
  Technology, U.S. House of Representatives......................     6
    Written Statement............................................     8

Statement by Representative Paul C. Broun, Ranking Minority 
  Member, Subcommittee on Investigations and Oversight, Committee 
  on Science and Technology, U.S. House of Representatives.......     9
    Written Statement............................................    10

                                Panel I:

Dr. Nassim N. Taleb, Distinguished Professor of Risk Engineering, 
  Polytechnic Institute of New York University; Principal, 
  Universa Investments L.P.
    Oral Statement...............................................    11
    Written Statement............................................    13
    Biography....................................................    56

Dr. Richard Bookstaber, Financial Author
    Oral Statement...............................................    56
    Written Statement............................................    59
    Biography....................................................    67

Discussion
  Can Economic Events Be Predicted?..............................    67
  Regulation of Financial Products...............................    69
  `Too Big to Fail'?.............................................    70
  Wall Street's Dependency on Government Bailouts................    72
  The Risks of Different Tupes of Institutions...................    74
  Incentive Structures for Trades................................    75
  Holding Wall Street Accountable for Bonuses....................    78
  Malpractice in Risk Management.................................    79
  Clawback Provisions............................................    80
  Credit Default Swaps...........................................    81
  Were the Bailouts and Stimulus Funds Necessary?................    82

                               Panel II:

Dr. Gregg E. Berman, Head of Risk Business, RiskMetrics Group
    Oral Statement...............................................    84
    Written Statement............................................    86
    Biography....................................................   105

Mr. James G. Rickards, Senior Managing Director for Market 
  Intelligence, Omnis, Inc., McLean, VA
    Oral Statement...............................................   106
    Written Statement............................................   108
    Biography....................................................   116

Mr. Christopher Whalen, Managing Director, Institutional Risk 
  Analytics
    Oral Statement...............................................   117
    Written Statement............................................   118
    Biography....................................................   124

Dr. David Colander, Christian A. Johnson Distinguished Professor 
  of Economics, Middlebury College
    Oral Statement...............................................   124
    Written Statement............................................   127
    Biography....................................................   141

Discussion
  Appropriate Uses of Financial Models...........................   141
  Proposals for Avoiding Recurrences of Financial Problems.......   144
  Abuse of the VaR...............................................   145
  Past Congressional Attempts to Regulate the Financial Industry.   145
  Should a Government Agency Test Financial Products for 
    Usefulness?..................................................   146
  Identifying Firms That Are `Too Big to Fail'...................   148
  Monitoring and Analyzing Hedge Fund Activity and Risk..........   149


     THE RISKS OF FINANCIAL MODELING: VAR AND THE ECONOMIC MELTDOWN

                              ----------                              


                      THURSDAY, SEPTEMBER 10, 2009

                  House of Representatives,
      Subcommittee on Investigations and Oversight,
                       Committee on Science and Technology,
                                                    Washington, DC.

    The Subcommittee met, pursuant to call, at 10:04 a.m., in 
Room 2318 of the Rayburn House Office Building, Hon. Brad 
Miller [Chairman of the Subcommittee] presiding.



                            hearing charter

              SUBCOMMITTEE ON INVESTIGATIONS AND OVERSIGHT

                  COMMITTEE ON SCIENCE AND TECHNOLOGY

                     U.S. HOUSE OF REPRESENTATIVES

                    The Risks of Financial Modeling:

                     VaR and the Economic Meltdown

                      thursday, september 10, 2009
                          10:00 a.m.-1:00 p.m.
                   2318 rayburn house office building

Purpose

    The Subcommittee on Investigations and Oversight on Sept. 10, 2009 
convenes the first Congressional hearing to examine the role of risk 
modeling in the global financial meltdown. Risk models, and 
specifically a method of risk measurement known as Value-at-Risk, or 
VaR, are widely viewed as an important factor in the extreme risk-
taking that financial institutions engaged in leading to last year's 
economic upheaval. That risk-taking has led to hundreds of billions of 
dollars in losses to financial firms, and to a global recession with 
trillions of dollars in direct and indirect costs imposed on U.S. 
taxpayers and working families.
    Given the central role of credit in the economy, the ability of 
major financial institutions to operate without assuming undue risks 
that gamble with the stability of the financial system, thereby 
endangering the broader economy, is of the utmost importance to both 
business and the public at large. The recent behavior by financial 
firms that are deemed ``too big to fail'' suggests that the financial 
system as currently structured and regulated creates a ``moral hazard'' 
because firms can expect that they will be bailed out if their risk-
taking fails to pay off. This is exactly what happened in the United 
States in October of 2008 with great consequences to the taxpayers, who 
have been called upon to shoulder much of the huge burden arising from 
financial firms' underestimation of risk, poor judgment, and profligate 
behavior. Relied on to guide the decisions of both financial firms and 
federal regulators responsible for monitoring their soundness by 
ensuring that they have sufficient capital, the VaR, whether it was 
misused or not, was involved in inducing or allowing this situation to 
arise.
    Given this dual function, it is critical that the Subcommittee 
examine: the role of the VaR and related risk-measurement methods in 
the current world financial crisis; the strengths and weaknesses of, 
and the limits to, the usefulness of the VaR; the degree to which the 
VaR is understood, and may be manipulated, within the institutions 
where it is in use; and the capabilities and needs of federal 
supervisors who may be called upon to work with the VaR in carrying out 
their regulatory duties. From a policy perspective, the most important 
question is how regulators will use VaR numbers produced by firms and 
whether it is an appropriate guide to setting capital reserve 
requirements.
    This is the second in a series of hearings on how economic thinking 
and methods have been used by policy-makers both inside and outside of 
government.

The VaR's Origins and Use

    Risk assessment models in the financial industry are the product of 
advances in economic and statistical methods developed in the social 
sciences over the last fifty years. J.P. Morgan adopted these 
techniques in developing the VaR in the 1980s as a tool to measure the 
risk of loss to its traders' portfolios. The VaR could produce a single 
number rating a trader's (or, in aggregate, the firm's cumulative) risk 
of loss of portfolio value over a specific period of time at a given 
level of confidence. The VaR provided managers a tool that appeared to 
allow them to keep a handle on the risks they were taking as financial 
instruments became more varied and complex and as assets became more 
difficult to value. Morgan decided to give the methodology of the VaR 
away, forming the now-independent RiskMetrics Group; this resulted in 
the VaR rapidly becoming ``so popular that it was considered the risk-
model gold standard.'' \1\
---------------------------------------------------------------------------
    \1\ ``Risk Management,'' by Joe Nocera, New York Times, Jan. 4, 
2009. J.P. Morgan was not the only firm to look for statistical tools 
to measure the risks of their portfolios, however Morgan's model became 
the most widely used. The model can be tweaked in many, many ways to 
meet the specific needs of a particular firm.
---------------------------------------------------------------------------
    To put it very simply, the VaR captures the probability of outcomes 
distributed along a curve-most commonly a ``bell'' or normal 
distribution. It provides an answer to the question of, ``what is 
likely to happen tomorrow to the value of an asset?'' by drawing from 
historical performance data. The highest probability of tomorrow's 
value is that it will be the same as today's value; the next highest 
probability is for a very small movement in value up or down, and so 
on. The more radical the movement in value, the lower the probability 
of that occurring. A manager may ask for a projection of the potential 
loss of an asset or portfolio at the 95 percent or even the 99 percent 
confidence level. At those levels, a complete loss of value is 
unlikely. The complete collapse of an asset or portfolio's value is not 
a 1-in-100 event; such a collapse is more likely a 1-in-500 or 1-in-
10,000 or event. The VaR is unlikely to warn, then, of great shifts in 
value. The danger to the financial firm or the community comes at the 
extreme margins of the distribution curves produced by the VaR. As a 
map to day-to-day behavior, the VaR is probably pretty accurate for 
normal times, but for asset bubbles or other ``non-normal'' market 
conditions, the VaR is likely to misrepresent risks and dangers.
    While the VaR was originally designed for financial institutions' 
use in-house, it has subsequently been given a key role in determining 
capital requirements for large banks under a major multilateral 
agreement, the Basel II Accord, published in 2004. That same year, the 
U.S. Securities and Exchange Commission adopted a capital regime 
applying Basel II standards to the Nation's largest investment 
banks,\2\ a move that has been viewed as playing a role in those 
institutions' subsequent over-leveraging and liquidity problems. Those 
financial institutions assured regulators that the VaR was a way to see 
the level of risk they were taking on and a low VaR justified lower 
reserve requirements. (The terms of Basel II are currently being re-
evaluated in light of the global economic crisis.)
---------------------------------------------------------------------------
    \2\ ``Alternative Net Capital Requirements for Broker-Dealers That 
are Part of Consolidated Supervised Entities; Supervised Investment 
Bank Holding Companies; Final Rules,'' Securities and Exchange 
Commission, June 21, 2004, 69 FR 34428-72. (According to Aswath 
Damodaran, Professor of Finance at the NYU Stern School of Business, 
``The first regulatory measures that evoke Value-at-Risk, though, were 
initiated in 1980, when the SEC tied the capital requirements of 
financial service firms to the losses that would be incurred, with 95 
percent confidence over a thirty-day interval, in different security 
classes; historical returns were used to compute these potential 
losses. Although the measures were described as haircuts and not as 
Value or Capital at Risk, it was clear the SEC was requiring financial 
service firms to embark on the process of estimating one month 95 
percent VaRs and hold enough capital to cover the potential losses.'' 
Damodaran, ``Value-at-Risk (VAR),'' found at http://
pages.stern.nyu.edu/?adamodar/pdfiles/papers/VAR.pdf)
---------------------------------------------------------------------------
    Along with extensive use, the VaR has come in for extensive 
criticism. Although its merits were debated at least as far back as 
1997,\3\ criticism of the VaR has mounted in the wake of last year's 
collapse of such major financial institutions as Bear Stearns and 
Lehman Brothers. Among the allegations: that the VaR is inadequate in 
capturing risks of extreme magnitude but low probability, to which an 
institution may be left vulnerable; that this shortcoming may open it 
to manipulation by traders taking positions that seem profitable but 
whose risks they know the VaR is unlikely to pick up, and that such 
``gaming'' can increase extreme risk; and that use of the VaR, derided 
for ``quantify[ing] the immeasurable with great precision,'' \4\ 
promotes an unfounded sense of security within financial institutions 
creating an environment where firms take on more risk than they would 
without the security-blanket of a VaR number.
---------------------------------------------------------------------------
    \3\ ``The Jorion-Taleb Debate,'' DerivativesStrategy.com, April 
1997, http://www.derivativesstrategy.com/magazine/archive/1997/
0497fea2.asp
    \4\ ``Against VAR,'' by Nassim Taleb, in ``The Jorion-Taleb 
Debate,'' ibid.
---------------------------------------------------------------------------
    Those who advocate for the VaR argue that any misuse of the model 
is not the model's fault and that it remains a useful management tool. 
VaR defenders' argue that its purpose is ``not to describe the worst 
possible outcomes;'' \5\ that it is essential to the ability of a 
financial institution to arrive at an estimate of its overall risk; and 
that in ``computing their VAR[, institutions] are forced to confront 
their exposure to financial risks and to set up a proper risk 
management function,'' so that ``the process of getting to VAR may be 
as important as the number itself.'' \6\ Some also argue that the VaR 
remains a useful tool for regulators to use as a baseline for 
establishing reserve requirements for ``normal'' times.
---------------------------------------------------------------------------
    \5\ ``In Defense of VAR,'' by Philippe Jorion, in ``The Jorion-
Taleb Debate,'' ibid.
    \6\ Jorion, idem.

---------------------------------------------------------------------------
Witnesses

Panel I

Dr. Nassim Nicholas Taleb, Distinguished Professor of Risk Engineering, 
Polytechnic Institute of New York University.

Dr. Richard Bookstaber, Financial Author

Panel II

Dr. Gregg Berman, Head of Risk Business, RiskMetrics Group

Mr. James G. Rickards, Senior Managing Director, Omnis Inc.

Mr. Christopher Whalen, Managing Director, Institutional Risk Analytics

Dr. David Colander, Christian A. Johnson Distinguished Professor of 
Economics, Middlebury College
    Chairman Miller. Good morning, and welcome to today's 
hearing: ``The Risks of Financial Modeling: VaR and the 
Economic Meltdown.''
    Economics has not been known in the past for mathematical 
precision. Harry Truman said he wanted a one-handed economist 
because he was frustrated with economists who equivocated by 
saying on the one hand, on the other hand. George Bernard Shaw 
said that if all the world's economists were laid end to end, 
they still wouldn't reach a conclusion. And apparently no one 
is sure who first observed that economics was the only field in 
which it was possible for two people to share a Nobel Prize for 
reaching exactly the opposite conclusion about the same 
question.
    In the last 15 or 20 years, math and physics Ph.D.s from 
academia and the laboratory have entered the financial sector. 
Quantitative analysts, or `quants,' directed their mathematical 
and statistical skills to financial forecasts at a time when 
global financial markets were becoming more interdependent than 
ever before.
    The quants conceived such financial instruments as 
collaterized debt obligations, or CDOs, and credit default 
swaps, or CDSs, that would never have existed without them and 
their computers. They developed strategies for trading those 
instruments even in the absence of any underlying security or 
any real market; for that matter, in the absence of anything at 
all. They constructed risk models that convinced their less 
scientifically and technologically adept bosses that their 
instruments and strategies were infallibly safe. And their 
bosses spread the faith in the quants' models to regulators, 
who agreed to apply them to establish capital reserve 
requirements that were supposed to guarantee the soundness of 
financial institutions against adverse events. It almost seemed 
like the economic models had brought the precision of the laws 
of physics, the same kind of certainty about the movement of 
the planets, to financial risk management. Engineering schools 
even offered courses in ``financial engineering.''
    The supposedly immutable laws underlying the quants' 
models, however, didn't work out, and the complex models turned 
out to have hidden risks rather than protecting against them, 
all at a terrible cost. Those risks, concealed and maybe even 
encouraged by the models, have led to hundreds of billions of 
dollars in losses to investors and taxpayers, to a global 
recession imposing trillions of dollars in losses to the world 
economy and immeasurable monetary and human costs. People 
around the world are losing their homes, their jobs, their 
dignity and their hope.
    Taxpayers here and around the world are shouldering the 
burden arising from financial firms' miscalculation of risk, 
poor judgment, excessive bonuses and general profligate 
behavior. It is for this reason that the Subcommittee is 
directing our attention today to the intersection of 
quantitative analysis, economics and regulation. The Value-at-
Risk model, or VaR, stands squarely at the intersection of 
quantitative analysis, economics and regulation. It is the most 
prominent risk model used by major financial institutions. The 
VaR is designed to provide an answer to the question, ``What is 
the potential loss that could be faced within a limited, 
specified time to the value of an asset?''
    The highest probability is that tomorrow's value will be 
the same as today's. The next highest probability is that there 
will be a small movement in value up or down, and so on. The 
more radical the movement in value, the lower the probability 
that it will happen. In other words, the danger to a financial 
firm or the community comes at the extreme margins of the VaR 
distribution curve, in the tails of the distribution. As a map 
to day-to-day behavior, the VaR is probably pretty accurate for 
normal times, just as teams favored by odds makers usually win. 
But just as long shots sometimes come home, just as underdogs 
do sometimes win, asset bubbles or other non-normal market 
conditions also occur, and the VaR is unlikely to capture the 
risks and dangers. The VaR also cannot tell you when you have 
moved into non-normal market conditions.
    While the VaR was originally designed for financial 
institutions' in-house use to evaluate short-term risk in their 
trading books, it has been given a key role in determining 
capital requirements for large banks under a major multilateral 
agreement, the Basel II Accord, published in 2004. That same 
year, the U.S. Securities and Exchange Commission, the SEC, at 
the instigation of the five largest investment banks, adopted a 
capital reserve regime, applying Basel II standards to the 
Nation's largest investment banks--a decision that opened the 
door to their over-leveraging and liquidity problems. Three of 
the institutions that asked the SEC for this change in rules--
Bear Stearns, Merrill Lynch, Lehman Brothers--no longer exist. 
At the time, those financial institutions assured regulators 
that the VaR would reflect the level of risk they were taking 
on, and that a low VaR justified lower capital requirements. 
The result was exactly what the investment banks asked for: 
lower capital requirements that allowed them to invest in even 
more risky financial instruments all justified with risk models 
that assured regulators that there was nothing to worry about. 
What could possibly go wrong?
    In light of the VaR's prominent role in the financial 
crisis, this subcommittee is examining that role and the role 
of related risk-measurement methods. From a policy perspective, 
the most important immediate question is how regulators use VaR 
numbers and other such models designed by regulated 
institutions, and whether they are an appropriate guide to 
setting capital reserve requirements. But, beyond that, we must 
also ask whether the scientific and technical capabilities that 
led us into the current crisis should be applied to prevent 
future catastrophic events. Can mathematics, statistics and 
economics produce longer-range models, more reliable models, 
that could give us early warning when our financial system is 
headed for trouble? Or are such models inevitably going to be 
abused to hide risk-taking and encourage gambling by firms 
whose failures can throw the whole world into a recession, as 
they have in the last couple of years? If models cannot be a 
useful guide for regulation, should we just abandon the 
approach, or simply increase reserves, which will reduce 
profits and perhaps reduce some useful economic conduct in the 
short run, but protect taxpayers and the world economy in the 
long run?
    Those are big questions, but the stakes for taxpayers and 
investors and the world economy justify some effort to get at 
some answers.
    I now recognize Dr. Broun for his opening statement.
    [The prepared statement of Chairman Miller follows:]

               Prepared Statement of Chairman Brad Miller

    Economics has not been known in the past for mathematical 
precision. Harry Truman said he wanted a one-handed economist because 
he was frustrated with economists who equivocated by saying ``on the 
one hand . . . on the other hand.'' George Bernard Shaw said that if 
all the world's economists were laid end to end, they still wouldn't 
reach a conclusion. And apparently no one knows who first observed that 
economics was the only field in which two people can share a Nobel 
Prize for reaching exactly the opposite conclusion.
    But in the last 15 or 20 years, math and physics Ph.D.s from 
academia and the laboratory have entered the financial sector. 
Quantitative analysts, or ``quants,'' directed their mathematical and 
statistical skills to financial forecasts at a time when global 
financial markets were becoming more interdependent than ever before.
    The quants conceived such financial instruments as collaterized 
debt obligations, or ``CDOs,'' and credit default swaps, or ``CDSs,'' 
that would never have existed without them and their computers. They 
developed strategies for trading those instruments even in the absence 
of any underlying security or any real market. They constructed risk 
models that convinced their less scientifically and technologically 
adept bosses that their instruments and strategies were infallibly 
safe. And their bosses spread faith in the quants' models to 
regulators, who agreed to apply them to establish capital reserve 
requirements that were supposed to guarantee the soundness of financial 
institutions against adverse events. It almost seemed like economic 
models had brought the precision of the laws of physics to financial 
risk management. Engineering schools even offered courses in 
``financial engineering.''
    The supposedly immutable ``laws'' underlying the quants' models 
didn't work, and the complex models turn out to have hidden risks 
rather than protected against them, all at a terrible cost. Those 
risks--concealed and maybe even encouraged by the models--have led to 
hundreds of billions of dollars in losses to investors and the 
taxpayers, to a global recession imposing trillions of dollars in 
losses to the world economy and immeasurable monetary and human costs. 
People around the world are losing their jobs, their homes, their 
dignity and their hope.
    Taxpayers here and around the world are shouldering the burden 
arising from financial firms' miscalculation of risk, poor judgment, 
excessive bonuses and profligate behavior. It is for this reason that 
the Subcommittee has chosen to direct its attention today to that 
intersection of quantitative analysis, economics, and regulation. The 
``Value-at-Risk'' model, or ``VaR'' stands squarely at the center of 
this intersection as the most prominent risk model used by major 
financial institutions. The VaR is designed to provide an answer to the 
question, ``What is the potential loss that could be faced within a 
limited, specified time to the value of an asset?''
    The highest probability is that tomorrow's value will be the same 
as today's; the next highest probability is of a very small movement in 
value up or down, and so on. The more radical the movement in value, 
the lower the probability of its occurrence. In other words, the danger 
to the financial firm or the community comes at the extreme margins of 
the VaR distribution curve, in the ``tails'' of the distribution. As a 
map to day-to-day behavior, the VaR is probably pretty accurate for 
normal times, just as teams favored by odds makers usually win. But 
just as long shots sometimes come home, asset bubbles or other ``non-
normal'' market conditions also occur, and the VaR is unlikely to 
capture the risks and dangers. The VaR also cannot tell you when you 
have moved into ``non-normal'' market conditions.
    While the VaR was originally designed for financial institutions' 
to use in-house to evaluate short-term risk in their trading books, it 
was given a key role in determining capital requirements for large 
banks under a major multilateral agreement, the Basel II Accord, 
published in 2004. That same year, the U.S. Securities and Exchange 
Commission, at the instigation of the five largest investment banks, 
adopted a capital reserve regime applying Basel II standards to the 
Nation's largest investment banks, a decision that opened the door to 
their over-leveraging and liquidity problems. Three of the institutions 
that asked the SEC for this change in rules--Bear Stearns, Merrill 
Lynch, Lehman Brothers--no longer exist. At the time, those financial 
institutions assured regulators that the VaR would reflect the level of 
risk they were taking on, and that a low VaR justified lower reserve 
requirements. The result was exactly what the investment banks asked 
for; lower capital reserve requirements that allowed them to invest in 
even more risky financial instruments all justified with risk models 
that assured regulators that there was nothing to worry about.
    In light of the VaR's prominent role in the financial crisis, this 
Subcommittee is examining that role and the role of related risk-
measurement methods. From a policy perspective, the most important 
immediate question is how regulators use VaR numbers and other such 
models devised by regulated institutions and whether they are an 
appropriate guide to setting capital reserve requirements. But, beyond 
that, we must also ask whether the scientific and technical 
capabilities that helped lead us into the current crisis should be 
applied to prevent future catastrophic events. Can mathematics, 
statistics, and economics produce longer-range models--models that 
could give us early warning of when our complex financial system is 
heading for trouble? Or are such models inevitably going to be abused 
to hide risk-taking and encourage excessive gambling by firms whose 
failures can throw the whole world into a recession? If models cannot 
be a useful guide for regulation, should we just abandon this approach 
and simply increase reserves, reducing profits and perhaps some useful 
economic conduct in the short run, but protecting taxpayers and the 
world economy in the long run?
    These are big questions, but the stakes for taxpayers and investors 
and the world economy justify the effort to get at some answers.
    I now recognize Mr. Broun for his opening statement.

    Mr. Broun. Thank you, Mr. Chairman. Let me welcome the 
witnesses here today and thank them for appearing. Today's 
hearing on financial modeling continues this committee's work 
on the role of science in finance and economics.
    As I pointed out in our previous hearing in May, for the 
last several years Wall Street has increasingly leveraged 
mathematics, physics and science to better inform their 
decisions. Even before Value-at-Risk was developed to 
characterize risk, bankers and economists were looking for a 
silver bullet to help them to beat the market.
    Despite the pursuit of a scientific panacea for financial 
decisions, models are simply tools employed by decision-makers 
and risk managers. They add another layer of insight but are 
not crystal balls. Leveraging a position too heavily or 
assuming future solvency based on modeling data alone is 
hazardous, to say the least. Conversely, it stands to reason 
that if we could accurately predict markets, then both losses 
and profits would be limited since there would be very little 
risk involved.
    Modeling is a subject this committee has addressed several 
times in the past, whether it is in regard to climate change, 
chemical exposures, pandemics, determining spacecraft 
survivability or attempting to value complex financial 
instruments. Models are only as good as the data and 
assumptions that go into them. Ultimately decisions have to be 
made based on a number of variables which should include 
scientific models but certainly not exclusively. As witnesses 
in our previous hearing stated, ``Science describes, it does 
not prescribe.'' No model will ever relieve a banker, trader or 
risk manager of the responsibility to make difficult decisions 
and hedge inevitable uncertainly.
    This committee struggles enough with the complexities of 
modeling, risk assessment and risk management regarding 
physical sciences. Attempting to adapt those concepts to 
economics and finance is even more complex. Appreciating this 
complexity and understanding the limitations and intended 
purpose of financial models is just as important as what the 
models tell you.
    We have two esteemed panels of witnesses here today who 
will discuss appropriate roles and limitations of models such 
as VaR. They will explain how these models are used and shed 
some light on what role they may have played in the recent 
economic crisis. I look forward to you all's testimony and I 
yield back my time. Thank you, Mr. Chairman.
    [The prepared statement of Mr. Broun follows:]

           Prepared Statement of Representative Paul C. Broun

    Thank you Mr. Chairman.
    Let me welcome the witnesses here today and thank them for 
appearing.
    Today's hearing on Financial Modeling continues this committee's 
work on the role of science in finance and economics.
    As I pointed out at our previous hearing in May, over the last 30 
years Wall Street has increasingly leveraged mathematics, physics, and 
science to better inform their decisions. Even before Value-at-Risk 
(VaR) was developed to characterize risk, bankers and economists were 
looking for a silver bullet to help them beat the market.
    Despite the pursuit of a scientific panacea for financial 
decisions, models are simply tools employed by decision-makers and risk 
managers. They add another layer of insight, but are not crystal balls. 
Leveraging a position too heavily or assuming future solvency based on 
modeling data alone is hazardous to say the least. Conversely, it 
stands to reason that if we could accurately predict markets, then both 
losses and profits would be limited since there would be very little 
risk involved.
    Modeling is a theme this committee has addressed several times in 
the past. Whether it is in regard to climate change, chemical 
exposures, pandemics, determining spacecraft survivability, or 
attempting to value complex financial instruments, models are only as 
good as the data and assumptions that go into them. Ultimately, 
decisions have to be made based on a number of variables which should 
include scientific models, but certainly not exclusively. As a witness 
at a previous hearing stated, ``science describes, it does not 
prescribe.'' No model will ever relieve a banker, trader, or risk 
manager of the responsibility to make difficult decisions and hedge for 
inevitable uncertainty.
    This committee struggles enough with the complexities of modeling, 
risk assessment, and risk management regarding physical sciences. 
Attempting to adapt those concepts to economics and finance is even 
more complex. Appreciating this complexity, and understanding the 
limitations and intended purpose of financial models is just as 
important as what the models tell you.
    We have two esteemed panels of witnesses here today who will 
discuss the appropriate roles and limitations of models such as VaR. 
They will explain how these models are used and shed some light on what 
role they may have played in the recent economic crisis. I look forward 
to their testimony and yield back my time.
    Thank you.

    Chairman Miller. Thank you, Dr. Broun.
    I now ask unanimous consent that all additional opening 
statements submitted by Members be included in the record. 
Without objection, that is so ordered.

                                Panel I:

    We do have an outstanding group of witnesses today. I know 
that Chairmen at hearings always say that but it is certainly 
true. This time I mean it. On our first panel, we have two very 
well known and respected authors whose books and other writings 
warned against many of the practices of the financial industry 
that resulted in the current economic meltdown. Both of them 
have years of experience on Wall Street. Dr. Nassim Taleb is 
the author of ``Fooled by Randomness'' and ``The Black Swan.'' 
After a career as a trader and fund manager, Dr. Taleb is now 
the Distinguished Professor of Risk Engineering at the 
Polytechnic Institute of New York University. And if you are 
one of that slice of the American population for whom Bloomberg 
and CNBC are your favorite TV channels, Dr. Taleb is a rock 
star. Dr. Taleb is joined by another rock star, Dr. Richard 
Bookstaber, who is the author of ``A Demon of Our Own Design: 
Markets, Hedge Funds and the Risk of Financial Innovation.'' 
Dr. Bookstaber has worked as a risk manager for Salomon 
Brothers, Morgan Stanley and Moore Capital Management. He also 
runs equity funds and he began on Wall Street designing 
derivative instruments. Does your mother know about that?
    As our witnesses should know, you will each have five 
minutes for your spoken testimony. Your written testimony will 
be included in the record for the hearing. When you all have 
completed your spoken testimony, we will begin with questions 
and each Member will have five minutes to question the panel. 
It is the practice of this subcommittee--it is an investigative 
and oversight subcommittee--to receive testimony under oath. As 
I pointed out to the panelists at our last hearing on economic 
issues, to prosecute a case for perjury, the prosecutor, the 
U.S. attorney would have to prove what the truth was, that you 
knew the truth and that you consciously departed from it. I 
think you can sleep easily without worrying about a prosecution 
for perjury, but we will ask you to take an oath. Do either of 
you have any objection to taking an oath? Okay. You also have 
the right to be represented by counsel. Do either of you have 
counsel here? If you would, please stand and raise your right 
hand. Do you swear to tell the truth and nothing but the truth?
    The record will reflect that both witnesses did take the 
oath. We will begin with Dr. Taleb. Dr. Taleb.

 STATEMENT OF DR. NASSIM N. TALEB, DISTINGUISHED PROFESSOR OF 
RISK ENGINEERING, POLYTECHNIC INSTITUTE OF NEW YORK UNIVERSITY; 
              PRINCIPAL, UNIVERSA INVESTMENTS L.P.

    Dr. Taleb. Mr. Chairman, Ranking Member, Members of the 
Committee, thank you for giving me this opportunity to testify 
on the risk measurement methods used by banks, particularly 
those concerned with the risks of VaR events. You know, Value-
at-Risk is just a method. It is a very general method, not very 
precise method, that measures the risks of VaR events. For 
example, a standard daily Value-at-Risk tells you that if your 
VaR is a million, daily VaR is a million, you have--it is at 
one percent probability, that you have less than one percent 
chance of losing a million or more on a given day. There are of 
course a lot of variations around VaR. For me, they are equally 
defective.
    Thirteen years ago, I wrote that the VaR encourages 
misdirected people to take risks with shareholders' and 
ultimately taxpayers' money--that is, regular people's money. I 
have been since begging for suspension of these measurements of 
tail risks. We just don't understand tail events. And lot of 
people say, oh, let's measure risks. My idea is very different. 
Let's find what risks we can measure and any other risks we 
should be taking instead of doing it the opposite way. We take 
a lot of risks and then we try to find some scientists who can 
confirm these methods, you know, that these risks we can 
measure and that these methods are sound.
    I have been begging, and actually I wrote that I would be 
on the witness stand 13 years ago, and today I am here. The 
banking system lost so far more than $4.3 trillion, according 
to the International Monetary Fund--that is more than they ever 
made in the history of banking--on tail risks, measurements of 
rare events. Most of the losses of course were in the United 
States, and I am not counting the economic consequences. But 
this shouldn't have happened. Data shows that banks routinely 
lose everything they made over a long period of time in one 
single blow-up. It happened in 1982 because of multi-center 
banks losing everything made in the history of multi-center 
banking, one single event, loans to Latin America. The same 
thing in variation happened in 1991, and of course now. And 
every time society bails them out. Bank risk takers retain 
their bonuses and say oh, one fluke, all right, and we start 
again. This is an aberrant case of capitalism for the profit, 
and socialism for the losses.
    So I have five points associated with VaR that I will go 
over very quickly, and I will give my conclusion. Number one: 
these problems were obvious all along. This should not have 
happened. We knew about the defects of the VaR when it was 
introduced. A lot of traders, a lot of my friends, everyone--I 
am not the only person ranting against VaR. A lot of people 
were ranting against it before. Nobody heard us. Regulators did 
not listen to anyone who knew what was going on, is my point 
number one.
    Point number two: VaR is ineffective. I guess I don't need 
more evidence than the recent events to convince you.
    Point number three, and that to me is crucial. You have a 
graph that shows you the performance profile of someone making 
steady earnings for a long time and then losing back 
everything. You can see from that graph, figure one on page 
four, that this is a strategy that is pretty much pursued by 
the majority of people on Wall Street, by banks. They make 
steady income for a long time, and when they blow up, they say, 
well, you know, it was unexpected, it was a black swan. I wrote 
a book called ``The Black Swan.'' Unfortunately, they used my 
book backwards. Oh, and it was unexpected, highly unexpected. 
They keep their bonuses. They go on vacation and here you have 
a regular person working very hard, a taxpayer, a taxi driver, 
a post office worker paying taxes to subsidize retrospectively, 
all right, bonuses made. For example, a former government 
official made $121 million in bonuses at Citibank. Okay. He 
keeps his bonuses. We retrospectively are paying for that. That 
I said 13 years ago, and it keeps happening, and now we are 
still in the same situation.
    So number four, and that is another crucial point. VaR has 
side effects. It is not neutral. You give someone a number--it 
has been shown and shown repeatedly, if you give someone a 
number, he will act on that number even if you tell him that 
that number is random. We humans cannot be trusted with 
numbers. You don't give someone the map of the Alps if he is on 
the Mount Ararat, all right, because he is going to act on that 
map. Even nothing is alot better, if it doesn't work. This is 
my central point, the side effects of numerical precision given 
to people who do not need it.
    Number five: VaR-style quantitative risk management was 
behind leverage. We increased our leverage in society as we 
thought we thought we could measure risk. If you think you can 
measure your blow-up risk, you are going to borrow, you know. 
You have more overconfidence, also, as a side effect of 
measurement, and you are going to borrow. Instead of, you know, 
taking equity from people, you borrow, so when you blow up, you 
owe that money. And of course, as was discussed in my paper, 
debt bubbles are very vicious. Equity bubbles are not very 
vicious.
    Conclusion: What should we be doing? Well, regulators 
should understand that finance is a complex system and complex 
systems have very clear characteristics, you know, and one of 
them is low levels of predictability, particularly of tail 
events. We have to worry--regulators should not encourage model 
error. My idea is to build a society that is resistant to 
expert mistakes. Regulators increased the dependence of society 
on expert mistakes and other things also in the Value-at-Risk, 
these AAA things. Okay. So we want to reduce that. We want to 
build a society that can sustain shocks because we are moving 
more and more into a world that delivers very large-scale 
variables, and we know exactly how they affect us or we know 
with some precision how they affect us, and we know how to 
build shocks. So the job of regulators should be to lower the 
impact of model error, and this is reminiscent of medicine. You 
know, the FDA, they don't let you bring any medicine without 
showing the side effects. Well, we should be doing the same 
thing in economic life. Thank you very much for this 
opportunity.
    [The prepared statement of Dr. Taleb follows:]

                 Prepared Statement of Nassim N. Taleb

               Report on the Risks of Financial Modeling,

                     VaR and the Economic Breakdown

INTRODUCTION

    Mr. Chairman, Ranking Member, Members of the Committee, thank you 
for giving me the opportunity to testify on the risk measurement 
methods used by banks, particularly those concerned with blowup risk, 
estimates of probabilities of losses from extreme events (``tail 
risks''), generally bundled under VaR.\1\
---------------------------------------------------------------------------
    \1\ The author thanks Daniel Kahneman, Pablo Triana, and Eric 
Weinstein for helpful discussions.
---------------------------------------------------------------------------
    What is the VaR? It is simply a model that is supposed to project 
the expected extreme loss in an institution's portfolio that can occur 
over a specific time frame at a specified level of confidence. Take an 
example. A standard daily VaR of $1 million at a one percent 
probability tells you that you have less than a one percent chance of 
losing $1 million or more on a given day.\2\ There are many 
modifications around VaR, ``conditional VaR,'' \3\ so my discussion 
concerns all quantitative (and probabilistic) methods concerned with 
losses associated with rare events. Simply, there are limitations to 
our ability to measure the risks of extreme events.
---------------------------------------------------------------------------
    \2\ Although such definition of VaR is often presented as a 
``maximum'' loss, it is technically not so in an open-ended exposure: 
since, conditional on losing more than $1 million, you may lose a lot 
more, say $5 million.
    \3\ Data shows that methods meant to improve the standard VaR, like 
``expected shortfall'' or ``conditional VaR'' are equally defective 
with economic variables--past losses do not predict future losses. 
Stress testing is also suspicious because of the subjective nature of 
``reasonable stress'' number--we tend to underestimate the magnitude of 
outliers. ``Jumps'' are not predictable from past jumps. See Taleb, 
N.N. (in press) ``Errors, robustness, and the fourth quadrant,'' 
International Journal of Forecasting (2009).
---------------------------------------------------------------------------
    Thirteen years ago, I warned that ``VaR encourages misdirected 
people to take risks with shareholders', and ultimately taxpayers' 
money.'' I have since been begging for the suspension of these 
measurements of tail risks. But this came a bit late. For the banking 
system has lost so far, according to the International Monetary Fund, 
in excess of four trillion dollars directly as a result of faulty risk 
management. Most of the losses were in the U.S. and will be directly 
borne by taxpayers. These losses do not include the other costs of the 
economic crisis.
    Data shows that banks routinely lose everything earned in their 
past history in single blowups--this happened in 1982, 1991, and, of 
course now. Every time society bails them out--while bank risk-takers 
retain their past bonuses and start the game afresh. This is an 
aberrant case of capitalism for the profits and socialism for the 
losses.

MAIN PROBLEMS ASSOCIATED WITH VAR-STYLE RISK MEASUREMENT

1. These problems have been obvious all along

    My first encounter with the VaR was as a derivatives trader in the 
early 1990s when it was first introduced. I saw its underestimation of 
the risks of a portfolio by a factor of 100--you set up your book to 
lose no more than $100,000 and you take a $10,000,000 hit. Worse, there 
was no way to get a handle on how much its underestimation could be.
    Using VaR after the crash of 1987 proved strangely gullible. But 
the fact that its use was not suspended after the many subsequent major 
events, such as the Long-Term Capital Management blowup in 1998, 
requires some explanation. Furthermore, regulators started promoting 
VaR (Basel 2) just as evidence was mounting against it.\4\
---------------------------------------------------------------------------
    \4\ My recollection is that the VaR was not initially taken 
seriously by traders and managers. It took a long time for the practice 
to spread--and it was only after regulators got involved that it became 
widespread.

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2. VaR is ineffective and lacks in robustness

    Alas, we cannot ``measure'' the risk of future rare events like we 
measure the temperature. By robustness, I mean that the measure does 
not change much if you change the model, technique, or theory. Indeed 
risk estimation has nothing to do with the notion of measure. And the 
rarer the event, the harder it is to compute its probability--yet the 
rarer the event, the larger the consequences.\5\
---------------------------------------------------------------------------
    \5\ See Taleb, N.N. and Pilpel, A. (2007) Epistemology and Risk 
Management, Risk and Regulation, 13.
---------------------------------------------------------------------------
    Furthermore, the type of randomness we have with economic variables 
does not have a well-tractable, well-known structure, and can deliver 
vastly large events--and we are unable to get a handle on ``how 
large.'' Conventional statistics, derived on a different class of 
variables, fail us here.\6\,\7\G5,\8\
---------------------------------------------------------------------------
    \6\ We are in the worst type of complex system characterized by 
high interdependence, low predictability, and vulnerability to extreme 
events. See N.N. Taleb, The Black Swan, Random House, 2007.
    \7\ There are other problems. 1) VaR does not replicate out of 
sample--the past almost never predicts subsequent blowups. (see data in 
the Fourth Quadrant). 2) A decrease in VaR does not mean decrease in 
risks; often quite the opposite holds, which allows the measure to be 
gamed.
    \8\ The roots of VaR come from modern financial theory (Markowitz, 
Sharpe, Miller, Merton, Scholes) which, in spite of its patent lack of 
scientific validity, continues to be taught in business schools. See 
Taleb, N.N., (2000), The Black Swan: The Impact of the Highly 
Improbable, Random House.

3. VaR encourages ``low volatility, high blowup'' risk taking which can 
---------------------------------------------------------------------------
be gamed by the Wall Street bonus structure

    Figure 1-A typical ``blow-up'' strategy with hidden risks: 
appearance of low volatility, with a high risk of blowup. The trader 
makes 11 bonuses, with no subsequent ``clawback'' as losses are borne 
by shareholders, then taxpayers. This is the profile for banks (losses 
in 1982,1991, and 2008) and many hedge funds. VaR encourages such types 
of risk taking.




    I have shown that operators like to engage in a ``blow-up'' 
strategy, (switching risks from visible to hidden), which consists in 
producing steady profits for a long time, collecting bonuses, then 
losing everything in a single blowup.\9\ Such trades pay extremely well 
for the trader--but not for society. For instance, a member of 
Citicorp's executive committee (and former government official) 
collected $120 million of bonuses over the years of hidden risks before 
the blowup; regular taxpayers are financing him retrospectively.
---------------------------------------------------------------------------
    \9\ Taleb, N.N. (2004) ``Bleed or Blowup: What Does Empirical 
Psychology Tell Us About the Preference For Negative Skewness?,'' 
Journal of Behavioral Finance, 5.
---------------------------------------------------------------------------
    Blowup risks kept increasing over the past few years, while the 
appearance of stability has increased.\10\
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    \10\ Even Chairman Bernanke was fooled by the apparent stability as 
he pronounced it the ``great moderation.''

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4. Var has severe side effects (anchoring)

    Many people favor the adjunct application of VaR on grounds that it 
is ``not harmful,'' using arguments like ``we are aware of its 
defects.'' VaR has side effects of increasing risk-taking, even by 
those who know that it is not reliable. We have ample evidence of so 
called ``anchoring'' \11\ in the calibration of decisions. Information, 
even when it is known to be sterile, increases overconfidence.
---------------------------------------------------------------------------
    \11\ Numerous experiments provide evidence that professionals are 
significantly influenced by numbers that they know to be irrelevant to 
their decision, like writing down the last four digits of one's social 
security number before making a numerical estimate of potential market 
moves. German judges rolling dice before sentencing showed an increase 
of 50 percent in the length of the sentence when the dice show a high 
number, without being conscious of it. See Birte Englich and Thomas 
Mussweiler, ``Sentencing under Uncertainty: Anchoring Effects in the 
Courtroom,'' Journal of Applied Social Psychology, Vol. 31, No. 7 
(2001), pp. 1535-1551; Birte Englich, Thomas Mussweiler, and Fritz 
Strack, ``Playing Dice with Criminal Sentences: the Influence of 
Irrelevant Anchors on Experts' Judicial Decision Making,'' Personality 
and Social Psychology Bulletin, Vol. 32, No. 2 (Feb. 2006), pp. 188-
200.

5. VaR-style quantitative risk measurement is the engine behind 
---------------------------------------------------------------------------
leverage, the main cause of the current crisis

    Leverage\12\ is a direct result of underestimation of the risks of 
extreme events--and the illusion that these risks are measurable. 
Someone more careful (or realistic) would issue equity.
---------------------------------------------------------------------------
    \12\ There is a large difference between equity and credit bubbles. 
Equity bubbles are benign. We went through an equity bubble in 2000, 
without major problems.

  Some credit can be benign. Credit that facilitates trade and economic 
transactions and finances conservative house-ownership does not have 
the same risk properties as credit for speculative reasons resulting 
from overconfidence.
    April 28, 2004 was a very sad day, when the SEC, at the instigation 
of the investment banks, initiated the abandonment of hard (i.e., 
robust) risk measures like leverage, in favor of more model-based 
probabilistic, and fragile, ones.

CONCLUSION: WHAT REGULATORY STRUCTURE DO WE NEED?

    Regulators should understand that financial markets are a complex 
system and work on increasing the robustness in it, by preventing ``too 
big to fail'' situations, favoring diversity in risk taking, allowing 
entities to absorb large shocks, and reducing the effect of model error 
(see ``Ten Points for a Black Swan Robust Society,'' in Appendix II). 
This implies reliance on ``hard,'' non-probabilistic measures rather 
than more error-prone ones. For instance ``leverage'' is a robust 
measures (like the temperature, it does not change with your model), 
while VaR is not.
    Furthermore, we need to examine the toxicity of models; financial 
regulators should have the same test as the Food and Drug 
Administration does. The promoter of the probability model must be able 
to show that no one will be harmed even if the event is rare. Alas, the 
history of medicine shows translational gaps, the lag between the 
discovery of harm and suspension of harmful practice, lasting up to 200 
years in pre-modern medicine.\13\ Unfortunately, economics resemble 
pre-modern medicine.\14\ But we cannot afford to wait 200 years to find 
out that the medicine is far worse than the disease. We cannot afford 
to wait even months.
---------------------------------------------------------------------------
    \13\ ``When William Harvey demonstrated the mechanism of blood 
circulation in the 1620s, humoral theory and its related practices 
should have disappeared, because the anatomy and physiology on which it 
relied was incompatible with this picture of the organism. In fact, 
people continued to refer to spirits and humors, and doctors continued 
to prescribe phlebotomies, enemas, and cataplasms, for centuries more--
even when it was established in the mid-1800, most notably by Louis 
Pasteur, that germs were the cause of disease.'' Noga Arikha ``Just 
Life in a Nutshell: Humours as common sense,'' in The Philosophical 
Forum Quarterly, XXXIX, 3.
    \14\ Most of the use of probabilistic methods lacking both 
mathematical and empirical justification can be attributed to the 
prestige given to modern finance by the various Nobel memorial prizes 
in economics. See P. Triana, 2009, Lecturing Birds on Flying: Can 
Mathematical Theories Destroy the Markets?, J. Wiley.

APPENDIX I:

                      AUTHOR'S WARNINGS, 1996-2007

                               1996-1997
    VaR is charlatanism because it tries to estimate something that is 
scientifically impossible to estimate, namely the risk of rare events. 
It gives people a misleading sense of precision. (Derivatives Strategy, 
citing from Dynamic Hedging)
    VaR encourages misdirected people to take risks with shareholders', 
and ultimately taxpayers' money. (Derivatives Strategy)
                                  2003
    Fannie Mae's models (for calibrating to the risks of rare events) 
are pseudoscience. (New York Times--Alex Berenson's article on FNMA)
    ``What happened to LTCM will look like a picnic compared to what 
should happen to you.'' (Lecture, Women in Hedge Funds Association, 
cited in Hedge World)
                                  2007
    Fannie Mae, when I look at its risks, seems to be sitting on a 
barrel of dynamite, vulnerable to the slightest hiccup. But not to 
worry: their large staff of scientists deems these events ``unlikely.'' 
(The Black Swan)
    Banks are now more vulnerable to the Black Swan than ever before 
with ``scientists'' among their staff taking care of exposures. The 
giant firm, J.P. Morgan, put the entire world at risk by introducing in 
the nineties RiskMetrics, a phony method aiming at managing people's 
risks. A related method called ``Value-at-Risk,'' which relies on the 
quantitative measurement of risk, has been spreading. (The Black Swan)

APPENDIX II:

                    TEN PRINCIPLES FOR A BLACK SWAN

                              ROBUST WORLD

                    (FINANCIAL TIMES, APRIL 8, 2009)
 1. What is fragile should break early while it is still small. Nothing 
should ever become too big to fail. Evolution in economic life helps 
those with the maximum amount of hidden risks--and hence the most 
fragile--become the biggest.

 2. No socialization of losses and privatization of gains. Whatever may 
need to be bailed out should be nationalized; whatever does not need a 
bail-out should be free, small and risk-bearing. We have managed to 
combine the worst of capitalism and socialism. In France in the 1980s, 
the socialists took over the banks. In the U.S. in the 2000s, the banks 
took over the government. This is surreal.

 3. People who were driving a school bus blindfolded (and crashed it) 
should never be given a new bus. The economics establishment 
(universities, regulators, central bankers, government officials, 
various organizations staffed with economists) lost its legitimacy with 
the failure of the system. It is irresponsible and foolish to put our 
trust in the ability of such experts to get us out of this mess. 
Instead, find the smart people whose hands are clean.

 4. Do not let someone making an ``incentive'' bonus manage a nuclear 
plant--or your financial risks. Odds are he would cut every corner on 
safety to show ``profits'' while claiming to be ``conservative.'' 
Bonuses do not accommodate the hidden risks of blow-ups. It is the 
asymmetry of the bonus system that got us here. No incentives without 
disincentives: capitalism is about rewards and punishments, not just 
rewards.

 5. Counter-balance complexity with simplicity. Complexity from 
globalization and highly networked economic life needs to be countered 
by simplicity in financial products. The complex economy is already a 
form of leverage: the leverage of efficiency. Such systems survive 
thanks to slack and redundancy; adding debt produces wild and dangerous 
gyrations and leaves no room for error. Capitalism cannot avoid fads 
and bubbles: equity bubbles (as in 2000) have proved to be mild; debt 
bubbles are vicious.

 6. Do not give children sticks of dynamite, even if they come with a 
warning. Complex derivatives need to be banned because nobody 
understands them and few are rational enough to know it. Citizens must 
be protected from themselves, from bankers selling them ``hedging'' 
products, and from gullible regulators who listen to economic 
theorists.

 7. Only Ponzi schemes should depend on confidence. Governments should 
never need to ``restore confidence.'' Cascading rumors are a product of 
complex systems. Governments cannot stop the rumors. Simply, we need to 
be in a position to shrug off rumors, be robust in the face of them.

 8. Do not give an addict more drugs if he has withdrawal pains. Using 
leverage to cure the problems of too much leverage is not homeopathy, 
it is denial. The debt crisis is not a temporary problem, it is a 
structural one. We need rehab.

 9. Citizens should not depend on financial assets or fallible 
``expert'' advice for their retirement. Economic life should be 
definancialized. We should learn not to use markets as storehouses of 
value: they do not harbor the certainties that normal citizens require. 
Citizens should experience anxiety about their own businesses (which 
they control), not their investments (which they do not control).

10. Make an omelet with the broken eggs. Finally, this crisis cannot be 
fixed with makeshift repairs, no more than a boat with a rotten hull 
can be fixed with ad hoc patches. We need to rebuild the hull with new 
(stronger) materials; we will have to remake the system before it does 
so itself. Let us move voluntarily into Capitalism 2.0 by helping what 
needs to be broken break on its own, converting debt into equity, 
marginalizing the economics and business school establishments, 
shutting down the ``Nobel'' in economics, banning leveraged buy-outs, 
putting bankers where they belong, clawing back the bonuses of those 
who got us here, and teaching people to navigate a world with fewer 
certainties.
    Then we will see an economic life closer to our biological 
environment: smaller companies, richer ecology, no leverage. A world in 
which entrepreneurs, not bankers, take the risks, and companies are 
born and die every day without making the news.
    In other words, a place more resistant to black swans.

    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
    
                     Biography for Nassim N. Taleb
    Nassim N. Taleb is currently Distinguished Professor in Risk 
Engineering at New York University Polytechnic Institute and Principal 
at Universa Investments. He spent close to 21 years as a senior trader 
on Wall Street before becoming a full time scholar. He is a combination 
of a scholar of risk and model error, literary essayist, and 
derivatives trader. He is known for a multi-disciplinary approach to 
the role of the high-impact rare event--across economics, philosophy, 
finance, engineering, and history. He also runs experiments on human 
errors in the assessment of probabilities of rare events as part of the 
Decision Science Laboratory. His current program is to design ways to 
live in a world we don't quite understand and help ``robustify'' the 
world against the Black Swan.
    Taleb is, among other books and research papers, the author of the 
NYT Bestseller The Black Swan: The Impact of the Highly Improbable 
which was according to The Times as one of the 12 most influential 
books since WW-II. His books have close to two and a half million 
copies in print in 31 languages.
    Taleb has an MBA from Wharton and a Ph.D. from the University of 
Paris.
    Among other activities, he is currently on the King of Sweden 
advisory committee for climate risks and modeling. The British Tory 
opposition is using Black Swan thinking as part of their platform.

    Chairman Miller. Thank you, Dr. Taleb.
    Dr. Bookstaber for five minutes.

     STATEMENT OF DR. RICHARD BOOKSTABER, FINANCIAL AUTHOR

    Dr. Bookstaber. Mr. Chairman and Members of the Committee, 
I thank you for the opportunity to testify today. My oral 
testimony will begin with a discussion of the limitations of 
VaR. I will then discuss the role of VaR in the recent market 
meltdown and conclude with suggestions for filling the gap left 
by the limitations of VaR.
    The limitations of VaR are readily apparent by looking at 
the critical assumptions behind it. For the standard 
construction of VaR, these assumptions are, first, that all 
portfolio positions are included; secondly, that the sample 
history used in VaR is a reasonable representation of things 
that are likely to occur going forward; and third, that the 
normal distribution function that it uses is a reasonable 
representation of the statistical distribution underlying the 
returns. These assumptions are often violated, leading VaR 
estimates to be misleading. So let me discuss each of these in 
turn.
    First of all, in terms of incomplete positions, obviously, 
for risk to be measured, all the risky positions must be 
included in the analysis, but for larger institutions, it is 
commonplace for some positions to be excluded. This can happen 
because the positions are held off a balance sheet beyond the 
purview of those doing the risk analysis, because they are in 
complex instruments that have not been sufficiently modeled, or 
because they are in new so-called innovative products that have 
yet to be added into the risk process. This provides a 
compelling reason to have what I call `flight to simplicity' in 
financial products, to move away from complex and customized 
innovative products and towards standardization.
    In terms of unrepresentative sample periods, VaR gives a 
measure of risk that assumes tomorrow is drawn from the same 
distribution as the sample data used to compute the VaR. If the 
future does not look like the past--in particular, if a crisis 
emerges, VaR will no longer be a good measure of risk, which is 
to say that VaR is a good measure of risk except when it really 
matters.
    Third, in terms of fat tails and normal distribution, 
largely because of crisis events, security returns tend to have 
fatter tails than what is represented by a normal distribution. 
That is, there tend to be more outliers and extreme events than 
a normal distribution would imply. Now, one way to address this 
well-known inaccuracy is to modify the distribution allowing 
for fatter tails, but this adds complication to VaR analysis 
while contributing little insight in terms of risk.
    A better approach is to accept the limitations of VaR, and 
then try to understand the market crises where VaR fails. If we 
understand the dynamics of market crises, we may be able to 
improve risk management to make it work when it is of the 
greatest importance. A starting point for understanding 
financial market crises is leverage and the crowding of trades. 
These lead to the common crisis dynamic--what I call a 
liquidity crisis cycle. Such a cycle begins when there is some 
exogenous shock that causes a drop in a market that is crowded 
with leveraged investors. The highly leveraged investors are 
forced to sell to meet their margin requirements. Their selling 
drops prices further, which in turn forces yet more selling, 
resulting in a cascading cycle downward in prices. Now, the 
investors that are under pressure discover there is no longer 
any liquidity in the stressed market, so they start to 
liquidate their positions in other markets to generate the 
required margin. And if many investors that are in the first 
market also have high exposure in a second one, the downward 
spiral propagates to this second market.
    This phenomenon explains why a crisis can spread in 
surprising and unpredictable ways. The contagion is primarily 
driven by what other securities are owned by the funds that 
need to sell. For example, a simple example of this is what 
happened with the silver bubble back in 1980. The silver market 
became closely linked with the market for cattle. Why? Because 
the Hunt family had margin calls on their silver position, and 
so they sold whatever else they could, and what else they had 
to sell happened to be cattle. So thus there was a contagion 
based not on any economic linkage but based on who was under 
pressure and what else they owned.
    Now, this cycle evolves unrelated to historical 
relationships, out of the reach of VaR-type models. But that 
doesn't mean it is beyond analysis. But if we want to analyze 
it, we need to know the leverage and the positions of the major 
market participants. Gathering these critical data is the first 
step in measuring and managing crisis risk, and should be the 
role of a market regulator.
    Now, let me talk specifically about the role of VaR in the 
current crisis. Whatever the limitations of VaR models, they 
were not the key culprits in the case of the multi-billion 
dollar write-downs central to the current crisis. The large 
bank inventories were there to be seen. You didn't need to have 
any models or sophisticated detective or forensic work to see 
them. Furthermore, it was clear that these inventories were 
illiquid and that their market values were uncertain. It is 
hard to understand how this elephant in the room was missed, 
how a risk manager could see inventory grow from a few billion 
dollars to 10 billion dollars and then to 30 or 40 billion 
dollars, and not take action to bring that inventory down.
    One has to look beyond VaR to sheer stupidity or collective 
management failure. The risk managers missed the growing 
inventory, or did not have the courage of their conviction to 
insist on its being reduced, or the senior management was not 
willing to heed their demands. Whatever the reason, VaR was not 
central to the crisis. Focus would be better placed on failures 
in risk governance than failures of risk models, whatever the 
flaws of VaR are.
    Now, in summary, let me first emphasize, I believe that VaR 
does have value. If one were forced to pick a single number for 
the risk of a portfolio in the near future, VaR would be a good 
choice for the job. VaR illuminates most of the risk landscape, 
but, unfortunately, the places its light fails to reach are the 
canyons, crevices and cliffs.
    So we can do two things to try to improve on and address 
the limitations of VaR. One is to employ coarser measures of 
risk, measures that have fewer assumptions and that are less 
dependent on the future looking like the past. The use of the 
leverage ratio mandated by U.S. regulators is an example of 
such a measure. The leverage ratio does not overlay assumptions 
about the correlation or the volatility of the assets, and does 
not assume any mitigating effects from diversification. It 
does, however, have its own limitations as a basis for capital 
adequacy. The second is to add other risk methods that are 
better at illuminating the areas VaR does not reach. So in 
addition to measuring risk using a standard VaR approach, 
develop scenarios for crises and test capital adequacy under 
those scenarios. Critical, of course, to the success of this 
approach is the ability to ferret out potential crises and 
describe them adequately for risk purposes. We can go a long 
way toward this goal by having regulators amass and aggregate 
data on the positions and leverage of large financial 
institutions. These data are critical because we cannot manage 
what we cannot measure, and we cannot measure what we cannot 
see. With these data, we will be better able to measure the 
crowding and leverage that leads to crisis, and shed light on 
risks that fail to be illuminated by VaR.
    Let me close my oral comments by responding to comments by 
both the Chairman and the Ranking Member. The analogy of VaR 
and the models related to risk to models used in other 
engineering and physical systems--I think there is a critical 
distinction between financial systems and other engineering 
systems, because financial systems are open to gaming. If I 
discover a valve that is poorly designed in a nuclear power 
plant and design a new valve to replace it, and install that 
valve, the valve doesn't sit there and try to figure out if it 
can fool me into thinking it is on when it is really off. But 
in the financial markets, that is what happens. So any 
engineering solution or any analogy to physical processes is 
going to be flawed when they are applied to the financial 
markets, because those in the financial markets can game 
against the system to try to find ways around any regulation, 
and to find other ways to do what they want to do. And I 
believe that one of the key tools for this type of gaming are 
sophisticated, innovative, complex products that can often 
obfuscate what people are doing.
    So, I think, parenthetical to the issues of VaR and other 
models is, number one, the recognition that no model can work 
completely in the financial markets the way they can in other 
physical systems, and number two, that if we want to curb or 
diminish the issues of gaming, we have to have more simplicity 
and transparency in the financial instruments.
    Thank you. I look forward to your questions.
    [The prepared statement of Dr. Bookstaber follows:]

                Prepared Statement of Richard Bookstaber

    Mr. Chairman and Members of the Committee, I thank you for the 
opportunity to testify today. My name is Richard Bookstaber. Over the 
past decade I have worked as the risk manager in two of the world's 
largest hedge funds, Moore Capital Management and, most recently, 
Bridgewater Associates. In the 1990s I oversaw firm-wide risk at 
Salomon Brothers, which at the time was the largest risk-taking firm in 
the world, and before that was in charge of market risk at Morgan 
Stanley.
    I am the author of A Demon of Our Own Design--Markets, Hedge Funds, 
and the Perils of Financial Innovation. Published in April, 2007, this 
book warned of the potential for financial crisis resulting from the 
growth of leverage and the proliferation of derivatives and other 
innovative products.
    Although I have extensive experience on both the buy-side and sell-
side, I left my position at Bridgewater Associates at the end of 2008, 
and come before the Committee in an unaffiliated capacity, representing 
no industry interests.
    My testimony will discuss what VaR is, how it can be used and more 
importantly, how it can be misused. I will focus on the limitations of 
VaR in measuring crisis risk. I will then discuss the role of VaR in 
the recent market meltdown, concluding with suggestions for ways to 
fill the gaps left by the limitations of VaR.

What is VaR?

    VaR, or Value-at-Risk, measures the risk of a portfolio of assets 
by estimating the probability that a given loss might occur. For 
example, the dollar VaR for a particular portfolio might be expressed 
as ``there is a ten percent probability that this portfolio will lose 
more than $VaR over the next day.''
    Here is a simplified version of the steps in constructing a VaR 
estimate for the potential loss at the ten percent level:

        1.  Identify all of the positions held by the portfolio.

        2.  Get the daily returns for each of these positions for the 
        past 250 trading days (about a one-year period).

        3.  Use those returns to construct the return to the overall 
        portfolio for each day over the last 250 trading days.

        4.  Order the returns for those days from the highest to the 
        lowest, and pick the return for the day that is the 25th worst 
        day's return. That will be a raw estimate of the daily VaR at 
        the ten percent level.

        5.  Smooth the results by fitting this set of returns to the 
        Normal distribution function.\1\
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    \1\ The risk for a Normal distribution is fully defined by the 
standard deviation, and the results from Step 3 can be used to estimate 
the standard deviation of the sample. If the estimated standard 
deviation is, say, five percent, then the VaR at the ten percent level 
will be a loss of eight percent. For a Normal distribution the ten 
percent level is approximately 1.6 standard deviations.

Limitations of VaR

    The critical assumptions behind the construction of VaR are made 
clear by the process described above:

        1.  All of the portfolio positions are included.

        2.  The sample history is a reasonable representation of what 
        things will look like going forward.

        3.  The Normal distribution function is a reasonable 
        representation of the statistical distribution underlying the 
        returns.

    The limitations to VaR boil down to issues with these three 
assumptions, assumptions that are often violated, leading VaR estimates 
to be misleading.

Incomplete positions
    Obviously, risk cannot be fully represented if not all of the risky 
positions are included in the analysis. But for larger institutions, it 
is commonplace for this to occur. Positions might be excluded because 
they are held off-balance sheet, beyond the purview of those doing the 
risk analysis; they might be in complex instruments that have not been 
sufficiently modeled or that are difficult to include in the position 
database; or they might be in new products that have not yet been 
included in the risk process. In the recent crisis, some banks failed 
to include positions in collateralized debt obligations (CDOs) for all 
three of these reasons.\2\ And that exclusion was not considered an 
immediate concern because they were believed to be low risk, having 
attained a AAA rating.
---------------------------------------------------------------------------
    \2\ Regulatory capital on the trading assets that a bank does not 
include in VaR--or for which the bank's VaR model does not pass 
regulatory scrutiny--is computed using a risk-rating based approach. 
However, the rating process itself suffers from many of the 
difficulties associated with calculating VaR, as illustrated by the AAA 
ratings assigned to many mortgage-backed CDOs and the consequent severe 
underestimation of the capital required to support those assets.
---------------------------------------------------------------------------
    The inability to include all of the positions in the VaR risk 
analysis, the most rudimentary step for VaR to be useful, is pervasive 
among the larger institutions in the industry. This provides a 
compelling reason to have a `flight to simplicity' in financial 
products, to move away from complex and customized innovative products 
and toward standardization.\3\
---------------------------------------------------------------------------
    \3\ I discuss the complexity and related risk issues surrounding 
derivatives and related innovative products in Testimony of Richard 
Bookstaber, Submitted to the Senate of the United States, Committee on 
Agriculture, Nutrition, and Forestry for the Hearing: ``Regulatory 
Reform and the Derivatives Markets,'' June 4, 2009.

Unrepresentative sample period
    VaR gives a measure of risk that assumes tomorrow is drawn from the 
same distribution as the sample data used to compute the VaR. If the 
future does not look like the past, in particular if a crisis emerges, 
then VaR will no longer be a good measure of risk.\4\ Which is to say 
that VaR is a good measure of risk except when it really matters.\5\
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    \4\ One way to try to overcome the problem of relying on the past 
is to use a very long time period in the VaR calculation, with the idea 
that a longer period will include many different regimes, crises and 
relationships. Such a view misses the way different regimes, 
essentially different distributions, mix to lead to a final result. A 
long time period gives muddied results. To see this, imagine the case 
where in half of the past two assets were strongly positively 
correlated and the other half they were strongly negatively correlated. 
The mixing of the two would suggest the average of little correlation, 
thus giving a risk posture that did not exist in either period, but 
that also incorrectly suggests diversification opportunities.
    \5\ As a corollary to this, one could also say that diversification 
works except when it really matters.
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    It is well known that VaR cannot measure crisis risk. During 
periods of crisis the relationship between securities changes in 
strange and seemingly unpredictable ways. VaR, which depends critically 
on a set structure for volatility and correlation, cannot provide 
useful information in this situation. It contains no mechanism for 
predicting the type of crisis that might occur, and does not consider 
the dynamics of market crises. This is not to say that VaR has no value 
or is hopelessly flawed. Most of the time it will provide a reasonable 
measure of risk--indeed the vast majority of the time this will be the 
case. If one were forced to pick a single number for the risk of a 
portfolio in the near future, VaR would be a good choice for the job. 
VaR illuminates most of the risk landscape. But unfortunately, the 
places its light fails to reach are the canyons, crevices and cliffs.

Fat Tails and the Normal Distribution
    Largely because of crisis events, security returns tend to have 
fatter tails than what is represented by a Normal distribution. That 
is, there tend to be more outliers and extreme events than what a 
Normal distribution would predict. This leads to justifiable criticism 
of VaR for its use of the Normal distribution. However, sometimes this 
criticism is overzealous, suggesting that the professionals who assume 
a Normal distribution in their analysis are poorly trained or worse. 
Such criticism is unwarranted; the limitations of the Normal 
distribution are well-known. I do not know of anyone working in 
financial risk management, or indeed in quantitative finance generally, 
who does not recognize that security returns may have fat tails. It is 
even discussed in many investment textbooks, so it is a point that is 
hard to miss.\6\
---------------------------------------------------------------------------
    \6\ For example, Investments, by Bodie, Kane, and Marcus, 8th 
edition (McGraw-Hill/Irwin), has a section (page 148) entitled 
``Measurement of Risk with Non-normal Distributions.''
---------------------------------------------------------------------------
    The issue is how this well-known inaccuracy of the Normal 
distribution is addressed. One way is knowingly to misuse VaR, to 
ignore the problem and act as if VaR can do what it cannot. Another is 
to modify the distribution to allow for fatter tails.\7\ This adds 
complication and obfuscation to the VaR analysis, because any approach 
employing a fat-tailed distribution increases the number of parameters 
to estimate, and this increases the chance that the distribution will 
be mis-specified. And in any case, simply fattening up the tails of the 
distribution provides little insight for risk management.
---------------------------------------------------------------------------
    \7\ Extreme value theory is the bastion for techniques that employ 
distributions with a higher probability of extreme events.
---------------------------------------------------------------------------
    I remember a cartoon that showed a man sitting behind a desk with a 
name plate that read `Risk Manager.' The man sitting in front of the 
desk said, ``Be careful? That's all you can tell me, is to be 
careful?'' Stopping with the observation that extreme events can occur 
in the markets and redrawing the distribution accordingly is about as 
useful as saying ``be careful.'' A better approach is to accept the 
limitations of VaR, and then try to understand the nature of the 
extreme events, the market crises where VaR fails. If we understand the 
dynamics of market crisis, we may be able to improve risk management to 
make it work when it is of the greatest importance.

Understanding the Dynamics of Market Crises

    A starting point for understanding financial market crises is 
leverage and the crowding of trades, both of which have effects that 
lead to a common crisis dynamic, the liquidity crisis cycle.
    Such a cycle begins when an exogenous shock causes a drop in a 
market that is crowded with leveraged investors. The highly leveraged 
investors are forced to sell to meet their margin requirements. Their 
selling drops prices further, which in turn forces yet more selling, 
resulting in a cascading cycle downward in prices. Those investors that 
are under pressure discover there is no longer liquidity in the 
stressed market, so they start to liquidate their positions in other 
markets to generate the required margin. If many of the investors that 
are in the first market also have high exposure in a second one, the 
downward spiral propagates to this second market.\8\
---------------------------------------------------------------------------
    \8\ The use of VaR-based capital can actually contribute to this 
sort of cycle. VaR will increase because of the higher volatility--and 
also possibly because of the higher correlations--leading potential 
liquidity providers and lenders to pull back. This was a likely 
exacerbating effect during the 1997 Asian crisis.
---------------------------------------------------------------------------
    This phenomenon explains why a crisis can spread in surprising and 
unpredictable ways. The contagion is driven primarily by what other 
securities are owned by the funds that need to sell.\9\ For example, 
when the silver bubble burst in 1980, the silver market became closely 
linked to the market for cattle. Why? Because when the Hunt family had 
to meet margin calls on their silver positions, they sold whatever else 
they could. And they happened also to be invested in cattle. Thus there 
is contagion based not on economic linkages, but based on who is under 
pressure and what else they are holding.
---------------------------------------------------------------------------
    \9\ As an illustration, the proximate cause of Long Term Capital 
Management's (LTCM's) demise was the Russian default in August, 1998. 
But LTCM was not highly exposed to Russia. A reasonable risk manager, 
aware of the Russian risks, might not have viewed it as critical to 
LTCM. But the Russian default hurt LTCM because many of those who did 
have high leverage in Russia also had positions in other markets where 
LTCM was leveraged. When the Russian debt markets failed and these 
investors had to come up with capital, they sold their more liquid 
positions in, among other things, Danish mortgage bonds. So the Danish 
mortgage bond market and these other markets went into a tail spin, and 
because LTCM was heavily exposed in these markets, the contagion took 
LTCM with it.
---------------------------------------------------------------------------
    This cycle evolves unrelated to historical relationships, out of 
the reach of VaR-type models. But that does not mean it is beyond 
analysis. Granted it is not easy to trace the risk of these potential 
liquidity crisis cycles. To do so with accuracy, we need to know the 
leverage and positions of the major market participants. No one firm, 
knowing only its own positions, can have an accurate assessment of the 
crisis risk. Indeed, each firm might be managing its risk prudently 
given the information it has at its disposal, and not only miss the 
risk that comes from crowding and leverage, but also unwittingly 
contribute to this risk. Gathering these critical data is the first 
step in measuring and managing crisis risk. This should be the role of 
a market regulator.\10\
---------------------------------------------------------------------------
    \10\ I discuss the need for firm-level position and leverage data 
in crisis risk management in previous testimony before both the House 
and the Senate. For example, Testimony of Richard Bookstaber, Submitted 
to the Congress of the United States, House Financial Services 
Committee, for the Hearing: ``Systemic Risk: Examining Regulators 
Ability to Respond to Threats to the Financial System,'' October 2, 
2007, and Testimony of Richard Bookstaber, Submitted to the Senate of 
the United States, Senate Banking, Housing and Urban Affairs 
Subcommittee on Securities, Insurance and Investment, for the Hearing: 
``Risk Management and Its Implications for Systematic Risk,'' June 19, 
2008.

The Role of VaR in the Current Crisis

    The above discussion provides part of the answer to the question of 
the role of VaR in the current market crisis: If VaR was used as the 
source of risk measurement, and thus as the determinant of risk 
capital, then it missed the potential for the current crisis for the 
simple reason that VaR is not constructed to deal with crisis risk. And 
if VaR was applied as if it actually reflected the potential for 
crisis, that is, if it was forgotten that VaR is only useful insofar as 
the future is drawn from the same distribution as the past, then this 
led to the mis-measurement of risk. So if VaR was the sole means of 
determining risk levels and risk capital coming into this crisis, it 
was misused. But this does not present the full story.
    Whatever the limitations of VaR models, they were not the key 
culprits in the case of the multi-billion dollar write-downs during the 
crisis. The large bank inventories were there to be seen; no models or 
detective work were needed. Furthermore, it was clear the inventories 
were illiquid and their market values uncertain.\11\ It is hard to 
understand how this elephant in the room was missed, how a risk manager 
could see inventory grow from a few billion dollars to ten billion 
dollars and then to thirty or forty billion dollars and not react by 
forcing that inventory to be brought down.
---------------------------------------------------------------------------
    \11\ This is especially true when one considers the business of the 
banks, which is to package the securities and sell them. The growth of 
inventory was outside the normal business of the banks. That the 
securities were not moving out the door should have been an immediate 
indication they were not correctly priced.
---------------------------------------------------------------------------
    Of course, if these inventories were not properly included in the 
VaR analysis, the risk embodied by these positions would have been 
missed, but one has to look beyond VaR, to culprits such as sheer 
stupidity or collective management failure: The risk managers missed 
the growing inventory, or did not have the courage of their conviction 
to insist on its reduction, or the senior management was not willing to 
heed their demands. Whichever the reason, VaR was not central to this 
crisis.\12\ Focus would be better placed on failures in risk governance 
than failures of risk models.
---------------------------------------------------------------------------
    \12\ Indeed, in some important cases, VaR was not even employed in 
the risk process. A case in point is the `super senior' mortgage CDO 
positions which caused huge trading losses at a number of banks. There 
is a common misconception that regulatory capital for trading assets is 
automatically computed using VaR. In fact, trading assets are eligible 
for VaR-based capital only if the bank can demonstrate to its 
supervisor that its model is robust. Absent this, a coarser method is 
applied. Many of the highly complex securities at the heart of the 
recent crisis were not regarded as being suitable for VaR treatment, 
and received a simpler ratings-based treatment, which proved to 
severely underestimate the capital required to support the assets.

Summary: VaR and Crisis Risk

    There are two approaches for moving away from over-reliance on VaR.
    The first approach is to employ coarser measures of risk, measures 
that have fewer assumptions and that are less dependent on the future 
looking like the past.\13\ The use of the Leverage Ratio mandated by 
U.S. regulators and championed by the FDIC is an example of such a 
measure.\14\ The leverage ratio does not overlay assumptions about the 
correlation or the volatility of the assets, and does not assume any 
mitigating effect from diversification, although it has its own 
limitations as a basis for capital adequacy.\15\
---------------------------------------------------------------------------
    \13\ I believe coarse measures--measures that are not fine tuned to 
be ideal in any one environment, but are robust across many 
environments--are a key to good risk management.
    \14\ The Leverage Ratio is the ratio of Tier 1 capital, principally 
equity and retained earnings, to total assets.
    \15\ The Leverage Ratio is inconsistent with Basel II because it is 
not sensitive to the riskiness of balance sheet assets and it does not 
capture off-balance sheet risks. By not taking the relative risk of 
assets into account, it could lead to incentives for banks to hold 
riskier assets, while on a relative basis penalizing those banks that 
elect to hold a low-risk balance sheet. In terms of risk to a financial 
institution, the time horizon of leverage is also important, which the 
Leverage Ratio also misses. The problems with Bear Stearns and Lehman 
was not only one of leverage per se, but of funding a sizable portion 
of leverage in the short-term repo market. They thus were vulnerable to 
funding drying up in the face of a crisis.
---------------------------------------------------------------------------
    The second approach is to recognize that while VaR provides a guide 
to risk in some situations, it must be enhanced with other measures 
that are better at illuminating the areas it does not reach. For 
example, Pillar II of Basel II has moved to include stress cases for 
crises and defaults into its risk capital process. So in addition to 
measuring risk using a standard VaR approach, firms must develop 
scenarios for crises and test their capital adequacy under those 
scenarios. Critical to the success of this approach is the ability to 
ferret out potential crises and describe them adequately for risk 
purposes.
    This means that for crisis-related stress testing to be feasible, 
we first must believe that it is indeed possible to model financial 
crisis scenarios, i.e., that crises are not `black swans.' This is not 
to say that surprises do not occur. Though recently popularized, the 
recognition that we are beset by unanticipatable risk, by events that 
seemingly come out of nowhere and catch us unawares, has a long history 
in economics and finance, dating back to Frank Knight in the 1920s.\16\ 
The best defense against such risks is to maintain a coarse, simple and 
robust financial structure. Rather than fine-tuning for the current 
environments, we need risk measures and financial instruments which, 
while perhaps not optimal for the world of today, will be able to 
operate reasonably if the world changes in unexpected ways. VaR as 
currently structured is not such a risk measure.
---------------------------------------------------------------------------
    \16\ Knight makes the distinction between risks we can identify and 
measure and those that are unanticipatable and therefore not measurable 
in Risk, Uncertainty, and Profit. (1921), Boston, MA: Houghton Mifflin 
Company.
---------------------------------------------------------------------------
    However, although surprises do occur, crisis scenarios are not 
wholly unanticipatable; they are not in the realm of Knightian 
uncertainty. We have had ample experience with financial crises. We 
know a thing or two about them.\17\ And we can further anticipate 
crisis risk by amassing data on the positions and leverage of the large 
investment firms. The regulator is best suited to take on this task, 
because these are data that no one firm can or should fully see.\18\ 
With these critical data we will be better able to measure the crowding 
and leverage that lead to liquidity crisis cycles and begin to shed 
light on the areas of financial risk that fail to be illuminated by 
VaR.\19\
---------------------------------------------------------------------------
    \17\ For example, even beyond the insights to be gained from a 
detailed knowledge of firm-by-firm leverage and market crowding, there 
are some characteristics of market crisis that can be placed into a 
general scenario. When a crisis occurs, equity prices drop, credit 
spreads rise, and the volatility of asset returns increases. The yield 
curve flattens and gold prices rise. Furthermore, the correlation 
between individual equities rises, as does the correlation between 
equities and corporate bonds. The riskier and less liquid assets fare 
more poorly, so, for example, emerging markets take a differentially 
bigger hit than their G-7 cousins. More broadly, anything that is risky 
or less liquid becomes more common and negative in its return; the 
subtleties of pricing between assets becomes overshadowed by the 
assets' riskiness. However, short-term interest rates and commodity 
prices are less predictable; in some cases, such as in the case of the 
inflation-laden crisis of 1973-1974, they rise, while in other cases, 
such as in the current crisis, they drop.

Each of these effects can occur with a ferocity far beyond what is seen 
in normal times, so if these crisis events are overlaid on the 
distribution coming out of the VaR model based on those normal times 
one will come away saying the crisis is a 100-year flood event, a 
twenty standard deviation event, a black swan. But it is none of these 
things. It is a financial crisis, and such crises occur frequently 
enough that to be understood without such shock and awe.
---------------------------------------------------------------------------
    \18\ Financial firms will be justifiably reticent to have their 
position and leverage information made public, so the collection and 
analysis of the data will have to reside securely in the regulator.
    \19\ With these data, the regulator is also in a position to run 
risk analysis independent of the firms. Under Basel II, the regulator 
still depends on the internal processes of the banks for the 
measurement of risk and the resulting capital requirements.
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Appendix

             Related Blog Posts on VaR and Risk Management

The Fat-Tailed Straw Man

    See http://rick.bookstaber.com/2009/03/fat-tailed-straw-man.html

    My Time article about the quant meltdown of August, 2007 started 
with ``Looks like Wall Street's mad scientists have blown up the lab 
again.'' Articles on Wall Street's mad scientist blowing up the lab 
seem to come out every month in one major publication or another. The 
New York Times has a story along these lines today and had a similar 
story in January.
    There is a constant theme in these articles, invariably including a 
quote from Nassim Taleb, that quants generally, and quantitative risk 
managers specifically, missed the boat by thinking, despite all 
evidence to the contrary, that security returns can be modeled by a 
Normal distribution.
    This is a straw man argument. It is an attack on something that no 
one believes.
    Is there anyone well trained in quantitative methods working on 
Wall Street who does not know that security returns have fat tails? It 
is discussed in most every investment text book. Fat tails are 
apparent--even if we ignore periods of crisis--in daily return series. 
And historically, every year there is some market or other that has 
suffered a ten standard deviation move of the ``where did that come 
from'' variety. I am firmly in the camp of those who understand there 
are unanticipatable risks; as far back as an article I co-authored in 
1985, I have argued for the need to recognize that we face uncertainty 
from the unforeseeable. To get an idea of how far back the appreciation 
of this sort of risk goes in economic thought, consider the fact that 
it is sometimes referred to as Knightian uncertainty.
    Is there any risk manager who does not understand that VaR will not 
capture the risk of market crises and regime changes? The conventional 
VaR methods are based on historical data, and so will only be an 
accurate view of risk if tomorrow is drawn from the same population as 
the sample it uses. VaR is not perfect, it cannot do everything. But if 
we understand its flaws--and every professional risk manager does--then 
it is a useful guide for day-to-day market risk. If you want to add fat 
tails, fine. But as I will explain below, that is not the solution.
    So, then, why is there so much currency given to a criticism of 
something that no one believes in the first place?
    It is because quant methods sometimes fail. We can quibble with 
whether `sometimes' should be replaced with `often' or `frequently' or 
`every now and again,' but we all know they are not perfect. We are 
not, after all, talking about physics, about timeless and universal 
laws of the universe when we deal with securities. Weird stuff happens. 
And the place where the imperfection is most telling is in risk 
management.
    When the risk manager misses the equivalent of a force five 
hurricane, we ask what is wrong with his methods. By definition, what 
he missed was a ten or twenty standard deviation event, so we tell him 
he ignored fat tails. There you have it, you failed because you did not 
incorporate fat tails. This is tautological. If I miss a large risk--
which will occur on occasion even if I am fully competent; that is why 
they are called risks--I will have failed to account for a fat tailed 
event. I can tell you that ahead of time. I can tell you now--as can 
everyone in risk management--that I will miss something. If after the 
fact you want to castigate me for not incorporating sufficiently fat 
tailed events, let the flogging begin.
    I remember a cartoon that showed a man sitting behind a desk with a 
name plate that read `risk manager.' The man sitting in front of the 
desk said, ``Be careful? That's all you can tell me, is to be 
careful?'' Observing that extreme events can occur in the markets is 
about as useful as saying ``be careful.'' We all know they will occur. 
And once they have occurred, we will all kick ourselves and our risk 
managers and our models, and ask ``how could we have missed that?''
    The flaw comes in the way we answer that question, a question that 
can be stated more analytically as ``what are the dynamics of the 
market that we failed to incorporate.'' If we answer by throwing our 
hands into the air and saying, ``well, who knows, I guess that was one 
of them there ten standard deviation events,'' or ``what do you expect; 
that's fat tails for you,'' we will be in the same place when the next 
crisis arrives. If instead we build our models with fatter and fatter 
tailed distributions, so that after the event we can say, ``see, what 
did I tell you, there was one of those fat tailed events that I 
postulated in my model,'' or ``see, I told you to be careful,'' does 
that count for progress?
    So, to recap, we all know that there are fat tails; it doesn't do 
any good to state the mantra over and over again that securities do not 
follow a Normal distribution. Really, we all get it. We should be 
constructive in trying to move risk management beyond the point of 
simply noting that there are fat tails, beyond admonitions like ``hey, 
you know, shit happens, so be careful.'' And that means understanding 
the dynamics that create the fat tails, in particular, that lead to 
market crisis and unexpected linkages between markets.
    What are these dynamics?
    One of them, which I have written about repeatedly, is the 
liquidity crisis cycle. An exogenous shock occurs in a highly leveraged 
market, and the resulting forced selling leads to a cascading cycle 
downward in prices. This then propagates to other markets as those who 
need to liquidate find the market that is under pressure no longer can 
support their liquidity needs. Thus there is contagion based not on 
economic linkages, but based on who is under pressure and what else 
they are holding. This cycle evolves unrelated to historical 
relationships, out of the reach of VaR-types of models, but that does 
not mean it is beyond analysis.
    Granted it is not easy to trace the risk of these potential 
liquidity crisis cycles. To do so with accuracy, we need to know the 
leverage and positions of the market participants. In my previous post, 
``Mapping the Market Genome,'' I argued that this should be the role of 
a market regulator. But even absent that level of detail, perhaps we 
can get some information indirectly from looking at market flows.
    No doubt there are other dynamics that lead to the fat tailed 
events currently frustrating our efforts to manage risk in the face of 
market crises. We need to move beyond the fat-tail critiques and the 
`be careful' mantra to discover and analyze them.

The Myth of Non-correlation

    See http://rick.bookstaber.com/2007/09/myth-of-noncorrelation.html

    [This is a modified version of an article I wrote that appeared in 
the September, 2007 issue of Institutional Investor.]

    With the collapse of the U.S. sub-prime market and the after-shocks 
that have been felt in credit and equity markets, there has been a lot 
of talk about fat tails, 20 standard deviation moves and 100-year 
event. We seem to hear such descriptions fairly frequently, which 
suggests that maybe all the talk isn't really about 100-year events 
after all. Maybe it is more a reflection of investors' market views 
than it is of market reality.
    No market veteran should be surprised to see periods when 
securities prices move violently. The recent rise in credit spreads is 
nothing compared to what happened in 1998 leading up to and following 
the collapse of hedge fund Long-Term Capital Management or, for that 
matter, during the junk bond crisis earlier that decade, when spreads 
quadrupled.
    What catches many investors off guard and leads them to make the 
``100 year'' sort of comment is not the behavior of individual markets, 
but the concurrent big and unexpected moves among markets. It's the 
surprising linkages that suddenly appear between markets that should 
not have much to do with one other and the failed linkages between 
those that should march in tandem. That is, investors are not as 
dumbfounded when volatility skyrockets as when correlations go awry. 
This may be because investors depend on correlation for hedging and 
diversifying. And nothing hurts more than to think you are well hedged 
and then to discover you are not hedged at all.

Surprising Market Linkages

    Correlations between markets, however, can shift wildly and in 
unanticipated ways--and usually at the worst possible time, when there 
is a crisis with volatility that is out of hand. To see this, think 
back on some of the unexpected correlations that have haunted us in 
earlier market crises:

          The 1987 stock market crash. During the crash, Wall 
        Street junk bond trading desks that had been using Treasury 
        bonds as a hedge were surprised to find that their junk bonds 
        tanked while Treasuries strengthened. They had the double 
        whammy of losing on the junk bond inventory and on the hedge as 
        well. The reason for this is easy to see in retrospect: 
        Investors started to look at junk bonds more as stock-like risk 
        than as interest rate vehicles while Treasuries became a safe 
        haven during the flight to quality and so were bid up.

          The 1997 Asian crisis. The financial crisis that 
        started in July 1997 with the collapse of the Thai baht sank 
        equity markets across Asia and ended up enveloping Brazil as 
        well. Emerging-markets fund managers who thought they had 
        diversified portfolios--and might have inched up their risk 
        accordingly--found themselves losing on all fronts. The reason 
        was not that these markets had suddenly become economically 
        linked with Brazil, but rather that the banks that were in the 
        middle of the crisis, and that were being forced to reduce 
        leverage, could not do so effectively in the illiquid Asian 
        markets, so they sold off other assets, including sizable 
        holdings in Brazil.

          The fall of Long-Term Capital Management in 1998. 
        When the LTCM crisis hit, volatility shot up everywhere, as 
        would be expected. Everywhere, that is, but Germany. There, the 
        implied volatility dropped to near historical lows. Not 
        coincidentally, it was in Germany that LTCM and others had 
        sizable long volatility bets; as they closed out of those 
        positions, the derivatives they held dropped in price, and the 
        implied volatility thus dropped as well. Chalk one up for the 
        adage that markets move to inflict the most pain.

    And now we get to the crazy markets of August 2007. Stresses in a 
minor part of the mortgage market--so minor that Federal Reserve Board 
Chairman Ben Bernanke testified before Congress in March that the 
impact of the problem had been ``moderate''--break out not only to 
affect other mortgages but also to widen credit spreads worldwide. And 
from there, sub-prime somehow links to the equity markets. Stock market 
volatility doubles, the major indexes tumble by 10 percent and, most 
improbable of all, a host of quantitative equity hedge funds--which use 
computer models to try scrupulously to be market neutral--are hit by a 
``100-year'' event.
    When we see this sort of thing happening, our not very helpful 
reaction is to shake our heads as if we are looking over a fender 
bender and point the finger at statistical anomalies like fat tails, 
100-year events, black swans, or whatever. This doesn't add much to the 
discourse or to our ultimate understanding. It is just more 
sophisticated ways of saying we just lost a lot of money and were 
caught by surprise. Instead of simply stating the obvious, that big and 
unanticipated events occur, we need to try to understand the source of 
these surprising events. I believe that the unexpected shifts in 
correlation are caused by the same elements I point to in my book as 
the major cause of market crises: complexity and tight coupling.

Complexity

    Complexity means that an event can propagate in nonlinear and 
unanticipated ways. An example of a complex system from the realm of 
engineering is the operation of a nuclear power plant, where a minor 
event like a clogged pressure-release valve (as occurred at Three Mile 
Island) or a shift in the combination of steam production and fuel 
temperature (as at Chernobyl) can cascade into a meltdown.
    For financial markets, complexity is spelled d-e-r-i-v-a-t-i-v-e-s. 
Many derivatives have nonlinear payoffs, so that a small move in the 
market might lead to a small move in the price of the derivative in one 
instance and to a much larger move in the price in another. Many 
derivatives also lead to unexpected and sometimes unnatural linkages 
between instruments and markets. Thanks to collateralized debt 
obligations, this is what is at the root of the first leg of the 
contagion we observed from the sub-prime market. Sub-primes were 
included in various CDOs, as were other types of mortgages and 
corporate bonds. Like a kid who brings his cold to a birthday party, 
the sickly sub-prime mortgages mingled with these other instruments.
    The result can be unexpected higher correlation. Investors that 
have to reduce their derivatives exposure or hedge their exposure by 
taking positions in the underlying bonds will look at them as part of a 
CDO. It doesn't matter if one of the underlying bonds is issued by a 
AA-rated energy company and another by a BB financial; the bonds in a 
given package will move in lockstep. And although sub-prime happens to 
be the culprit this time around, any one of the markets involved in the 
CDO packaging could have started things off.

Tight Coupling

    Tight coupling is a term I have borrowed from systems engineering. 
A tightly coupled process progresses from one stage to the next with no 
opportunity to intervene. If things are moving out of control, you 
can't pull an emergency lever and stop the process while a committee 
convenes to analyze the situation. Examples of tightly coupled 
processes include a space shuttle launch, a nuclear power plant moving 
toward criticality and even something as prosaic as bread baking.
    In financial markets tight coupling comes from the feedback between 
mechanistic trading, price changes and subsequent trading based on the 
price changes. The mechanistic trading can result from a computer-based 
program or contractual requirements to reduce leverage when things turn 
bad.
    In the '87 crash tight coupling arose from the computer-based 
trading of those running portfolio insurance programs. On Monday, 
October 19, in response to a nearly 10 percent drop in the U.S. market 
the previous week, these programs triggered a flood of trades to sell 
futures to increase the hedge. As those trades hit the market, prices 
dropped, feeding back to the computers, which ordered yet more rounds 
of trading.
    More commonly, tight coupling comes from leverage. When things 
start to go badly for a highly leveraged fund and its collateral drops 
to the point that it no longer has enough assets to meet margin calls, 
its manager has to start selling assets. This drops prices, so the 
collateral declines further, forcing yet more sales. The resulting 
downward cycle is exactly what we saw with the demise of LTCM.
    And it gets worse. Just like complexity, the tight coupling born of 
leverage can lead to surprising linkages between markets. High leverage 
in one market can end up devastating another, unrelated, perfectly 
healthy market. This happens when a market under stress becomes 
illiquid and fund managers must look to other markets: If you can't 
sell what you want to sell, you sell what you can. This puts pressure 
on markets that have nothing to do with the original problem, other 
than that they happened to be home to securities held by a fund in 
trouble. Now other highly leveraged funds with similar exposure in 
these markets are forced to sell, and the cycle continues. This may be 
how the sub-prime mess expanded beyond mortgages and credit markets to 
end up stressing quantitative equity hedge funds, funds that had 
nothing to do with sub-prime mortgages.
    All of this means that investors cannot put too much stock in 
correlations. If you depend on diversification or hedges to keep risks 
under control, then when it matters most it may not work.

                    Biography for Richard Bookstaber

    Richard Bookstaber has worked in some of the largest buy-side and 
sell-side firms, in capacities ranging from risk management to 
portfolio management to derivatives research.
    Over the past decade he has worked as a risk manager at Bridgewater 
Associates in Westport, Connecticut, Moore Capital Management and Ziff 
Brothers Investments. He also ran the FrontPoint Quantitative Fund, a 
market neutral long/short equity fund, at FrontPoint Partners.
    From 1994 through 1998, Mr. Bookstaber was the Managing Director in 
charge of firm-wide risk management at Salomon Brothers. In this role 
he oversaw both the client and proprietary risk-taking activities of 
the firm, and served on that firm's powerful Risk Management Committee. 
He remained in these positions at Salomon Smith Barney after the firm's 
purchase by Traveler's and the merger that formed Citigroup.
    Before joining Salomon, Mr. Bookstaber spent ten years at Morgan 
Stanley in quantitative research and as a proprietary trader. He also 
marketed and managed portfolio hedging programs as a fiduciary at 
Morgan Stanley Asset Management. With the creation of Morgan Stanley's 
risk management division, he was appointed as the Firm's first Director 
of Market Risk Management.
    He is the author of four books and scores of articles on finance 
topics ranging from option theory to risk management. He has received 
various awards for his research, including the Graham and Dodd Scroll 
from the Financial Analysts Federation and the Roger F. Murray Award 
from the Institute of Quantitative Research in Finance.
    Mr. Bookstaber's most recent book is A Demon of Our Own Design--
Markets, Hedge Funds and the Perils of Financial Innovation (Wiley, 
2007).
    He received a Ph.D. in economics from MIT.

                               Discussion

    Chairman Miller. Thank you very much. We will now have 
rounds of questions of five minutes for each Member, and I will 
begin by recognizing myself for five minutes.

                   Can Economic Events Be Predicted?

    Dr. Bookstaber, what you just described, what I have heard 
you describe as gaming, I have heard celebrated on the 
Financial Services Committee, on which I also serve, as 
innovation--that a lot of innovation seems to be simply a way 
to evade existing regulations. And I think both of you got it--
Dr. Bookstaber, in that last bit of testimony you certainly got 
at it, but the supporters of the VaR now they say want a do-
over, that the VaR model was perhaps flawed but it can be 
fixed, and they can now develop a more reliable model that will 
predict fat tail events, the unlikely events. Do you think that 
it is a failure of that model, or do you think the failure is 
in the idea that economic events can be predicted with the same 
precision that the movement of the planets can be predicted? Do 
you think that it is inherently flawed to think that we can 
develop models that will be unfailingly reliable? Dr. Taleb.
    Dr. Taleb. This is my life story. From the beginning--and I 
heard, Dr. Bookstaber and I share a lot of opinions, you know, 
on things like gaming, like the numbers that are going to be 
gamed on the interaction between model and participants. 
However, there are two things or three things that I heavily 
disagree with, and the first one is, he said that we can use 
different distribution to model tail events. Well, that is the 
story of my life. This is why I provided this paper forthcoming 
in which I look at 20 million pieces of data, every single 
economic variable I could find, and tried to see if there is 
regularity in the data helping to predict itself, you know, 
outside that sample from which it was derived. Unfortunately, 
it is impossible, and that is my first argument, that the more 
remote the event, the less we can predict it, and that's my 
first point. And the second one is, we know which variables are 
more unpredictable than others, and it is very easy to protect 
against that. And the third one is that I agree with Dr. 
Bookstaber; if I were, you know, an omnipotent person seeing 
all the leverage and everything in the system, and equipped 
with heavy, you know, equations, I could probably figure it 
out. However, this is Soviet-style thinking, that someone, some 
regulator, some unit out there can see what is going on and be 
able to model it, because unfortunately when we model in 
complex systems, we have non-linearity. Even if I gave you all 
the data and you missed something by $1 million, okay--your 
probabilities will change markedly.
    Chairman Miller. I will get to you, Dr. Bookstaber, but 
your solution then is just higher liquidity requirements?
    Dr. Taleb. No, my solution is figuring out--it is very 
simple. I was a trader in the 1980s. There were some products 
we could really risk manage on a napkin. Options, instruments, 
futures, all these we could risk manage on a napkin. Once we 
started having these toxic products--to me, the sole purpose of 
these products is to create bonuses, like complex derivatives. 
I was a complex derivatives trader. I have a textbook on 
complex derivatives, and I tell you, these products, okay, can 
hide massive amounts of tail risks. They are not needed for 
anyone. A lot of these products should not be there. If you 
eliminate some of the products, some of the exposure, it would 
not change anything to economic life and it would make things a 
lot more measurable. So my solution is to ban some products 
that have a toxic exposure to tail events.
    Chairman Miller. Dr. Bookstaber.
    Dr. Bookstaber. Let me just correct one point. I do not 
advocate trying to fix VaR by fattening the tails. I am simply 
arguing that some people make that as a suggestion. I think VaR 
is what it is, it does what it does, and the best thing to do 
is recognize the limitations of VaR, which I stated, and use it 
for what it is good for but not try to oversell it, not to 
think that it represents all possible risk, because any 
attempts to somehow make it more sophisticated are just going 
to obfuscate it all the more. So you take VaR as one tool for 
risk management, and then extend out from there.
    The second point, just addressing what you are saying, is 
that, number one, I don't think that you can use VaR and have a 
`do-over' to try to expand it and have it solve these crisis-
type problems. I also don't think that we will ever be at the 
point of being able to know all the risks. But I do think that 
we can move somewhat in the direction of understanding crisis 
risk more. But to do it, you need the data, and the data that 
you really need to start with is: how highly leveraged are the 
people in the market, and what are their positions--so that if 
there is a shock in a particular market, will there be so much 
leverage there that people will be forced to liquidate? What 
other positions do they have, so how could that propagate out? 
It is not a panacea. You can't have a silver bullet because of 
the feedback and gaming capabilities but I think you can move 
more in the direction of dealing with these crisis risks.

                    Regulation of Financial Products

    Chairman Miller. My time has expired but I have a question 
that is sort of in hot pursuit of what you both just said, and 
I will be similarly indulgent to the other Members here.
    Dr. Taleb, you said there should be something like a Food 
and Drug Administration (FDA) to look at financial products, to 
see if they actually do something useful, or if they simply 
create additional risks that create short-term profits. 
Apparently about 90 percent of derivatives--I was only half 
kidding when I asked you if your mother knew you designed 
derivatives. But in about 90 percent of derivatives, no party 
to the transaction has any interest in the underlying, whatever 
it was, that the derivative is derived from--credit default 
swaps. Do you agree that some financial products should simply 
be banned as having no readily discernible usefulness, utility 
for society, for the economy--and creating a risk that we 
cannot begin to understand? Should credit default swaps be 
banned? Should they be limited to--have a requirement that is 
equivalent to an insurable interest requirement in insurance 
law? Dr. Taleb or Dr. Bookstaber?
    Dr. Taleb. I cannot--I don't--I am not into regulation to 
know whether we should be allowed to ban people based on uses 
but--based on risk, okay, because society doesn't bear the 
risk. I have here what I call the `fourth quadrant,' and we 
should ban financial products--and when I call it the fourth, 
it is a little technical, but it is a very simple rule of thumb 
that takes minutes to check if a given financial product 
belongs or doesn't belong to the fourth quadrant. In other 
words, does it have any explosive toxic effects on either the 
user or the issuer, or both, you know, so it is very easy. So 
these products--and this is how I have my fourth quadrant--
these are the exposures we should not just compute, you know, 
but eliminate. And there are a lot of things we can measure. I 
mean, I may agree with Dr. Bookstaber, VaR may work for some 
products, and we know which ones, but not for these products 
that have open-ended, toxic, geometric--what I call geometric--
in other words, escalating payoffs.
    Chairman Miller. Dr. Bookstaber.
    Dr. Bookstaber. For reference, I refer the Committee to 
testimony that I gave in June to the Agricultural Committee of 
the Senate on the topic of derivatives, and there I pointed out 
that, over time, derivatives have moved more and more towards 
being used for gaming. In fact, I said that derivatives are the 
weapon of choice for gaming the system. They are used to allow 
you to hedge when you are not supposed to hedge, to avoid 
taxes, to lever when you are not supposed to lever. There is 
vested interest on both the sell and the buy side to have 
derivatives that are complex and obfuscating, that are 
customized. I believe, number one, that many derivative 
instruments that exist today are used more for either gaming or 
gambling purposes as opposed to having true economic function. 
And I believe that there are many customized and complex 
instruments that could easily be transformed into a set of 
standardized instruments that would be easier to track, more 
transparent, and possibly even put on an exchange. So I 
certainly agree with the concept that derivatives is a point to 
focus on, because it is one of the ways that we find risk 
coming in these tail events in surprising ways.
    Chairman Miller. Thank you, Dr. Bookstaber.
    I now recognize Dr. Broun for nine minutes and 45 seconds.

                           `Too Big to Fail'?

    Mr. Broun. Thank you, Mr. Chairman. I want to make a quick 
statement. I believe, first thing, that there is no such thing 
as an entity that is too big to fail, particularly when we look 
at businesses, even large businesses such as the investment 
banks, and I believe in holding people personally accountable 
and responsible, and I believe that when you take away the 
taxpayer safety net that people are utilizing to gamble away 
other people's future, then people will be held more 
accountable and will make better decisions. I think greed and 
lust are two tremendous blinding factors when people start 
making decisions.
    Having said that, I also want to state that I think that 
there were a lot of warning signs about this current economic 
crisis that we found ourselves in, and many people sounded the 
horn of warning saying that we needed to change federal law and 
regulation to prevent what has happened, and those warnings 
were unheeded by Congress and by people who were in the 
decision-making process. Having said that, I am real concerned 
too because investment banks took excessive risk based on these 
models and commercial banks are also now forced to rein in 
risk, even though they are not taking risky positions to begin 
with, those commercial banks. What can we do to ensure that 
small commercial banks around the country are not punished by 
the risky behavior of large investment banks? Either or both, 
who wants to go first?
    Dr. Bookstaber. That is a difficult question, and I don't 
know that I can illuminate it too much, but I can go in a 
particular direction. You can correct me if I am going the 
wrong way. I think there is a distinction between the larger 
banks, which de facto actually are the investment banks, and 
the smaller banks, because the larger banks end up quasi-market 
makers in the sense that they take on positions of risk for 
clients. They become market makers in the fixed-income market. 
They issue and support derivatives. They also have proprietary 
trading desks so they are also quasi-hedge funds. So I think 
you can look at the various functions of banks, and look at 
smaller banks, and they typically have a pure banking function. 
Larger banks are not really just bigger versions of smaller 
banks. They are actually institutions that take different types 
of risk that smaller banks don't take, that can have some of 
these tail events of their own creation--that are demons of 
their own design--that they have created because they have 
elected to go into the derivatives markets, or take market-
making functions.
    So I think the question for a regulator is, do you have a 
different set of regulations and requirements for the banks 
that--it is not an issue of being too big to fail, but banks 
that are taking on types of risk that make them distinct from 
their smaller cousins.
    Mr. Broun. Isn't it greed that drives that as far as the 
large institutions, though?
    Dr. Bookstaber. Well, you know, greed has a little bit of 
spin to it. I mean, there are incentives, and people act based 
on their incentives; and if we give somebody a set of 
incentives that, as Dr. Taleb has mentioned, lead them to say, 
`I want to take risks which might blow the bank up, with small 
probability, but with very high probability will give me a 
large bonus,' you are going to have people acting accordingly. 
So I think the way to think of it is, not that they are acting 
on the basis of greed, but they are acting on the basis of 
incentives that lead to behavior that, for the market overall, 
may be unduly risky.
    Mr. Broun. Isn't it so particularly when you have somebody 
else who is going to be held responsible for that decision-
making process?
    Dr. Bookstaber. Right.
    Mr. Broun. Like the taxpayer is going to be on the hook if 
they make a bad decision.
    Dr. Bookstaber. That is right. There is no doubt that 
incentives have played a large role in what we have observed. 
You know, had you had, for example, somebody like Mr. Prince 
saying--apparently recognizing the riskiness of what they are 
doing--and saying, well, as long as the music is playing, we 
are going to keep dancing. Why is he going to keep dancing? 
Because his incentive is based on next quarter's earnings, and 
he can't walk away from that dance floor while his competitors 
are still on it, because his incentives are structured to make 
that incorrect decision.
    Mr. Broun. And he has everything to gain and nothing to 
lose in that process, correct?
    Dr. Bookstaber. Yes.
    Mr. Broun. Dr. Taleb.
    Dr. Taleb. Yes. Well, I just wrote a paper with my 
colleague (Charles Tapiero) in which we showed why--I don't 
know if you have heard about the case of Societe Generale, the 
French bank that lost $7 billion, $8 billion on a rogue trader, 
and we showed that it came from too big a size. Size has effect 
in compounding risk, and let me give you the intuition. If you 
have a bank a tenth of the size of Societe Generale, and they 
had a rogue trader that had a tenth of the size of the position 
of that rogue trader, the losses would have been close to zero. 
The fact that they had to liquidate, they discovered that that 
rogue trader had 50 billion euros in hidden position and they 
had to liquidate that, and liquidating 50 billion euros rapidly 
costs a lot more than liquidating five billion euros. You 
liquidate five billion euros at no transaction cost almost, or 
a very small transaction cost, compared to liquidating 50 
billion. So that would generalize to risks of unexpected events 
tend to affect large size more.
    And I have here another comment to make about banks. Banks, 
of course, have done so far--I mean, we have evidence they have 
done, so far, very little for society, except generate bonuses 
for themselves, from the data, and that is not from recent 
events that I am deriving that. When I wrote ``The Black Swan'' 
it was before these events. But look at hedge funds. Hedge 
funds, I heard the number, 1,800 hedge fund failed in the last 
episode. I don't know if many of them made the front page of 
any Washington paper. So the hedge funds seem to be taking 
risks without endangering society, or at least not taxpayers 
directly. And this model of hedge fund corresponds to my norm, 
okay--what is a complex system that is robust? The best one is 
Mother Nature. Mother Nature has a lot of interdependence. We 
have an ecosystem, a lot of interdependence. But if you went 
and shot the largest mammal, a whale, or the largest land 
mammal, an elephant, you would not destroy the ecosystem. If 
you shot Lehman Brothers, well, you know what happened, okay. 
You destroyed the system--too much interdependence means you 
should not have large units. But hedge funds have shown us the 
way to go. They are born and they die every day, literally 
every day. Today I am sure that many hedge funds are born and 
many hedge funds have died. So this is a model that replicates 
how nature works with interdependence. But of course we have to 
listen to Dr. Bookstaber's advice to make sure that they don't 
all have the same positions you have to put the exclusionary 
system, but they have a lot more diversity than banks.

            Wall Street's Dependency on Government Bailouts

    Mr. Broun. Isn't it though that the implied or even 
outright safety net of the taxpayers picking up the pieces if 
there is a failure, isn't that the thing that is driving the 
derivatives and all these other complex financial instruments 
that cause people to make these risky behavior judgments?
    Dr. Taleb. Well, I am under oath and I will say exactly 
something that I want to be on the record. I was a trader for 
21 years, and every time I said what if we blow up, he said, 
who cares, the government bails us out. And I heard that so 
many times throughout my career, that, ``don't worry about 
extreme risks, worry about down five percent, ten percent, 
don't worry about extreme risks, they are not your problem 
anymore, it is not our problem.'' I heard that so many times, 
and here I am under oath and I say it.
    Dr. Bookstaber. If I may add to that, there is the notion, 
well known, of what is called the trader's option. The trader's 
option is, I get X percent of the upside and limited or zero of 
the downside, but that trader's option extends also in many 
cases to the management of the firms. They get the upside and 
so you would much rather, you know, construct a position that 
makes a little, makes a little, makes a little and makes a 
little and with small probability loses everything, because 
that increases the chance that you have consistent earnings, 
consistent bonuses, and in the extreme events, your downside is 
limited because of the option characteristic of your 
compensation.
    Mr. Broun. So in the ten seconds I have left, I just want 
to state that taking away the government safety net is going to 
make people more responsible and they will make better 
decisions on a real risk management basis, and I thank you all. 
It is my opinion that that is what I am getting from you all, 
correct?
    Dr. Taleb. In my opinion as well.
    Dr. Bookstaber. If I may, I would just say, it is not just 
the safety net. If I am an individual in a firm, I don't care 
about the safety net, I care about my own bonus, so with or 
without the safety net for the firm overall, if my incentives 
are, I make money if things go up, I get a new job if things 
blow up, I don't know that the safety net matters to me 
personally.
    Dr. Taleb. May I respond to this point?
    Chairman Miller. Dr. Taleb.
    Dr. Taleb. I agree that if I am a trader, I don't care who 
is going to bail me out. The problem is that the shareholders 
don't care when society can bail them out because there is 
unlimited liability, that shareholders are protected so society 
bears the rest. So we have three layers: a trader, the 
shareholder and thirdly, society. So in the end, the free 
option comes from society.
    Mr. Broun. Thank you, Mr. Chairman.
    Chairman Miller. Thank you.
    I think something like 90 percent of American households 
have a household income of less than $105,000 a year, so for a 
trader to make $100 million, $120 million does not seem like 
make a little, lose a lot.
    Ms. Dahlkemper for five minutes.
    Ms. Dahlkemper. Thank you, Mr. Chairman.
    I wanted to go back to your statement in terms of some--
that maybe some financial products should be banned, and there 
are some that may argue that banning any financial product is 
an excessive intrusion into the free market. So if you could 
just give me your response to that claim.
    Dr. Taleb. I believe in free markets but I do not believe 
in state socialism, okay, and I don't believe--I believe the 
situation we have had so far is not free markets. It is 
socialism for losses and capitalism for profits. So if the 
taxpayer is involved ultimately in bailing out, which the 
taxpayer should be able to say, I want this product or that 
product, the risk, okay? You know, my opinion, I am in favor of 
free markets but that is not my definition of free markets, 
okay, state-sponsored socialism for the losses and capitalism 
for the profit--I mean, free market for the profit. That I 
don't--as a taxpayer, and I am paying taxes.
    Ms. Dahlkemper. Dr. Bookstaber.
    Dr. Bookstaber. I think even in a capitalist system, the 
argument that some products should not go forward or should be 
banned is a reasonable one for the following reason: that if I 
construct some new product, and let us say it is a fairly 
complex product or has a fat tail and it can inflict problems 
for society, there is a negative externality to that product 
that is not priced--that is, I sell it, I create it, somebody 
wants to buy it, but the negative externality is the increased 
probability of crisis that it causes, and any time that you 
have a non-price-negative externality is a time that I think 
even a libertarian would argue you can have government 
intervention.

              The Risks of Different Tupes of Institutions

    Ms. Dahlkemper. Thank you. I also wanted to go back a 
little bit to the `too big to fail' subject in terms of the 
institutions. When we look at the surviving large banks, they 
are bigger than ever, so where do you know when an institution 
is `too big to fail' and how do we restructure these firms?
    Dr. Taleb. `Too big to fail,' you can see it. If anything 
in nature is bigger than an elephant, it won't survive, and you 
can see, I am sure anything bigger than a large hedge fund, to 
me, is too big. But there is one thing here associated with the 
problem. The reason we depend so much on banks is because the 
economy has been over-financialized over the past 25 years, 
over-financialized. The level of debt today in relation to GDP 
is three times, according to some numbers, even more or less, 
but three times the level of debt to GDP that we had in the 
1980s. So that is rather worrisome. This is why we have `too 
big' banks, all right, because it comes with the system. It is 
a process, you know, that feeds on itself, that is a recursive 
process. And if we definancialize the economy more, the debt 
level will come down. Then the discussion about `too big to 
fail,' about banks, will be less relevant. I mean, banks' role 
is not so--you know, banks where I can withdraw money when, you 
know, when I go to Atlanta and then there is a bank that is 
used for letter of credit, very useful things for society. And 
there are banks that trade for speculative reason, banks that 
issue paper that nobody needs and there are banks, the banking 
that corresponds to lending, you know, increased lending 
because a lender makes a bonus based on the size of loans. So 
if you brought this down, the size of banks would drop 
dramatically. Particularly, the balance sheets would shrink 
dramatically, and particularly if we moved the risk away from 
banks. The banks are more of a utility in the end, and they are 
hijacking us because a utility with a bonus structure, it 
doesn't work. As I said here, don't give someone managing a 
nuclear plant a bonus based on cost savings, okay, or based on 
profitability. You don't, all right? So the problem is, they 
are hijacking us because of the dual function of a utility that 
we need them to have, a letter of credit or something as basic 
as withdrawing cash, and at the same time they take risks with 
bonuses. So if we brought down the level of banking, moved the 
risks more and more to hedge funds, these people are adults, 
they don't endanger anyone, just make sure they don't get big 
and have Dr. Bookstaber's rules on, you know, leverage and 
stuff like that well enforced . . . then the level of--then 
that problem would disappear. So let us worry more about the 
cancer rather than worry about the symptoms.
    Ms. Dahlkemper. Dr. Bookstaber.
    Dr. Bookstaber. You know, the Treasury came out with some 
principles for regulation of capital on September 3, and one of 
the key issues that they mentioned is dealing with `too big to 
fail.' I think one of the difficulties is, I don't think we can 
measure too big to fail. I don't think we know. It is not just 
a matter of the capital that you have or the leverage that you 
have. For example, LTCM was a hedge fund and it was a 
relatively small firm and had $3 billion capital, yet in a 
sense it was `too big to fail' because it almost brought down, 
actually, Lehman along with it, and the Fed had to step in. 
What matters is how what you are doing weaves in with what 
other people, what other funds or firms are doing within the 
economy. So you could have a `too big to fail' that is not 
predicated on one institution and what that institution is 
doing, but it could be based on some strategy or some new 
instrument, where for anyone from that instrument that strategy 
is relatively small, but if the exogenous shock occurs in the 
market and it affects that strategy, it affects so many firms 
in the same way that it has a substantial systemic effect. And 
I get back to the point that we don't have the information to 
even know right now what type of positions or leverage or 
strategies might have that threading across different 
institutions.
    Ms. Dahlkemper. Thank you. My time is expired.
    Chairman Miller. We are about to have 40 minutes of votes 
shortly so I would like for both Mr. Wilson and Mr. Grayson to 
have a chance to ask questions. I should just tell the panel 
that this Charlie Wilson has never had a movie made about him. 
So far as I know, he has never been in a hot tub. Mr. Wilson 
for five minutes.

                    Incentive Structures for Trades

    Mr. Wilson. Thank you, Mr. Chairman.
    Gentlemen, good morning. I serve on the Financial Services 
Committee also and I have to keep pinching myself that really I 
am in a Science and Technology Subcommittee and so it is hard 
to realize the conversations we are having. Dr. Taleb, if I 
could say that, you know, what you said earlier in your 
testimony about people not being concerned about the success or 
failure of a firm because they knew there would be a public 
bailout is frightening. That is certainly not the American way 
or certainly not the way we want to do business. With those 
things in mind, I have a couple questions I would like to ask 
and maybe we can get some of your feeling as to how people 
would get so far off track, that that would be the thought 
process. That concerns me.
    People have been outraged at the size of the bonuses and 
especially when we were doing the voting for the bailout. Some 
of the employees were bailed out, as you all know, with 
government money, huge amounts of money to the Wall Street 
firms. Much of the conversation was about firms being `too big 
to fail,' and you say that in the bonuses, that is really the 
motivator for everybody. I would hate to think that there was 
no leadership that wouldn't try to keep people on the right 
track rather than money being the only motivator, the true part 
of it. So can you explain that? And I was going to address this 
question, if I could, to Dr. Bookstaber if I could.
    Dr. Taleb. You would like me to explain how people were 
handling extreme risks.
    Mr. Wilson. I did. That was confusing. I am sorry. I did 
address that to you but I would be interested in Dr. Bookstaber 
also. If you would go first, Dr. Bookstaber, please.
    Dr. Bookstaber. Thank you. I don't think--I don't mean to 
be cynical, but I don't think that leadership within a 
financial firm can overcome the incentives that exist, 
incentives not just including the trader's option, but to do 
the bidding of the people who have put you in your position, 
namely the shareholders whose interest is earnings and maybe 
even earnings quarter by quarter. So I think the way that you 
have to change things is through the incentive structure of the 
people who are taking risk in ways that has been widely 
discussed, and I think it is fairly clear that you don't 
finally get paid until whatever trade you have put on, or 
whatever position you put on, is off the books and has been 
recorded. You can't basically put on positions and get paid 
based on the flow of those positions until the trade is 
realized, that is, until the book is closed on that trade. So 
this is the notion of longer-term incentives. So if you have 
longer-term incentives, if you have incentives where you can't 
game the system by constructing trades or portfolios that again 
make a little, make a little, maybe blow up, then people will 
act based on those incentives. But the leadership of the firm 
is always going to have the following statement, that our 
responsibility is to the shareholders, we have to maximize 
shareholder value, and then the shareholders, by the way, 
although in theory they have a vote, in practice don't. And so, 
you know, you have sort of this--the management pointing 
towards the shareholders, the shareholders effectively being 
silent partners within the corporation.
    Mr. Wilson. Thank you.
    Dr. Talber, am I saying that right?
    Dr. Taleb. Taleb.
    Mr. Wilson. Taleb. I am sorry.
    Dr. Taleb. There are two problems, and I gave two names, a 
name to each problem. The first one is called, the title of my 
first book, fools of randomness, `Fooled by Randomness,' and 
other people who believe their own story and actually don't 
know that they are engaging in these huge amount of hidden 
risks out of psychological, you know--as humans, we are not 
good at seeing negative outcomes. We are victims of 
overconfidence, so we make these mistakes whether or not there 
is a bonus, is the psychological, the first one. And the second 
one, I call them `crooks of randomness,' so there is `fools of 
randomness' and `crooks of randomness,' and you always have the 
presence of both ailments in a system. Like, for example, when 
we had LTCM, Long-Term Capital Management, the problem, these 
people had their money in it, so, visibly, they were not gaming 
the system consciously, all right, they were just incapable of 
understanding that securities can take large variations. So 
there are these two problems. So the bonus, it is imperative to 
fix the bonus structure, and as I said here, that I don't know 
any place in society where people manage risk and get a bonus. 
The military people, the police, they don't get a bonus. So 
fix, make sure that he who bears risk for society doesn't have 
a bonus. Fix the bonus structure that is not sufficient.
    Mr. Wilson. One of the things that, you know, we have heard 
a lot about since the money was invested in Wall Street was 
that if the big bonuses didn't continue, the firms couldn't 
necessarily keep the talent. Do you have any comment on that, 
Dr. Taleb?
    Dr. Taleb. I am laughing, sorry, because a lot of these 
people--in my book there is a gentleman who had $10 million, a 
big hotshot trader, and when he blew up, he couldn't even drive 
a car. I mean, you can find better cab drivers. I don't know 
what you could do with these Wall Street derivatives, high-
income people other than use them as drivers but even then, I 
mean, you can use someone less reckless as a driver. So I don't 
know what to use them for, honestly. I don't know what is the--
I was on Wall Street for 21 years and a lot of people I 
wouldn't use for anything. I don't know if you have some 
suggestions. So I don't know what you are competing against, 
all right, and you have high unemployment on Wall Street, and 
calling that `talent' is a real--it is a very strange use of 
language, people who lost $4.3 trillion worldwide in the 
profession, and then calling it `talent.' So there is talent in 
generating bonuses, definitely, that you cannot deny. Other 
than that, I don't know.
    Dr. Bookstaber. There is--on this point, there are people 
who are not merely talented, but gifted, in areas like medicine 
and physics and other fields and they seem to get by on some 
amount of money, $200,000, $500,000, $1,000,000. I don't know 
that the talent in Wall Street is so stellar that it is worth 
$50 million or $100 million versus the talent in these other 
fields. The issue with the talent more is that the structure of 
Wall Street somehow allows that level of compensation, so if 
one firm does not allow it, people can move to another firm 
that does. But if there is a leveling of the field overall so 
that instead of $20 million people are making $1 million or $2 
million, you know, then I think this issue of, you know, `we 
will lose our talent' disappears. It has to be done in a 
uniform way, as opposed to affecting just one firm versus 
another.
    Mr. Wilson. Thank you.
    Chairman Miller. Dr. Taleb, do you want to respond?
    Dr. Taleb. Yes, I have one comment. He is making a 
socialistic argument to limit bonuses. I am making a 
capitalistic argument to limit bonuses. I am saying if people 
want to pay each other, they can pay whatever they want. I just 
don't want society to subsidize bonuses. That is it. I am 
making the opposite argument coming from--so this is an extreme 
bipartisan conclusion here where----
    Mr. Wilson. We have a few of those here.
    Dr. Taleb. If people want to take risk, you know, and two 
adults can hurt each other financially as much as they want. 
The problem is, as a taxpayer, okay, I don't want these 
bonuses.
    Mr. Wilson. Thank you. Thank you both.
    Mr. Chairman, just one comment if I could. It just seems 
that we have to try to find a way to legislate maybe some 
character to Wall Street.
    Chairman Miller. Thank you. I misread the note that said 
that we would shortly have 40 minutes of votes. We will have 
votes at around 11:45 and they will last 40 minutes, so I am 
delighted that we will be able to continue with this panel for 
Mr. Grayson and for a second round of questioning. Mr. Grayson.

              Holding Wall Street Accountable for Bonuses

    Mr. Grayson. Thank you, Mr. Chairman.
    We are talking today about what proper incentive structures 
we should have on Wall Street, and I am wondering if we are 
talking too much about carrots and not enough about sticks. In 
fact, people on Wall Street did lose over $4 trillion of our 
money, and I have seen almost no one punished for it. Don't you 
think that it would be likely to deter bad behavior and an 
overly fond view of risks if we actually punished people?
    Dr. Taleb. I am not a legal scholar but there has got to be 
a way to--there is something called malpractice, okay. There 
has got to be a way where we can go after these people that I 
haven't seen so far, because people are scared, because Wall 
Street has `talents.' These people would run away and go to 
Monte Carlo or something, so we are afraid of letting them, you 
know, of them running away, but we should be doing it 
immediately, find people who made [these losses]--like the 
Chairman of an executive committee or the firm that we had to 
support who made $120 million of bonuses, and supervised 
unfettered risk taking and made sure that that gentleman got 
returns of $120 million bonuses. The place where my idea was 
most popular was Switzerland. The first event of a clawback in 
any country took place in Switzerland, where the authorities 
went to Mr. Marcel Ospel, head of UBS, after the events of 
October and told him, listen, give us 12 million Swiss francs, 
please, and it was voluntary and he gave back almost--a large 
share of his--but he clawed back his bonuses.
    Mr. Grayson. But it was voluntary only because the 
government intervenes by limiting people's liability. The 
concept of liability is determined by our law, not by the free 
market. In fact, if we were to say that we will not give people 
the right to hide behind corporate shields, wouldn't that have 
a dramatic effect on holding people accountable for the bad 
decisions that they make?
    Dr. Taleb. To answer, okay, this is still the same problem, 
fooled by randomness or not fooled by randomness. Some people I 
have seen in Chicago trade their own money and lose huge 
amounts of money, not knowing they could lose it, so someone 
whose net worth is $2 million loses $2 million and had to go 
burn his house to collect insurance money. So I have seen that. 
It is not just--so people sometimes engage in crazy trades, 
okay, where they have liability themselves. It may not be 
sufficient, but it would be, for me, economically, a good way 
to have a bonus compensated by malice because capitalism is not 
just about incentives, it is about punishment.
    Mr. Grayson. When you say it wouldn't be sufficient, all 
you are really saying is that it wouldn't solve the problem for 
all time, forever in every case, but it would certainly be a 
step in the right direction.
    Dr. Taleb. Oh, it would be imperative, not a step.
    Mr. Grayson. Imperative?
    Dr. Taleb. It is an imperative.
    Mr. Grayson. Okay. Now, Dr. Bookstaber, I understand that 
in Sweden, the bank managers have unlimited liability for the 
mistakes that they make, but what happened in our system with 
regard to blow-ups, with regard to crazy risks that people take 
in order to pad their own pockets, what effect would that have 
if we were to take that law and introduce it in America?

                     Malpractice in Risk Management

    Dr. Bookstaber. You know, something along those lines that 
I have advocated is to have the potential of penalties for the 
risk managers within a firm similar to what are there for the 
CFO of a firm. You know, if a CFO knowingly allows some 
accounting statement to go out, where he knows it is incorrect, 
he is on the hook not just from a civil but from a criminal 
standpoint. If you had the risk managers have to sign on the 
dotted line, that the risk--that they have executed their 
function correctly, and all material risks have been duly 
represented--I think that could go a long way towards solving 
the problem, because they would then have an incentive to make 
sure everything is right. And there are cases, I think, as we 
go back to this last crisis, where the risk managers were in 
some sense not up to the task, or possibly in bed with the 
people involved in trading or with senior management, to where 
they were willing to have their views overridden--because they 
had no liability on the one side, and they didn't want to get 
fired on the other.
    Mr. Grayson. But don't we have to do more than that? Don't 
we have to not only say to people, you have to fill out these 
forms properly and you have to disclose, but we have to 
actually hold people accountable for the mistakes that they 
make, and hold them personally accountable? Isn't that what we 
need to actually deter this kind of misconduct?
    Dr. Bookstaber. I guess the question is what type of 
mistake, because everybody makes certain types of mistakes. I 
think that sort of mistake where you can hold people 
accountable is where they--obviously if they knowingly 
misrepresent--but where there is something material that they--
on the one hand it is a malpractice where you say, you know, 
somebody doing this job in a reasonable way should have 
discovered that.
    Mr. Grayson. But let us talk about the specific problems we 
have seen time and time again in the last few years. Let us 
talk about, for instance, AIG. In AIG, the fundamental problem 
is that the traders entered into literally billions upon 
billions of dollars of heads, I win, tails, you lose bets, bets 
that couldn't possibly be made good on by anybody but the U.S. 
Government, and that wasn't a problem of not filling out the 
form properly, not disclosing. Don't those people need to be 
punished in order to deter that conduct in the future?
    Dr. Bookstaber. Well, this gets to Dr. Taleb's point that 
you would have to go into the mindset of the people. Was it, as 
he is saying, you know----
    Dr. Taleb. Crooks or fools.
    Dr. Bookstaber. Yeah, were they crooks or fools. If you can 
discern one from the other, then I agree with you, but what I 
am saying is, you could also go one level higher to require, 
which now is required, risk management oversight for those 
functions where it is believed to be credible, and these were 
supposed to be the people who know how to do their job, and 
they have the responsibility to represent that this type of 
event is not occurring.
    Mr. Grayson. Dr. Taleb.
    Dr. Taleb. Yes. Well, the problem I saw and I wrote about, 
actually, in one of my writings not yet published, I say it is 
easier to fool a million than fool a person and it is much 
easier to fool people with billions than to fool them with 
millions. Why? Because you have bandwagon effects, and you have 
collective--something called diffusion of collective 
responsibility, and I will tell you exactly why. If you have--
what risk managers are doing is to make sure they do exactly 
what other risk managers do. If there is a mistake, it is a 
mistake that they did not commit individually, but committed--
that had company on that. We call it `company on a trade.' It 
is not like an individual doctor who is just incompetent. It is 
collective incompetence. We had collective risk management 
incompetence, but they were all doing what other people--the 
hedge is to do what the other guy is doing and that, I don't 
know if, you know----
    Chairman Miller. Well, the note I got earlier was incorrect 
and now it appears we are going to have votes at any moment, so 
I will start a round of questions and we will try to keep it--I 
know that everybody would like to ask questions of this panel.

                          Clawback Provisions

    Just one--it is not clear to me whether you actually 
supported a legal requirement that there be clawback provisions 
in bonus contracts, that if a bonus is based upon a profit this 
year, that if the very same transaction results in a loss in 
two or three years there be requirement, a legal requirement 
that that bonus be repaid. Dr. Taleb?
    Dr. Taleb. Indeed.
    Chairman Miller. You do----
    Dr. Taleb. Indeed.
    Chairman Miller. Dr. Bookstaber.
    Dr. Bookstaber. I don't know that I would go to the extent 
of having it be a legal requirement. Ideally, it should be 
requirements placed on the corporation by the equity holders, 
because it makes good economic sense. I think the issue of it 
being a legal requirement gets into the question of, okay, if 
we are ultimately the ones holding the bag if this fails, we 
now have a societal obligation. But I think whether it is done 
through the shareholders or if it is legislated, that type of 
structure, incentive structure, clearly makes sense for 
trading.
    Chairman Miller. Dr. Taleb.
    Dr. Taleb. There is an additional problem other than the 
clawback. There is the fact that if in any given year, I take 
$1 million from you, okay--say I win, I get my bonus, and I 
lose, you keep all the losses, so that clawback situation 
doesn't solve the free option problem. You are solving the 
mistiming problem, you are not solving the free option problem.
    So we have two problems with bonuses. The first one is 
symmetry. In other words, I make, all right, either a big bonus 
or nothing, whereas if he loses, I take his money, risk his 
money. He loses or makes [money], all right, I just make 
[money], I just earn. So that problem is not solved with the 
clawback. For example, say the TARP money we gave Goldman, all 
right--okay, let us forget about clawbacks. Had they lost 
money, all right, it would have been--we would have eaten the 
loss. If they made money, they kept the bonuses, okay, so that 
idea of having just profits and never losses, net, net . . . 
the clawback is about repaying previous bonuses, but it doesn't 
address the vicious incentive of paying someone for the profits 
and not charging him for the overall losses, and the clawback 
doesn't solve that.
    Chairman Miller. Are you suggesting that that should be 
prohibited by law, or should people just have better sense than 
to agree to that kind of compensation system?
    Dr. Taleb. In other words, people should have skin in the 
game. Net, net, net, if I fail, I should be penalized 
personally some way or another. Don't have an option where I 
only have the profits and none of the losses.
    Chairman Miller. I am still not clear if you are suggesting 
that that be a legal requirement or there simply should be a 
change in the culture, that anyone who agrees to a hedge fund 
compensation of 220 is a fool, and if people stopped agreeing 
to it, the compensation system would change.
    Dr. Taleb. No, to me, it should be only a legal requirement 
wherever TARP or a possible society bailout is possible. If 
there is no society--if someone signs no society bailout, then 
no.

                          Credit Default Swaps

    Chairman Miller. I asked the question earlier but I am not 
sure I got a clear answer. Do you think credit default swaps 
should be banned? If not, do you think they should be limited 
to--they should have a requirement that would be comparable to 
the requirement of an insurable interest in insurance law?
    Dr. Bookstaber. I agree with the latter. I don't believe 
that credit default swaps should be banned, because they do 
have economic function in the sense that--if I have the debt of 
a company and perhaps it is illegal, or for some reason it is 
difficult for me to undo my risk by selling it, I can use the 
swap to mitigate or hedge my risk. But I don't think that it 
should turn into what it has turned into--basically, a gambling 
parlor of side bets for people who have no economic interest at 
all in the underlying firm. The point you mentioned, Mr. 
Chairman, in your opening remarks, that the number of people 
doing side bets far exceeds those who actually have an economic 
reason to be taking that exposure.
    Chairman Miller. Dr. Taleb.
    Dr. Taleb. Mr. Chairman, these products are absurd. They 
are class B products for me, for the simple reason that it is 
like someone buying insurance on the Titanic from someone on 
the Titanic. These credit default swaps, you buy them from a 
bank, so they make no sense. And I have been writing about 
these class B instruments that have absolutely no meaning and I 
don't believe that they have economic justification other than 
[to] generate bonuses.
    Chairman Miller. The other analogy I have heard is buying 
insurance against a nuclear holocaust; if you think you are 
going to be around to file a claim, who do you think you are 
going to file it with. I will give up my own time; Dr. Broun.

            Were the Bailouts and Stimulus Funds Necessary?

    Mr. Broun. Thank you, Mr. Chairman. Do you believe that 
bailing out banks and transferring debt from private sources to 
public sources is a responsible action?
    Dr. Taleb. I mean, my opinion is, I am going to be very, 
very, very honest--it is irresponsible because we have levels 
of about $60 trillion, $70 trillion worldwide in excess debt 
that is being slowly transformed into something for our 
children. If a company goes bankrupt, that debt disappears the 
old-fashioned way or it turns into equity. If government bails 
out a company, it is a debt that our children and grandchildren 
will have to bear. So it doesn't reduce debt in society, and 
this is why I have been warning against the stimulus packages 
and all of these. Transforming private debt into public debt is 
vastly more vicious than just taking the pain of reducing the 
level of debt.
    Mr. Broun. Dr. Bookstaber.
    Dr. Bookstaber. In the abstract, I don't think that makes 
sense. In the current crisis, I think it was inevitable, 
because we had to adjust for problems that got us to where we 
are. So I would say we would want to construct a system with 
regulatory safeguards, with adequate capital, with correct 
incentives so that the event doesn't occur where we have to 
move into the bailout mode that we had in the recent past. But 
my sense is that if we hadn't taken this action, as distasteful 
and costly as it may be, the end results for the economy may 
have been far worse.
    Mr. Broun. So you believe that stimulus spending and debt 
accumulation and the bailouts are all necessary responses to 
this economic crisis, is what I am gathering.
    Dr. Bookstaber. Yes, I believe they were for this crisis. I 
don't believe that as a general principle it is something that 
we want to occur, and hopefully we can take steps so that it 
doesn't occur again.
    Mr. Broun. Dr. Taleb.
    Dr. Taleb. I don't believe in deficit spending for the 
following reason, and it comes from the very same mathematics 
that I used to talk about tail risks. We live in a very 
nonlinear world--as you know, the butterfly effects, a 
butterfly in India causes a rainstorm in Washington. You know, 
these small, little--we don't quite understand the link between 
action and consequences in some areas, particularly monetary 
policy. So if you have deficit spending, it is debt that 
society has to repay someday, okay? You depend a lot more on 
expert error and projections. I showed in ``The Black Swan,'' 
in my book, ``The Black Swan'' from 27,000 economic 
projections, that an astrologist would do better than 
economists, including, you know, some people here who are 
economists making projections. So I don't want to rely on 
expert projections to be able to issue a stimulus and say oh, 
no, no, look what will happen by 2014, we will be paying it 
back. These are more of the huge errors.
    So what is the solution? The solution is going to be that 
all this, all right, may lead to what governments have been 
very good at doing for a long time--printing, okay. And we know 
the consequences of printing; everybody would like to have a 
little bit of inflation but you cannot. Because of non-
linearities, it is almost impossible to have the 3.1 percent 
inflation everybody would love to have. You see, a little bit 
of error could cause hyperinflation, or if you do a little 
less, maybe it would be ineffective. So to me, deficit 
spending, aside from the morality of transferring, you know, 
private debt into my children's debt--okay, aside from that, 
because someone has got to buy that bond, okay, the way it may 
lead--you know, because of error in projection--[is] into 
printing of money.
    Mr. Broun. So from my previous questions as well as 
others', I take it that both of you all would agree, looking in 
the future, not only with this economic crisis but in the 
future, to prevent other economic crises, the real solution is 
to take away the taxpayer safety net which was implied and now 
with Freddie and Fannie is express taxpayers being on the hook 
for this mismanagement and their bad decisions. Would you both 
agree, yes or no, that taking away that safety net will help 
people be more responsible, and we will have more of the sticks 
that my colleague was talking about and that they can within 
their own company just to protect their own company's 
viability, et cetera, will put in place more responsible risk 
management and they will make better decisions. Would you both 
agree with that statement?
    Dr. Taleb. I agree with the statement, remove the safety 
net.
    Dr. Bookstaber. I don't know that I can say yes or no 
because I have to envision what the future world looks like. If 
we make no changes in terms of regulation and oversight, then I 
wouldn't agree with the notion of taking away the safety net 
because we have a flawed system where there is a notion of `too 
big to fail' . . . where if certain institutions do fail, it 
has severe adverse consequences for people on Main Street. I 
think that we have to say, we want to get rid of the safety 
net, and to do that we need to get the corrective incentive 
structures, the correct level of oversight from regulators, the 
right capital requirements. So as an end result, that is where 
I believe we should go, but I don't think we can be there in 
good conscience for the typical citizen without doing a better 
job, you know, in the regulatory arena.
    Dr. Taleb. I don't understand this logic because I don't 
see how--in 1983, when banks were bailed out, and even one of 
them was the First National Bank of Chicago. It set a bad 
precedent. Every time I heard the same argument, you hear the 
same argument, ``this is necessary, society can't function, but 
in the future we'll make sure we don't do it again.'' I don't 
understand this argument.
    Mr. Broun. Thank you, Mr. Chairman.
    Chairman Miller. Ms. Dahlkemper? Okay, Mr. Wilson?
    Mr. Broun. I think we need to go vote.
    Chairman Miller. We have been called to our votes. Thank 
you very much to this panel. We will be gone for about 20 
minutes, not 40 minutes as I earlier understood. But at that 
point it does make sense to excuse this panel, but thank you 
very much. It has been very helpful and even entertaining. And 
then when we come back, when we return we will have the second 
panel, although these are the last votes of the week so it is 
possible some Members will not come back but go straight to the 
airport. Thank you, and we will be at ease.
    [Recess.]

                               Panel II:

    Chairman Miller. Other Members may return or may not, but I 
think we should begin the second panel, and I also mean it when 
I say that this panel is unusually distinguished. Our witnesses 
are leading experts in their respective fields. Dr. Gregg 
Berman is the Head of Risk Business at RiskMetrics Group, which 
is the present-day descendant of the group at J.P. Morgan that 
created the Value-at-Risk methodology. He has worked with many 
of the world's largest financial institutions on the 
development of risk models. Mr. James Rickards is the Senior 
Managing Director of the consulting firm Omnis Inc., is a 
former risk manager and investment banker who has been involved 
in the launch of several hedge funds. As general counsel of 
Long-Term Capital Management during the 1998 crisis, he was the 
firm's principal negotiator of a bailout plan that rescued it. 
And Mr. Christopher Whalen is the Managing Director at 
Institutional Risk Analytics, a provider of risk management 
tools and consulting services. He volunteers as the Regional 
Director of the Professional Risk Managers International 
Association, and edits a weekly report on global financial 
markets. And finally, Dr. David Colander, the Christian A. 
Johnson Distinguished Professor of Economics at Middlebury 
College, has written or edited over 40 books, more than 40 
books, including a top-selling Principals of Economics textbook 
and more than 150 articles on various aspects of economics, 
including the sociology of the economics profession.
    You will also have five minutes for your oral testimony, 
your spoken testimony. Your written testimony will be included 
in the record for the hearing. When you have completed your 
spoken testimony, when all of you have, we will have rounds of 
questions from the Members who are here, which may include me 
repeatedly. It is the practice of this subcommittee, as you saw 
earlier, to receive testimony under oath. Again, I don't think 
any of you have to worry about perjury. That would require that 
the prosecutor prove what the truth was, beyond a reasonable 
doubt, and that you knew what the truth was beyond a reasonable 
doubt. Do any of you have any objection to swearing an oath? 
Okay, and I think you may sleep easy tonight without worrying 
about perjury prosecution. You also have the right to be 
represented by counsel. Do any of you have counsel here? And 
all the witnesses said that they do not. If you would now 
please stand and raise your right hand. Do you swear to tell 
the truth and nothing but the truth?
    The record will show that all the witnesses did take the 
oath. We will begin with Dr. Berman.

   STATEMENT OF DR. GREGG E. BERMAN, HEAD OF RISK BUSINESS, 
                       RISKMETRICS GROUP

    Dr. Berman. Thank you. I would like to begin by thanking 
the Committee for this opportunity to present our thoughts on 
Value-at-Risk and banking capital, especially in the context of 
the present financial crisis.
    My name is Gregg Berman and I am currently the Head of the 
Risk Business at RiskMetrics Group. I joined as a founding 
partner 11 years ago when we first spun off from J.P. Morgan, 
and throughout that time I have had a number of roles, from 
leading research and development to leading product design, but 
mostly spending time with clients, and those clients include 
some of the world's largest hedge funds, largest asset managers 
and certainly the world's largest banks. During that time, and 
even under oath I feel I can say this, I have not traded any 
derivatives in any way, shape or form.
    My comments today revolve around three essential points. 
First, Value-at-Risk, or simply `VaR,' was created about 15 
years ago to address issues faced by risk managers of large, 
multi-asset, complex portfolios. The purpose of VaR was to 
answer the question: how much can you lose? In this context, it 
has actually enjoyed tremendous success, ranging from revealing 
the hidden risks of complex strategies to communicating with 
investors in a consistent and transparent fashion.
    Second, VaR is a framework. It is not a prescriptive set of 
rules. As such, it has been implemented in many different ways 
across a wide variety of institutions. Criticisms of VaR that 
focus on the use of normal distributions or poor historical 
data must be taken in context. These issues are often the 
results of specific VaR implementations that may not have kept 
up with the best practices in the community.
    Third, most VaR methodologies utilize recent market data to 
estimate future short-term movements in order to allow risk 
managers to make proactive decisions based on rapidly changing 
market conditions. This is what VaR was designed to do. 
Research shows that these estimates are indeed quite robust, 
but they are not designed to predict long-term trends, and they 
are not designed to operate when the markets themselves stop 
functioning. Banks, on the other hand, must be protected 
against adverse long-term trends and in situations where the 
markets actually stop functioning. This, therefore, is not the 
domain of Value-at-Risk.
    So how do we tackle this problem? We start by noting that 
the current crisis is driven by two primary factors: one, the 
failure of market participants and of regulators to acknowledge 
and prepare for large negative long-term trends, such as a 
decline in home prices or buildup of leveraged credit, coupled 
with, two, the failure of many institutions to accurately and 
completely model how these negative long-term trends would 
actually affect their financial holdings. In this context, I am 
using the word ``model'' to mean a mathematical representation 
of a security or derivative that shows how its value is driven 
by one or more underlying market factors. Since both of these 
issues were quite well known for quite long periods of time, it 
is very hard to say that this crisis was unforeseeable, 
unknowable or a fat-tailed event.
    All market participants, including banks, must do a better 
job at modeling complex securities and in understanding how 
their strategies will fare under changing market conditions. 
For example, if the holders of mortgage-backed bonds would have 
known how sensitive these assets were to modest changes in 
default rates, they may not have purchased them in the first 
place. New rules, regulations and other types of policy changes 
regarding better disclosure in data must be done in order to 
address this critical issue.
    But it is banks and regulators who must specifically focus 
on preparing more for the negative long-term trends that lie 
ahead and less on trying to predict things with probabilities. 
Though current VaR methodologies are designed to estimate 
short-term market movements under normal market conditions, 
regulators nevertheless try to recast these models in order to 
measure the probability of long-term losses under extended 
market dislocations. We propose that it is not the model that 
needs to be recast, but that regulators need to recast the 
question itself.
    VaR is about making dynamic decisions, constructing 
portfolios, sizing bets and communicating risk. On the 
contrary, banking capital is more like an insurance policy 
designed to protect against worst-case events and their 
consequences. Instead of having banks report probabilities of 
short-term losses, banks should estimate the losses they would 
expect to sustain under a set of adverse conditions chosen by 
regulators. The question of `how much can you lose' is thus 
changed to `how much would you lose.'
    The conditions that banks are tested against should depend 
on what type of events policy-makers in the public interest 
believe that banks should be able to withstand. In this 
fashion, models, probabilities, simulations and predictions are 
left to those making ongoing risk-reward business decisions, 
whereas the minimum levels of capital needed to ensure a bank's 
survival are based on how regulators implement the broader 
requirements of policy-makers. Perhaps one bank needs to 
survive a 100-year flood whereas an orderly liquidation is all 
that is required for a different bank. Perhaps all banks should 
be able to weather a further ten percent downturn in housing 
prices, but no bank is required to survive a 50 percent default 
rate or a 40 percent unemployment rate--not because these 
events are highly improbable, but because policy-makers decide 
that this is too onerous a burden for a bank to bear.
    In summary, VaR is an excellent framework for active risk 
management by banks and other financial institutions and the 
development of risk models must continue unabated. But banking 
capital serves a different purpose, and a resetting of 
expectations will allow for the development of much better 
solutions driven by policy instead of by probability. Thank 
you.
    [The prepared statement of Dr. Berman follows:]
                 Prepared Statement of Gregg E. Berman
    I'd like to begin by thanking the Committee for this opportunity to 
present our thoughts on Value-at-Risk and banking capital in the 
context of the present financial crisis. My name is Gregg Berman and I 
am currently the head of the risk business at RiskMetrics Group, a 
provider of risk and corporate governance services to the financial 
community. I have been at RiskMetrics since its founding 11 years ago 
and in the last decade have worked with many of the world's largest 
financial institutions on the development of risk models, their use by 
hedge funds, asset managers, and banks.

SIMPLE ROOTS OF A COMPLEX CRISIS

    My comments today start with a rather bold assertion--the current 
crisis was not unpredictable, unforeseeable, or unknowable. In that 
sense I'm not sure it should be classified as a fat-tailed event. 
Rather, it was caused by the coupling of two fundamental problems, 
namely:

        1.  the inability of market participants to acknowledge and 
        prepare for the consequences of long-term trends, such as a 
        protracted downward spiral in home prices, or a leveraging of 
        the credit market through the use of CDS, and

        2.  the inability of market participants to recognize the 
        economic exposures they had to those trends through holdings 
        such as asset-backed securities and derivative contracts.

    The fact that these issues went unchecked for many years led 
directly to the creation of multiple, unsustainable market bubbles, 
which when burst propelled us downwards into a full-blown crisis.
    But if my assertion is correct and these events were foreseeable, 
then what does that imply about all the financial models and risk 
methodologies that were supposed to monitor and protect us from such a 
crisis? It is the answer to this question that I'd like to explore.

THE INEVITABILITY OF VALUE-AT-RISK

    In the early days of risk management size was used as a primary 
measure of risk. After all, intuition tells us that $10,000,000 in Ford 
bonds should be ten times riskier than $1,000,000 in Ford bonds. But 
soon the market realized that $10,000,000 of Ford bonds is probably 
riskier than a similar value of government bonds, but not as risky as 
$10,000,000 of Internet start-up equity.\1\
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    \1\ The matter is further complicated by derivative contracts that 
do not even have a well-defined measure of size. For example, what is 
the size of a contract that pays the holder $1,000 for each penny-
increase in the average spread throughout September between the price 
of natural gas for delivery in November and the price for delivery in 
January? Technically the answer is zero since the holder owns no 
natural gas, but the risk is certainly not zero.
---------------------------------------------------------------------------
    To address these issues practitioners switched from asking ``how 
large is your position'' to ``how much can you lose.'' But there is not 
just one answer to that question since for any given security differing 
amounts can be lost with different probabilities. One can estimate 
these probabilities by polling traders, by building econometric models, 
by relying on intuition, or by using variations of history to observe 
relevant patterns of past losses. Each of these methods has their own 
benefits and weaknesses. And unless we consider only one security at a 
time, it will also be necessary to make estimates of how the movements 
in each security are related to the movements of every other security 
in a given portfolio.
    These concepts are encapsulated by two well-known statistical 
terms: volatility and correlation. If one could measure the volatility 
and correlation of every security in a portfolio the question ``how 
much can you lose'' could be meaningfully addressed. This process is 
the basis of a popular risk methodology known as Value-at-Risk, or VaR.

HOW VaR IS COMPUTED AND HOW IT IS USED

    Because security valuations are often driven by underlying market 
factors, such as equity prices, spreads, interest rates, or housing 
prices, VaR is usually calculated in a two-step process that mimics 
this behavior. In the first step a model for the economic exposure of 
each security is created that links its value to one or more of 
underlying market factors. In the second step future trends of these 
underlying factors are simulated using volatilities, correlations, and 
other probabilistic methods. These two steps are then combined to 
create a curve that plots potential profits-and-losses against the 
probability of occurrence. For any given curve VaR is defined to be the 
amount that can be lost at a specific level of probability. It is a way 
of describing the entire profit-and-loss curve without having to list 
every data point.\2\
---------------------------------------------------------------------------
    \2\ Exhibit 1 on page 10 shows the potential one-day profit-and-
loss distribution of selling a short-term at-the-money put on the S&P 
500. Out of 5,000 trials we see that about 50 of them have losses of 
250 percent or worse. Thus VaR is 250 percent with a one percent 
probability. Alternatively we can ask for the worst five out of 5,000 
trials (a 0.1 percent probability) and observe these losses to be 400 
percent or worse.
---------------------------------------------------------------------------
    The accuracy of any VaR number depends on how well underlying 
markets have been simulated, and how well each security has been 
modeled. There unfortunately exists a tremendous variability in current 
practices and different financial institutions perform each step with 
varying levels of accuracy and diligence.\3\ Deficiencies in how VaR is 
implemented at a particular firm should not be confused with 
limitations of VaR itself.\4\
---------------------------------------------------------------------------
    \3\ The marketplace is rife with common fallacies about VaR due to 
poor implementations. When VaR first became popular in the mid-1990's 
computing power limited how accurately instruments, especially 
derivatives, could be modeled. Approximations that relied on the use of 
so-called normal distributions (bell-shaped curves) were often 
required. Also, the amount of market data that could be used, and the 
frequency at which this data was updated, was limited by technical and 
mathematical challenges resulting in further approximations. However, 
by the early part of this decade many of these challenges were overcome 
and today's simulation techniques do not rely on normal distributions 
and are not restricted by limited data. Unfortunately many institutions 
with older implementations still use somewhat outdated and approximate 
methods that do a poor job in estimating the risk of multi-asset, 
derivative-heavy portfolios.
    \4\ One fundamental criticism of VaR is that it can be ``gamed'' or 
manipulated since one number cannot by itself represent or reveal all 
possible ``tail-loss'' events. This is easily rectified by simply 
asking for VaR numbers at more than one level of probability, by 
computing the average of all losses comprising a tail event (often 
called conditional VaR or expected loss), or by examining the entire 
distribution of estimated future losses and their corresponding 
probabilities.
---------------------------------------------------------------------------
    But indeed there are limitations. When computed according to 
current best practices, VaR is most applicable for estimating short-
term market volatilities under ``normal'' market conditions. These 
techniques are based on over a decade of well-tested research 
demonstrating that in most circumstances recent market movements are 
indeed a good predictor of future short-term volatility. VaR models 
have seen tremendous success in a wide range of applications including 
portfolio construction, multi-asset-class aggregation, revealing 
unexpected bets, investor communication, the extension of margin, and 
general transparency.
    As such, VaR has become an essential part of risk management, and 
when properly integrated into an overall investment process it provides 
an excellent framework for deploying capital in areas that properly 
balance risk and reward.

VAR AND BANKING CAPITAL

    So why did this not foretell the current crisis? First and 
foremost, many institutions and market participants did not perform 
step one correctly--they failed to correctly model how their securities 
would behave under changing market conditions. This failure is one of 
the leading causes of current crisis.\5\
---------------------------------------------------------------------------
    \5\ Many institutions and market participants did not recognize nor 
understand how their portfolios and strategies would be affected by a 
fall in housing prices or a widening of credit spreads. Regulators had 
even less information on these effects and almost no information on how 
they were linked across institutions.

It could be argued that if investors had understood the nature of the 
mortgage-backed products they had purchased, many would not have 
purchased them in the first place (which would have significantly 
curtailed the formation of the bubble itself). If regulators had 
understood how CDS contracts inherently lever the credit markets they 
may not have allowed their unbridled expansion. And if insurance 
companies understood that changes in the mark-to-market values of their 
derivative contracts would require the posting of collateral to their 
counter-parties many would not have entered into those deals. None of 
these decisions involve predicting the future or modeling fat tails. 
They do involve understanding the present, spending time on the details 
---------------------------------------------------------------------------
of financial instruments, and being incented to care about their risk.

Tackling these significant shortcomings may require new regulations 
regarding data availability, disclosure, and the analytical 
capabilities of each market participant. Central oversight of the 
markets themselves will be needed to monitor, and sometimes even limit, 
actions that could trigger systemic risk and future liquidity crises.
    The second issue is where banking capital comes in. Recall that our 
crisis stems from long-term trends, not short-term volatility. And as 
mentioned, most of today's VaR techniques are only applicable for 
estimating potential short-term movements in well-functioning 
markets.\6\ But it is long-term trends and non-functioning markets that 
are the concerns of banking capital.
---------------------------------------------------------------------------
    \6\ There is nothing endemic to VaR that limits its applicability 
to short-term estimates or functioning markets. However, current 
methodologies are optimized for those conditions and this is where most 
parameters have been tested for proper use. Research into new models 
that lengthen the prediction horizon and include factors like liquidity 
to account for non-functioning markets is underway. As development of 
these methodologies progresses we may see the domain of VaR extended 
into more areas of risk.
---------------------------------------------------------------------------
    Nevertheless regulators rely on VaR as the basis for many bank 
capital calculations.\7\ And even today they continue to recast VaR-
like models in order to address VaR's perceived shortcomings.\8\ We 
propose that it is not the model that needs to be recast but rather the 
question that regulators want the model to address.
---------------------------------------------------------------------------
    \7\ One technique employed to ``fix'' the short-term aspect of VaR 
models is to utilize long-term historical data as the basis for 
``better'' future estimates. This is a very common but dangerous 
practice since it both invalidates any estimates of short-term 
volatility (preventing proper use by risk managers trying to be 
reactive to rapid changes to the market) and it doesn't actually 
provide any better estimates of long-term trends. For a complete 
discussion on this and other related topics see included reference by 
Christopher Finger (RiskMetrics Research Monthly--April 2009) and 
references therein (including a March 2008 report issued by the Senior 
Supervisors Group on their study of how risk was implemented at a 
variety of large banks).
    \8\ See included reference by Christopher Finger (RiskMetrics 
Research Monthly--February 2009) containing our comments on the Basel 
committee's proposed Incremental Risk Charge--an extension that uses 
VaR for additional types of capital charges.

POLICY-BASED BANKING CAPITAL

    We believe that the foundation of banking capital is rooted in the 
following two questions:

        1)  What are the adverse events that consumers, banks, and the 
        financial system as a whole, need to be protected against?

        2)  What is required from our banks when those events occur?

    This is not the domain of VaR. On the contrary, banking capital is 
more like an insurance policy designed to protect against worst-case 
events and their consequences. Instead of having banks report 
probabilities of short-term losses, banks should estimate the losses 
they would expect to sustain under a set of adverse conditions chosen 
by regulators. The question of ``how much can you lose'' is thus 
changed to ``how much would you lose.''
    The conditions that banks are tested against should depend on what 
types of events policy-makers decide that, in the public interest, 
banks should be able to withstand. In this fashion models, 
probabilities, simulations, and predictions are left to those making 
ongoing risk-reward business decisions whereas the minimum levels of 
capital needed to ensure a bank's survival are based on how regulators 
implement the broader requirements of policy-makers. Perhaps one bank 
needs to survive a hundred-year flood whereas an orderly liquidation is 
all that is required for a different bank. Perhaps all banks should be 
able to weather a further 10 percent downturn in housing prices, but no 
bank is required to survive a 50 percent default rate or a 40 percent 
unemployment rate--not because these are highly improbable, but because 
policy-makers decide that this is too onerous a burden to expect a bank 
to bear.\9\
---------------------------------------------------------------------------
    \9\ The recent stress-tests conducted on banks by the Federal 
Reserve is an excellent example of how policy, as opposed to 
probability, can help set capital requirements. This should not 
diminish the role of simulations and the use of models to explore 
possibilities and uncover unexpected relationships, but this should be 
a guide of what the future may bring, not a prediction of what it will 
(or will not) bring.
---------------------------------------------------------------------------
    To summarize, we believe that key differences between the needs of 
risk management and banking capital suggest different solutions are 
required. And in doing so each field can separately develop to meet the 
ever-expanding array of challenges we face today.


































                     Biography for Gregg E. Berman

    Gregg E. Berman, 43, is currently head of RiskMetrics Risk Business 
covering institutional and wealth management offerings that serve Hedge 
Funds, Asset Managers, Prime Brokers, Banks, Financial Advisors, 
Insurance Companies, and Corporates. Mr. Berman joined RiskMetrics as a 
founding member during the time of its spin-off from J.P. Morgan in 
1998 and has held a number of roles from research to head of product 
management, market risk, and of business management.
    Prior to joining RiskMetrics Group, Mr. Berman co-managed a number 
of multi-asset Hedge Funds within New York-based ED&F Man. His start in 
the Hedge Fund space began in 1993, researching and developing multi-
asset trading strategies as part of Mint Investment Management 
Corporation, a $1bn CTA based in New Jersey.
    Mr. Berman is a physicist by training and holds degrees from 
Princeton University (Ph.D. 1994, M.S. 1989), and the Massachusetts 
Institute of Technology (B.S. 1987).

    Chairman Miller. Thank you, Dr. Berman.
    Mr. Rickards for five minutes.

 STATEMENT OF MR. JAMES G. RICKARDS, SENIOR MANAGING DIRECTOR 
        FOR MARKET INTELLIGENCE, OMNIS, INC., MCLEAN, VA

    Mr. Rickards. Mr. Chairman, my name is James Rickards and I 
appreciate the opportunity to speak to you on a subject of the 
utmost importance to global capital markets.
    The world is two years into the worst financial crisis 
since the Great Depression. The list of culprits is long, 
including mortgage brokers, investment bankers and rating 
agencies. The story sadly is, by now, well known. What is less 
well known is that behind these actors were quantitative risk 
models which said that all was well even as the bus was driving 
over a cliff.
    Unfortunately, we have been here before. In 1998, capital 
markets came to the brink of collapse due to the failure of a 
hedge fund, Long-Term Capital Management. The amounts involved 
seem small compared to today's catastrophe. However, it did not 
seem that way at the time. I know, I was general counsel of 
LTCM. What is most striking to me now as I look back is how 
nothing has changed and how no lessons were learned. The 
lessons should have been obvious. LTCM used fatally flawed VaR 
models, too much leverage, and the solutions were clear. Risk 
models needed to be changed or abandoned, leverage needed to be 
reduced, and regulatory oversight needed to be increased.
    Amazingly, the United States Government did the opposite. 
They repealed Glass-Steagall in 1999 and allowed banks to act 
like hedge funds. The Commodity Futures Modernization Act of 
2000 allowed more unregulated derivatives. SEC regulations in 
2004 allowed increased leverage. It was as if the United States 
had looked at the catastrophe of LTCM and decided to double 
down. None of this would have happened without the assurance 
and comfort provided to regulators and Wall Street by VaR 
models. But all models are based on assumptions. If the 
assumptions are flawed, no amount of mathematics will 
compensate. Therefore, the root of our inquiry into VaR should 
be an examination of the assumptions behind the models.
    The key assumptions are the following: one, the efficient 
market hypothesis, which assumes that investors behave 
rationally; two, the random walk, which assumes that no 
investor can beat the market consistently, because future 
prices are independent of the past; three, normally distributed 
risk. This says that since future price movements are random, 
the relationship of the frequency and the severity of the 
events will also be random, like a coin toss or roll of the 
dice. The random distribution is represented as a bell curve. 
Value-at-Risk would be a fine methodology but for the fact that 
all three of these assumptions are wrong. Markets are not 
efficient, future prices are not independent of the past, risk 
is not normally distributed. As the saying goes, ``Besides 
that, Mrs. Lincoln, how was the play?''
    Behavioral economics has done a masterful job of showing 
that investors do not behave rationally and are guided by 
emotion. Similarly, prices do not move randomly but are 
dependent on past prices. In effect, news may be ignored for 
sustained periods of time until a kind of tipping point is 
achieved, at which point investors will react en masse. The 
normal distribution of risk has been known to be false since 
the early 1960s, when studies showed price distributions to be 
shaped in what is known as a power curve. A power curve has 
fewer low-impact events than the bell curve but has far more 
high-impact events. In short, a power curve corresponds to 
market reality while a bell curve does not.
    Power curves have low predictability but can offer other 
valuable insights. One lesson is that as you increase the scale 
of the system, the size of the largest possible catastrophe 
grows exponentially. An example will illustrate the 
relationship between the scale of the system and the greatest 
catastrophe possible. Imagine a vessel with a large hold 
divided into three sections, separated by watertight bulkheads. 
If a hole is punched in one section and that section fills with 
water, the vessel will still float. Now imagine the bulkheads 
are removed and the same hole is punched into the vessel. The 
entire hold will fill with water and the vessel will sink. In 
this example, the hold can be thought of as the system. The 
sinking of the vessel represents the catastrophic failure of 
the system. When the bulkheads are in place, we have three 
small systems. When the bulkheads are removed, we have one 
large system. By removing the bulkheads, we increase the scale 
of the system by a factor of three, but the likelihood of 
failure did not increase by a factor of three. It went from 
practically zero to practically 100 percent. The system size 
tripled, but the risk of sinking went up exponentially.
    If scale is the primary determinant of risk in complex 
systems, it follows that descaling is the most effective way to 
manage risk. This does not mean that the totality of the system 
needs to shrink--merely that it be divided into subcomponents 
with limited interaction. This has the same effect as 
installing the watertight bulkheads referred to above. In this 
manner, severe financial distress in one sector does not result 
in contagion among all sectors.
    This descaling can be accomplished with three reforms: 
number one, the enactment of a modernized version of Glass-
Steagall with a separation between bank deposit taking on the 
one hand, and market risk on the other; two, strict 
requirements for all derivative products to be traded on 
exchanges subject to margin position limits, price transparency 
and netting; three, higher regulatory capital requirements and 
reduced leverage for banks and brokers. Traditional ratios of 
eight to one for banks and 15 to one for brokers seem adequate, 
provided off-balance sheet positions are included.
    Let us abandon VaR and the bell curve once and for all and 
accelerate empirical research into the actual metrics of event 
distributions. Even if predictive value is low, there is value 
in knowing the limits of our knowledge. Understanding the way 
risk metastasizes with scale might be lesson enough. It would 
offer a proper dose of humility to those trying to supersize 
banks and regulators.
    Thank you for this opportunity to testify.
    [The prepared statement of Mr. Rickards follows:]

                Prepared Statement of James G. Rickards

                    The Risks of Financial Modeling:

                     VaR and the Economic Meltdown

Introduction

    Mr. Chairman, Mr. Ranking Member and Members of this subcommittee, 
my name is James Rickards, and I want to extend my deep appreciation 
for the opportunity and the high honor to speak to you today on a 
subject of the utmost importance in the management of global capital 
markets and the global banking system. The Subcommittee on 
Investigations and Oversight has a long and distinguished history of 
examining technology and environmental matters which affect the health 
and well-being of Americans. Today our financial health is in jeopardy 
and I sincerely applaud your efforts to examine the flaws and misuse in 
financial modeling which have contributed to the impairment of the 
financial health of our citizens and the country as a whole.
    As a brief biographical note, I am an economist, lawyer and author 
and currently work at Omnis, Inc. in McLean, VA where I specialize in 
the field of threat finance and market intelligence. My colleagues and 
I provide expert analysis of global capital markets to members of the 
national security community including military, intelligence and 
diplomatic directorates. My writings and research have appeared in 
numerous journals and I am an Op-Ed contributor to the Washington Post 
and New York Times and a frequent commentator on CNBC, CNN, Fox and 
Bloomberg. I was formerly General Counsel of Long-Term Capital 
Management, the hedge fund at the center of the 1998 financial crisis, 
where I was principal negotiator of the Wall Street rescue plan 
sponsored by the Federal Reserve Bank of New York.

Summary: The Problem with VaR

    The world is now two years into the worst financial crisis since 
the Great Depression. The IMF has estimated that the total lost wealth 
in this crisis so far exceeds $60 Trillion dollars, more than the cost 
of all of the wars of the 20th century combined. The list of causes and 
culprits is long including mortgage brokers making loans borrowers 
could not afford, investment bankers selling securities while 
anticipating their default, rating agencies granting triple-A ratings 
to bonds which soon suffered catastrophic losses, managers and traders 
focused on short-term profits and bonuses at the expense of their 
institutions, regulators acting complacently in the face of growing 
leverage and imprudence and consumers spending and borrowing at non-
sustainable rates based on a housing bubble which was certain to burst 
at some point. This story, sadly, is by now well known.
    What is less well-known is that behind all of these phenomena were 
quantitative risk management models which told interested parties that 
all was well even as the bus was driving over a cliff. Mortgage brokers 
could not have made unscrupulous loans unless Wall Street was willing 
to buy them. Wall Street would not have bought the loans unless they 
could package them into securities which their risk models told them 
had a low risk of loss. Investors would not have bought the securities 
unless they had triple-A ratings. The rating agencies would not have 
given those ratings unless their models told them the securities were 
almost certain to perform as expected. Transaction volumes would not 
have reached the levels they did without leverage in financial 
institutions. Regulators would not have approved that leverage unless 
they had confidence in the risk models being used by the regulated 
entities. In short, the entire financial edifice, from borrower to 
broker to banker to investor to rating agency to regulator, was 
supported by a belief in the power and accuracy of quantitative 
financial risk models. Therefore an investigation into the origins, 
accuracy and performance of those models is not ancillary to the 
financial crisis; it is not a footnote; it is the heart of the matter. 
Nothing is more important to our understanding of this crisis and 
nothing is more important to the task of avoiding a recurrence of the 
crisis we are still living through.
    Unfortunately, we have been here before. In 1998, western capital 
markets came to the brink of collapse, owing to the failure of a hedge 
fund, Long-Term Capital Management, and a trillion dollar web of 
counter-party risk with all of the major banks and brokers at that 
time. Then Fed Chairman Alan Greenspan and Treasury Secretary Robert 
Rubin called it the worst financial crisis in over 50 years. The 
amounts involved and the duration of the crisis both seem small 
compared to today's catastrophe, however, it did not seem that way at 
the time. Capital markets really did teeter on the brink of collapse; I 
know, I was there. As General Counsel of Long-Term Capital Management, 
I negotiated the bail out which averted an even greater disaster at 
that time. What is most striking to me now as I look back is how 
nothing changed and how no lessons were applied.
    The lessons were obvious at the time. LTCM had used fatally flawed 
VaR risk models. LTCM had used too much leverage. LTCM had transacted 
in unregulated over-the-counter derivatives instead of exchange traded 
derivatives. The solutions were obvious. Risk models needed to be 
changed or abandoned. Leverage needed to be reduced. Derivatives needed 
to be moved to exchanges and clearinghouses. Regulatory oversight 
needed to be increased.
    Amazingly the United States Government did the opposite. The repeal 
of Glass-Steagall in 1999 allowed banks to act like hedge funds. The 
Commodities Futures Modernization Act of 2000 allowed more unregulated 
derivatives. The Basle II accords and SEC regulations in 2004 allowed 
increased leverage. It was as if the United States had looked at the 
near catastrophe of LTCM and decided to double-down.
    What reason can we offer to explain this all-in approach to 
financial risk? Certainly the power of Wall Street lobbyists and 
special interests cannot be discounted. Alan Greenspan played a large 
role through his belief that markets could self-regulate through the 
intermediation of bank credit. In fairness, he was not alone in this 
belief. But none of this could have prevailed in the aftermath of the 
1998 collapse without the assurance and comfort provided by 
quantitative risk models. These models, especially Value-at-Risk, cast 
a hypnotic spell, as science often does, and assured bankers, investors 
and regulators that all was well even as the ashes of LTCM were still 
burning.
    What are these models? What is the attraction that allows so much 
faith to be placed in them? And what are the flaws which lead to 
financial collapse time and time again?
    The term ``Value-at-Risk'' or VaR is used in two senses. One 
meaning refers to the assumptions, models and equations which 
constitute the risk management systems most widely used in large 
financial institutions today. The other meaning refers to the output of 
those systems, as in, ``our VaR today is $200 million'' which refers to 
the maximum amount the institution is expected to lose in a single day 
within some range of probability or certainty usually expressed at the 
99 percent level. For purposes of this testimony, we will focus on VaR 
in the first sense. If the models are well founded then the output 
should be of some value. If not, then the output will be unreliable. 
Therefore the proper focus of our inquiry should be on the soundness of 
the models themselves.
    Furthermore, any risk management system is only as good as the 
assumptions behind it. It seems fair to conclude that based on a 
certain set of assumptions, the quantitative analysts and computer 
developers are able within reason to express those assumptions in 
equations and to program the equations as computer code. In other 
words, if the assumptions are correct then it follows that the model 
development and the output should be reasonably correct and useful as 
well. Conversely, if the assumptions are flawed then no amount of 
mathematical equation writing and computer development will compensate 
for this deficiency and the output will always be misleading or worse. 
Therefore, the root of our inquiry into models should be an examination 
of the assumptions behind the models.
    In broad terms, the key assumptions are the following:

The Efficient Market Hypothesis (EMH): This assumes that investors and 
market participants behave rationally from the perspective of wealth 
maximization and will respond in a rational manner to a variety of 
inputs including price signals and news. It also assumes that markets 
efficiently price in all inputs in real time and that prices move 
continuously and smoothly from one level to another based on these new 
inputs.

The Random Walk: This is a corollary to EMH and assumes that since 
markets efficiently price in all information, no investor can beat the 
market consistently because any information which an investor might 
rely on to make an investment decision is already reflected in the 
current market price. This means than future market prices are 
independent of past market prices and will be based solely on future 
events that are essentially unknowable and therefore random.

Normally Distributed Risk: This is also a corollary to EMH and says 
that since future price movements are random, their degree distribution 
(i.e., relationship of frequency to severity of events) will also be 
random like a coin toss or roll of the dice. This random or normal 
degree distribution is also referred to as Gaussian and is most 
frequently represented as a bell curve in which the large majority of 
outcomes are bunched in a region of low severity with progressively 
fewer outcomes shown in the high severity region. Because the curve 
tails off steeply, highly extreme events are so rare as to be almost 
impossible.
    Value-at-Risk would be a fine methodology but for the fact that all 
three of these assumptions are wrong. Markets are not efficient. Future 
prices are not independent of the past. Risk is not normally 
distributed. As the saying goes, ``Besides that, Mrs. Lincoln, how was 
the play?'' Let's take these points separately.
    Behavioral economics has done a masterful job of showing 
experimentally and empirically that investors do not behave rationally 
and that markets are not rational but are prone to severe shocks or 
mood swings. Examples are numerous but some of the best known are risk 
aversion (i.e., investors put more weight on avoiding risk than seeking 
gains), herd mentality (i.e., investors buy stocks when others are 
buying and sell when others are selling leading to persistent losses) 
and various seasonal effects. Prices do not smoothly and continuously 
move from one price level to the next but have a tendency to gap up or 
down in violent thrusts depriving investors of the chance to get out 
before large losses are incurred.
    Similarly, prices to not move randomly but are highly dependent on 
past price movements. In effect, relevant news will be discounted or 
ignored for sustained periods of time until a kind of tipping point is 
achieved at which point investors will react en masse to what is mostly 
old news mainly because other investors are doing likewise. This is why 
markets exhibit periods of low and high volatility in succession, why 
markets tend to overshoot in response to fundamental news and why 
investors can profit consistently by momentum trading which exploits an 
understanding of these dynamics.
    Finally, the normal distribution of risk has been known to be false 
at least since the early 1960's when published studies of time series 
of prices showed price distributions to be shaped in what is known as a 
power curve rather than a bell curve. This has been borne out by many 
studies since. A power curve has fewer low impact events than the bell 
curve but has far more high impact events. This corresponds exactly to 
the actual market behavior we have seen including frequent extreme 
events such as the stock market crash of 1987, the Russian-LTCM 
collapse of 1998, the dot corn bubble collapse of 2000 and the housing 
collapse of 2007. Statistically these events should happen once every 
1,000 years or so in a bell curve distribution but are expected with 
much greater frequency in a power curve distribution. In short, a power 
curve corresponds to market reality while a bell curve does not.
    How is it possible that our entire financial system has come to the 
point that it is risk managed by a completely incorrect system?
    The Nobelist, Daniel Kahneman, tells the story of a Swiss Army 
patrol lost in the Alps in a blizzard for days. Finally the patrol 
stumbles into camp, frostbitten but still alive. The Commander asks how 
they survived and the patrol leader replies, ``We had a map.'' The 
Commander looks at the map and says, ``This is a map of the Pyrenees; 
you were in the Alps.'' ``Yes,'' comes the reply; ``but we had a map.'' 
The point is that sometimes bad guidance is better than no guidance; it 
gives you confidence and an ability to function even though your system 
is flawed.
    So it is with risk management on Wall Street. The current system, 
based on the idea that risk is distributed in the shape of a bell 
curve, is flawed and practitioners know it. Practitioners treat extreme 
events as outliers and develop mathematical fixes. They call extreme 
events fat tails and model them separately from the rest of the bell 
curve. They use stress tests to gauge the impact of extreme events. The 
problem is they never abandon the bell curve. They are like medieval 
astronomers who believe the sun revolves around the earth and are 
furiously tweaking their geocentric math in the face of contrary 
evidence. They will never get this right; they need their Copernicus.
    But the right map exists. It's called a power curve. It says that 
events of any size can happen and extreme events happen more frequently 
than the bell curve predicts. There is no need to treat fat tails as a 
special case; they occur naturally on power curves. And power curves 
are well understood by scientists because they apply to extreme events 
in many natural and man-made systems from power outages to earthquakes.
    Power curve analysis is not new. The economist, Vilfredo Pareto, 
observed in 1906 that wealth distributions in every society conform to 
a power curve; in effect, there is one Bill Gates for every 100 million 
average Americans. Benoit Mandelbrot pioneered empirical analysis in 
the 1960's that showed market prices move in power curve patterns.
    So why have we gone down the wrong path of random walks and normal 
distributions for the past 50 years? The history of science is filled 
with false paradigms that gained followers to the detriment of better 
science. People really did believe the sun revolved around the earth 
for 2,000 years and mathematicians had the equations to prove it. The 
sociologist, Robert K. Merton, called this the Matthew Effect from a 
New Testament verse that says, ``For to those who have, more will be 
given . . .'' The idea is that once an intellectual concept attracts a 
critical mass of supporters it becomes entrenched while other concepts 
are crowded out of the marketplace of ideas.
    Another reason is that practitioners of bell curve science became 
infatuated with the elegance of their mathematical solutions. The 
Black-Scholes options formula is based on bell curve type price 
movements. The derivatives market is based on variations of Black-
Scholes. Wall Street has decided that the wrong map is better than no 
map at all--as long as the math is neat.
    Why haven't scientists done more work in applying power curves to 
capital markets? Some excellent research has been done. But one answer 
is that power curves have low predictive value. Researchers approach 
this field to gain an edge in trading and once the edge fails to 
materialize they move on. But the Richter Scale, a classic power curve, 
also has low predictive value. That does not make earthquake science 
worthless. We know that 8.0 earthquakes are possible and we build 
cities accordingly even if we cannot know when the big one will strike.
    We can use power curve analysis to make our financial system more 
robust even if we cannot predict financial earthquakes. One lesson of 
power curves is that as you increase the scale of the system, the risk 
of a mega-earthquake goes up exponentially. If you increase the value 
of derivatives by a factor of 10, you may be increasing risk by a 
factor of 10,000 without even knowing it. This is not something that 
Wall Street or Washington currently comprehend.
    Let's abandon the bell curve once and for all and accelerate 
empirical research into the proper risk metrics of event distributions. 
Even if predictive value is low, there is value in knowing the limits 
of our knowledge. Understanding the way risk metastasizes with scale 
might be lesson enough. It would offer a proper dose of humility to 
those trying to supersize banks and regulators.

Detailed Analysis--History of VaR Failures

    The empirical failures of the Efficient Market Hypothesis and VaR 
are well known. Consider the October 19, 1987 stock market crash in 
which the market fell 22.6 percent in one day; the December 1994 
Tequila Crisis in which the Mexican Peso fell 85 percent in one week; 
the September 1998 Russian-LTCM crisis in which capital markets almost 
ceased to function; the March 2000 dot corn collapse during which the 
NASDAQ fell 80 percent over 30 months, and the 9-11 attacks in which 
the NYSE first closed and then fell 14.3 percent in the week following 
its reopening. Of course, to this list of extreme events must now be 
added the financial crisis that began in July 2007. Events of this 
extreme magnitude should, according to VaR, either not happen at all 
because diversification will cause certain risks to cancel out and 
because rational buyers will seek bargains once valuations deviate 
beyond a certain magnitude, or happen perhaps once every 1,000 years 
(because standard deviations of this degree lie extremely close to the 
x-axis on the bell curve which corresponds to a value close to zero on 
the y-axis, i.e., an extremely low frequency event). The fact that all 
of these extreme events took place in just over 20 years is completely 
at odds with the predictions of VaR in a normally distributed paradigm.
    Practitioners treated these observations not as fatal flaws in VaR 
but rather as anomalies to be explained away within the framework of 
the paradigm. Thus was born the ``fat tail'' which is applied as an 
embellishment on the bell curve such that after approaching the x-axis 
(i.e., the extreme low frequency region), the curve flattens to 
intersect data points representing a cluster of highly extreme but not 
so highly rare events. No explanation is given for what causes such 
events; it is simply a matter of fitting the curve to the data (or 
ignoring the data) and moving on without disturbing the paradigm. This 
process of pinning a fat tail on the bell curve reached its apotheosis 
in the invention of generalized auto-regressive conditional 
heteroskedasicity or GARCH and its ilk, which are analytical techniques 
for modeling the section of the degree distribution curve containing 
the extreme events as a separate case and feeding the results of this 
modeling into a modified version of the curve. A better approach would 
have been to ask the question: if a normal distribution has a fat tail, 
is it really a normal distribution?
    A Gaussian distribution is not the only possible degree 
distribution. One of the most common distributions in nature, which 
accurately describes many phenomena, is the power curve which shows 
that the severity of an event is inversely proportional to its 
frequency with the proportionality expressed as an exponent. When 
graphed on a double logarithmic scale, the power law describing 
financial markets risk is a straight line sloping downward from left to 
right; the negative exponent is the slope of the line.
    This difference is not merely academic. Gaussian and power curve 
distributions describe two entirely different phenomena. Power curves 
accurately describe a class of phenomena known as nonlinear dynamical 
systems which exhibit scale invariance, i.e., patterns are repeated at 
all scales.
    The field of nonlinear dynamical systems was enriched in the 1990s 
by the concept of self-organized criticality. The idea is that actions 
propagate throughout systems in a critical chain reaction. In the 
critical state, the probability that an action will propagate is 
roughly balanced by the probability that the original action will 
dissipate. In the subcritical state, the probability of extensive 
effects from the initial action is low. In the super-critical state, a 
single minor action can lead to a catastrophic collapse. Such states 
have long been observed in physical systems, e.g., nuclear chain 
reactions in uranium piles, where a small amount of uranium is 
relatively harmless (subcritical) and larger amounts can either be 
carefully controlled to produce desired energy (critical), or can be 
shaped to produce atomic explosions (supercritical).
    The theory of financial markets existing in a critical state cannot 
be tested in a laboratory or particle accelerator in the same fashion 
as theories of atomic physics. Instead, the conclusion that financial 
markets are a nonlinear critical state system rests on two non-
experimental bases; one deductive, one inductive. The deductive basis 
is the ubiquity of power curves as a description of the behavior of a 
wide variety of complex systems in natural and social sciences, e.g., 
earthquakes, forest fires, sunspots, polarity, drought, epidemiology, 
population dynamics, size of cities, wealth distribution, etc. This is 
all part of a more general movement in many natural and social sciences 
from 19th and early 20th century equilibrium models to non-equilibrium 
models; this trend has now caught up with financial economics.
    The inductive basis is the large variety of capital markets 
behavior which has been empirically observed to fit well with the 
nonlinear paradigm. It is certainly more robust than VaR when it comes 
to explaining the extreme market movements described above. It is 
consistent with the fact that extreme events are not necessarily 
attributable to extreme causes but may arise spontaneously in the same 
initial conditions from routine causes.
    While extreme events occur with much greater than normal frequency 
in nonlinear critical state systems, these events are nevertheless 
limited by the scale of the system itself. If the financial system is a 
self-organized critical system, as both empirical evidence and 
deductive logic strongly suggest, the single most important question 
from a risk management perspective is: what is the scale of the system? 
Simply put, the larger the scale of the system, the greater the 
potential collapse with correlative macroeconomic and other real world 
effects.
    The news on this front is daunting. There is no normalized scale 
similar to the Richter Scale for measuring the size of markets or the 
size of disruptive events that occur within them, however, a few 
examples will make the point. According to recent estimates prepared by 
the McKinsey Global Institute, the ratio of world financial assets to 
world GDP grew from 100 percent in 1980 to 200 percent in 1993 to 316 
percent in 2005. Over the same period, the absolute level of global 
financial assets increased from $12 trillion to $140 trillion. The 
drivers of this exponential increase in scale are globalization, 
derivative products, and leverage.
    Globalization in this context is the integration of capital markets 
across national boundaries. Until recently there were specific laws and 
practices that had the effect of fragmenting capital markets into local 
or national venues with little interaction. Factors included 
withholding taxes, capital controls, protectionism, non-convertible 
currencies, licensing, regulatory and other restrictions that tilted 
the playing field in favor of local champions and elites. All of these 
impediments have been removed over the past 20 years to the point that 
the largest stock exchanges in the United States and Europe (NYSE and 
Euronext) now operate as a single entity.
    Derivative products have exhibited even faster growth than the 
growth in underlying financial assets. This stems from improved 
technology in the structuring, pricing, and trading of such instruments 
and the fact that the size of the derivatives market is not limited by 
the physical supply of any stock or commodity but may theoretically 
achieve any size since the underlying instrument is notional rather 
than actual. The total notional value of all swaps increased from $106 
trillion to $531 trillion between 2002 and 2006. The notional value of 
equity derivatives increased from $2.5 trillion to $11.9 trillion over 
the same period while the notional value of credit default swaps 
increased from $2.2 trillion to $54.6 trillion.
    Leverage is the third element supporting the massive scaling of 
financial markets; margin debt of U.S. brokerage firms more than 
doubled from $134.58 billion to $293.2 billion from 2002 to 2007 while 
the amount of total assets per dollar of equity at major U.S. brokerage 
firms increased from approximately $20 to $26 in the same period. In 
addition, leveraged investors invest in other entities which use 
leverage to make still further investments. This type of layered 
leverage is impossible to unwind in a panic.
    There can be no doubt that capital markets are larger and more 
complex than ever before. In a dynamically complex critical system, 
this means that the size of the maximum possible catastrophe is 
exponentially greater than ever. Recalling that systems described by a 
power curve allow events of all sizes and that such events can occur at 
any time, particularly when the system is super-critical, the 
conclusion is inescapable that progressively greater financial 
catastrophes of the type we are experiencing today should be expected 
frequently.
    The more advanced risk practitioners have long recognized the 
shortcomings of using VaR in a normally distributed paradigm to compute 
risk measured in standard deviations from the norm. This is why they 
have added stress testing as an alternative or blended factor in their 
models. Such stress testing rests on historically extreme events such 
as the market reaction to 9-11 or the stock market crash of 1987. 
However, this methodology has its own flaws since the worst outcomes in 
a dynamically complex critical State system are not bounded by history 
but are only bounded by the scale of the system itself. Since the 
system is larger than ever, there is nothing in historical experience 
that provides a guide to the size of the largest catastrophe that can 
arise today. The fact that the financial crisis which began in July 
2007 has lasted longer, caused greater losses and been more widespread 
both geographically and sectorally than most analysts predicted or can 
explain is because of the vastly greater scale of the financial system 
which produces an exponentially greater catastrophe than has ever 
occurred before. This is why the past is not a guide and why the 
current crisis may be expected to produce results as severe as the 
Great Depression of 1929-1941.

Policy Approaches and Recommendations

    A clear understanding of the structures and vulnerabilities of the 
financial markets points the way to solutions and policy 
recommendations. These recommendations fall into the categories of 
limiting scale, controlling cascades, and securing informational 
advantage.
    To explain the concept of limiting scale, a simple example will 
suffice. If the U.S. power grid east of the Mississippi River were at 
no point connected to the power grid west of the Mississippi River, a 
nationwide power failure would be an extremely low probability event. 
Either the ``east system'' or the ``west system'' could fail 
catastrophically in a cascading manner but both systems could not fail 
simultaneously except for entirely independent reasons because there 
are no nodes in common to facilitate propagation across systems. In a 
financial context, governments should give consideration to preventing 
mergers that lead to globalized stock and bond exchanges and universal 
banks. The first order efficiencies of such mergers are outweighed by 
the risks of large-scale failure especially if those risks are not 
properly understood and taken into account.
    Another example will help to illustrate the relationship between 
the scale of a system and extent of the greatest catastrophe possible 
in that system. Imagine a vessel with a large hold. The hold is divided 
into three equal sections separated by watertight bulkheads. If a hole 
is punched in one section and that section is completely filled with 
water, the vessel will still float. Now imagine the watertight 
bulkheads are removed and the same hole is punched into the vessel. In 
this case, the entire hold will fill with water and the vessel will 
sink. In this example, the area of the hold can be thought of as the 
relevant dynamic system. The sinking of the vessel represents the 
catastrophic failure of the system. When the bulkheads are in place we 
have three small systems. When the bulkheads are removed we have one 
large system. By removing the bulkheads we increased the scale of the 
system by a factor of three. But the likelihood of failure did not 
increase by a factor of three; it went from practically zero to 
practically 100 percent. The system size tripled but the risk of 
sinking went up exponentially. By removing the bulkheads we created 
what engineers call a ``single point of failure,'' i.e., one hole is 
now enough to sink the entire vessel.
    Something similar happened to our financial system between 1999 and 
2004. This began with the repeal of Glass-Steagall in 1999 which can be 
thought of as removing the watertight bulkheads separating commercial 
banks and investment banks. This was exacerbated by the Commodities 
Futures Modernization Act of 2000 which removed the prohibition on many 
kinds of derivatives. This allowed banks to increase the scale of the 
system through off-balance sheet transactions. Finally, in 2004, the 
SEC amended the broker-dealer net capital rule in such a way that 
allowed brokers to go well-beyond the traditional 15:1 leverage ratio 
and to use leverage of 30:1 or more. All three of these events 
increased the scale of the system by allowing regulated financial 
institutions to enter new markets, trade new products and use increased 
leverage. Using a power curve analysis, we see that while the scale of 
the system was increased in a linear way (by a factor of three, five, 
ten or fifty depending on the product) the risk was increasing in a 
nonlinear way (by a factor of 100, 1000, or 10,000 depending on the 
slope of the power curve). VaR models based on normal distributions 
were reporting that risk was under control and sounding the all clear 
signal because so much of the risk was offsetting or seen to cancel out 
in the models. However, a power curve model would have been flashing a 
red alert sign because it does not depend on correlations, instead it 
sees risk as an emergent property and an exponential function of scale.
    The fact that government opened the door to instability does not 
necessarily mean that the private sector had to rush through the door 
to embrace the brave new world of leveraged risk. For that we needed 
VaR. Without VaR models to tell bankers that risk was under control, 
managers would not have taken so much risk even if government rules 
allowed them to do so. Self-interest would have constrained them 
somewhat as Greenspan expected. But with VaR models telling senior 
management that risk was contained the new government rules became an 
open invitation to pile on massive amounts of risk which bankers 
promptly did.
    Our financial system was relatively stable from 1934-1999 despite 
occasional failures of institutions (such as Continental Illinois Bank) 
and entire sectors (such as the S&L industry). This 65-year period can 
be viewed as the golden age of compartmented banking and moderate 
leverage under Glass-Steagall and the SEC's original net capital rule. 
Derivatives themselves were highly constrained by the Commodity 
Exchange Act. In 1999, 2000 and 2004 respectively, all three of these 
watertight bulkheads were removed. By 2006 the system was poised for 
the most catastrophic financial collapse in history. While subprime 
mortgage failures provided the catalyst, it was the scale of the system 
itself which caused the damage. The catalyst could just as well have 
come from emerging markets, commercial real estate or credit default 
swaps. In a dynamically critical system, the catalyst is always less 
important than the chain reaction and the reaction in this case was a 
massive collapse.
    The idea of controlling cascades of failure is, in part, a matter 
of circuit breakers and pre-rehearsed crisis management so that nascent 
collapses do not spin into full systemic catastrophes before regulators 
have the opportunity to prevent the spread. The combination of diffuse 
credit and layered leverage makes it infeasible to assemble all of the 
affected parties in a single room to discuss solutions. There simply is 
not enough time or condensed information to respond in real time as a 
crisis unfolds.
    One significant circuit breaker which has been discussed for over a 
decade but which has still not been fully implemented is a 
clearinghouse for all over-the-counter derivatives. Experience with 
clearinghouses and netting systems such as the Government Securities 
Clearing Corporation shows that gross risk can be reduced 90 percent or 
more when converted to net risk through the intermediation of a 
clearinghouse. Bearing in mind that a parametric decrease in scale 
produces an exponential decrease in risk in a nonlinear system, the 
kind of risk reduction that arises in a clearinghouse can be the single 
most important step in the direction of stabilizing the financial 
system today; much more powerful than bail outs which do not reduce 
risk but merely bury it temporarily.
    A clearinghouse will also provide informational transparency that 
will allow regulators to facilitate the failure of financial 
institutions without producing contagion and systemic risk. Such 
failure (what Joseph Schumpeter called ``creative destruction'') is 
another necessary step on the road to financial recovery. Technical 
objections to clearinghouse implementation based on the non-uniformity 
of contracts can be overcome easily through consensual contractual 
modification with price adjustments upon joining the clearinghouse 
enforced by the understanding that those who refuse to join will be 
outside the safety net. Only by eliminating zombie institutions and 
creating breathing room for healthy institutions with sound balance 
sheets can the financial sector hope to attract sufficient private 
capital to replace government capital and thus re-start the credit 
creation process needed to produce sound economic growth.
    Recently a number of alternative paradigms have appeared which not 
only do not rely on VaR but rather assume its opposite and build models 
that are more robust to empirical evidence and market price patterns. 
Several of these approaches are:

Behavioral Economics--This field relies on insights into human behavior 
derived from social science and psychology, in particular, the 
``irrational'' nature of human decision-making when faced with economic 
choices. Insights include risk aversion, herding, the presence or 
absence of cognitive diversity and network effects among others. While 
not summarized in a general theory and while not always amendable to 
quantitative modeling, the insights of behavioral economics are 
powerful and should be considered in weighing reliance on VaR-style 
models which do not make allowance for subjective influences captured 
in this approach.

Imperfect Knowledge Economics--This discipline (under the abbreviation 
IKE) attempts to deal with uncertainty inherent in capital markets by 
using a combination of Bayesian networks, link analysis, causal 
inference and probabilistic hypotheses to fill in unknowns using the 
known. This method is heavily dependent on the proper construction of 
paths and the proper weighing of probabilities in each hypothesis cell 
or evidence cell, however, used properly it can guide decision-making 
without applying the straitjacket of VaR.

Econoahysics--This is a branch of financial economics which uses 
insights gained from physics to model capital markets behavior. These 
insights include nonlinearity in dynamic critical state systems the 
concept of phase transitions. Such systems exhibit an unpredictably 
deterministic nonlinear relationship between inputs and outputs (the 
so-called ``Butterfly Effect'') and scale invariance which accords well 
with actual time series of capital markets prices. Importantly, this 
field leads to a degree distribution characterized by the power curve 
rather than the bell curve with implications for scaling metrics in the 
management of systemic risk.
    It may be the case that these risk management tools work best at 
distinct scales. For example, behavioral economics seems to work well 
at the level of individual decision-making but has less to offer at the 
level of the system as a whole where complex feedback loops cloud its 
efficacy. IKE may work best at the level of a single institution where 
the hypothesis and evidence cells can be reasonably well defined and 
populated. Econophysics may work best at the systemic level because it 
goes the furthest in its ability to model highly complex dynamics. This 
division of labor suggests that rather than replacing VaR with a one-
size-fits-all approach, it may be best to adopt a nested hierarchy of 
risk management approaches resembling the following:




    While all of these approaches and others not mentioned here require 
more research to normalize metrics and build general theories, they are 
efficacious and robust alternatives to EMH and VaR and their 
development and use can serve a stabilizing function since they have a 
strong empirical basis unlike EMH and VaR.
    In summary, Wall Street's reigning risk management paradigm 
consisting of VaR using a normally distributed model combined with 
GARCH techniques applied to the non-normal region and stress testing to 
account for outliers is a manifest failure. It should be replaced at 
the systemic level with the empirically robust model based on nonlinear 
complexity and critical state dynamics as described by the power curve. 
This method also points the way to certain solutions, most importantly 
the creation of an over-the-counter derivatives clearinghouse which 
will de-scale the system and lead to an exponential decrease in actual 
risk. Such a clearinghouse can also be used to improve transparency and 
manage failure in ways that can leave the system far healthier while 
avoiding systemic collapse.
    Importantly, if scale is the primary determinant of risk, as 
appears to be the case in complex systems such as the financial 
markets, then it follows that de-scaling the system is the simplest and 
most effective way to manage risk. This does not mean that the totality 
of the system needs to shrink, merely that it be divided into sub-
components with limited interaction. This has the same effect as 
installing the watertight bulkheads referred to in the example above. 
In this manner, severe financial distress in one sector does not 
automatically result in contagion among all sectors.
    This effective de-scaling can be accomplished with three reforms:

1.  The enactment of a modernized version of Glass-Steagall with a 
strict separation between commercial banking and deposit taking on the 
one hand and principal risk taking in capital markets on the other.

2.  Strict requirements for all derivative products to be traded on 
exchanges subject to credit tests for firm memberships, initial margin, 
variation margin, position limits, price transparency and netting.

3.  Higher regulatory capital requirements and reduced leverage for 
banks and broker-dealers. Traditional ratios of 8:1 for banks and 15:1 
for brokers seem adequate provided off-balance sheet positions (other 
than exchange traded contracts for which adequate margin is posted) be 
included for this purpose.

    These rules can be implemented directly and do not depend on the 
output of arcane and dangerous models such as VaR. Instead, they derive 
from another proven model, the power curve, which teaches that risk is 
an exponential function of scale. By de-scaling, we radically reduce 
risk and restore stability to individual institutions and to the system 
as a whole.

                    Biography for James G. Rickards

    James G. Rickards is Senior Managing Director for Market 
Intelligence at Omnis, Inc., a scientific consulting firm in McLean, 
VA. He is also Principal of Global-I Advisors, LLC, an investment 
banking firm specializing in capital markets and geopolitics. Mr. 
Rickards is a seasoned counselor, investment banker and risk manager 
with over thirty years experience in capital markets including all 
aspects of portfolio management, risk management, product structure, 
regulation and operations. Mr. Rickards's market experience is focused 
in alternative investing and derivatives in global markets.
    Mr. Rickards was a first hand participant in the formation and 
growth of globalized capital markets and complex derivative trading 
strategies. He held senior executive positions at sell side firms 
(Citibank and RBS Greenwich Capital Markets) and buy side firms (Long-
Term Capital Management and Caxton Associates) and technology firms 
(OptiMark and Omnis). Mr. Rickards has participated directly in many of 
the most significant financial events over the past 30 years including 
the release of U.S. hostages in Iran (1981), the Stock Market crash of 
1987, the collapse of Drexel (1990), the Salomon Bros. bond trading 
scandal (1991) and the LTCM financial crisis of 1998 (in which Mr. 
Rickards was the principal negotiator of the government-sponsored 
rescue). He has founded several hedge funds and fund-of-funds. His 
advisory clients include private investment funds, investment banks and 
government directorates. Since 2001, Mr. Rickards has applied his 
financial expertise to missions for the benefit of the U.S. national 
security community.
    Mr. Rickards is licensed to practice law in New York and New Jersey 
and the Federal Courts. Mr. Rickards has held all major financial 
industry licenses including Series 3 (National Commodities Futures), 
Series 7 (General Securities Representative), Series 24 (General 
Securities Principal), Series 30 (Futures Manager) and Series 63.
    Mr. Rickards has been a frequent speaker at conferences sponsored 
by bar associations and industry groups in the fields of derivatives 
and hedge funds and is active in the International Bar Association. He 
has been the interviewed in The Wall Street Journal and on CNBC, Fox, 
CNN, NPR and C-SPAN and is an OpEd contributor to the New York Times 
and the Washington Post.
    Mr. Rickards is a graduate school visiting lecturer in finance at 
the Kellogg School and the School of Advanced International Studies. He 
has delivered papers on econophysics at the Applied Physics Laboratory 
and the Los Alamos National Laboratory. Mr. Rickards has written 
articles published in academic and professional journals in the fields 
of strategic studies, cognitive diversity, network science and risk 
management. He is a member of the Business Advisory Board of Shariah 
Capital, Inc., an advisory firm specializing in Islamic finance and is 
a member of the International Business Practices Advisory Panel to the 
Committee on Foreign Investment in the United States (CFIUS) Support 
Group of the Director of National Intelligence.
    Mr. Rickards holds the following degrees: LL.M. (Taxation) from the 
New York University School of Law; J.D. from the University of 
Pennsylvania Law School; M.A. in international economics from the 
School of Advanced International Studies, Washington DC; and a B.A. 
degree with honors from the School of Arts & Sciences of The Johns 
Hopkins University, Baltimore, MD.

    Chairman Miller. Thank you, Mr. Rickards. I did practice 
repeatedly saying ``Taleb.'' I should have practiced 
``Rickards'' as well.
    Mr. Whalen.

    STATEMENT OF MR. CHRISTOPHER WHALEN, MANAGING DIRECTOR, 
                  INSTITUTIONAL RISK ANALYTICS

    Mr. Whalen. Thank you, Mr. Chairman. I am going to just 
summarize a couple points further to my written testimony. You 
will notice in my comments I focused on the distinction between 
subjectivity and objectivity, and I think this committee is 
probably better placed to understand those distinctions than 
most of the other panels in the Congress.
    You know, we have seen over the last 100 years in this 
country a shift in our financial world from focusing on current 
performance of companies and financial institutions to focusing 
on predicting the future. This is very well illustrated in the 
Graham and Dodd volume, Securities Analysis, in chapter 38 
where they talk about new era investing, and I urge you to 
reread that if you have never done so before.
    The bottom line to me as someone who has worked in the 
industry as a supervisor and a trader and investment banker, is 
that when you use assumptions and models, you have already 
stepped off the deep edge, you know, the deep end of the pool, 
and there's no water in the pool. You essentially are in the 
world of speculation, and you have left the world of investing. 
Why do I say this? Well, if we use the same rules that govern 
the assumptions that go into most VaR models to design 
airplanes and buildings and dams, all of these physical 
structures would fail, because they violate the basic rules of 
scientific method that the Members of this committee know very, 
very well. I would submit to you that if we are going to allow 
our financial system to design products that are based on 
assumptions rather than hard data, than we are in big trouble. 
My firm has over the last seven years shunned the quantitative 
world. Our entire methodology is focused on benchmarking the 
current performance of banks, and taking observations about 
that current performance that may suggest what they are going 
to do in the future. But we don't guess, we don't speculate. We 
have almost 20,000 retail customers who use the bank monitor to 
track the safety and soundness of their institution. It is an 
entirely mechanical survey process. We stress-test every bank 
in the United States the same way, whether it is J.P. Morgan or 
Cullen/Frost Bank in Texas. We ask the same question, how did 
you do this quarter, and we compare it to 1995, which was a 
nice, boring year.
    The second point I would like to make is that I think a big 
part of the problem is that we allowed the economist profession 
to escape from the world of social sciences, and enter into an 
unholy union with commission-driven dealers in the securities 
market. Your colleague, Mr. Broun, said earlier that economists 
can't make up their mind. Well, yes, they can. When they are 
working in the securities business they have no trouble making 
up their mind. They offer opinions and hypotheses and `what if' 
or `I want' in regards to the creation of a security. This is a 
big problem. I wouldn't let most economists park my car, and 
the problem is not that they are not smart people, not that 
they are not interesting people, but they live in the world of 
supposition rather than the world of fact, and again, their 
methodologies are not governed by the iron rules that you find 
in physics or chemistry or any of the other physical sciences 
where you have to live by those rules. You can't come up with 
some neat concept and say to your colleagues, hey, look at me, 
or hey, look at this new CDO I designed, and then go out and 
sell that security to the public.
    I think it all comes down at the end of the day to what 
kind of economy do we want. There is an old-fashioned American 
concept called `fair dealing' that I spent a lot of time 
talking in my testimony to the Senate Banking Committee earlier 
this year, and it comes from the Greeks, the concept of 
proportional requital. One person gives value, the other person 
receives value. The problem with products like credit default 
swaps, is that they are entirely speculative. There is no 
visible underlying market for single-name credit default swaps 
really. The corporate bonds that are supposedly the derivative 
or the basis for the derivative are fairly liquid and not a 
very good source of pricing information, so we use models and 
we then sell these securities to anyone and everyone. I would 
submit that that is unfair, and it goes against the basic grain 
of American society that we are a fair and transparent nation. 
So bottom line to me is, if you want to fix the problem, I 
think we have got to reimpose not higher capital requirements 
on banks that are out of control, and which take risks that no 
one can really quantify. I think what we have to do is reimpose 
restrictions on their risk taking and get them to the point 
where an eight percent capital assets ratio makes sense again, 
because it clearly doesn't now. Does anybody really think we 
can get the private sector to double the capital of J.P. Morgan 
when their equity returns are going to be falling for the next 
couple of years? The only entity that would fund that 
opportunity would be a government, so what we are really saying 
is that these are GSEs. I think we have got to come back almost 
to the Glass-Steagall-era draconian division between the 
utility function of a bank and the transactional function of 
hedge funds, broker dealers, whatever, and that latter group 
can do whatever they want.
    So let me stop there, and I look forward to your questions.
    [The prepared statement of Mr. Whalen follows:]

                Prepared Statement of Christopher Whalen

    Chairman Miller, Congressman Broun, Members of the Committee, my 
name is Christopher Whalen and I live in the State of New York. I work 
in the financial community as an analyst and a principal of a firm that 
rates the performance of commercial banks.\1\ Thank you for inviting my 
comments today on this important subject.
---------------------------------------------------------------------------
    \1\ Mr. Whalen is a co-founder of Institutional Risk Analytics, a 
Los Angeles unit of Lord, Whalen LLC that publishes risk ratings and 
provides customized financial analysis and valuation tools.
---------------------------------------------------------------------------
    The Committee has asked witnesses to comment on the topic of ``The 
Risks of Financial Modeling: VaR and the Economic Meltdown.'' The 
comments below reflect my own views, as well as comments from my 
colleague and business partner Dennis Santiago, and others in the 
financial and risk management community.
    By way of background, our firm provides ratings for assessing the 
financial condition of U.S. banks and commercial companies. We build 
the analytical tools that we use to support these rating activities and 
produce reports for thousands of consumer and professional users.
    We use mathematical tools such as models to explore the current 
financial behavior of a given subject. In the course of our work, we 
use these tools to make estimates, for example, as to the maximum 
probable loss in a bank's loan portfolio through an economic cycle or 
the required Economic Capital for a financial institution. Models help 
us understand and illustrate how the financial condition of a bank or 
other obliger have changed and possibly will change in the future.
    But in all that we at Institutional Risk Analytics do in the world 
of ratings and financial analysis, we do our best to separate objective 
measures based upon empirical observations, and subjective analyses 
that employ speculative assumptions and directives which are often 
inserted into the very ground rules for the analysis process itself. 
The difference between subjectivity and objectivity in finance has 
significant implications for national policy when it comes to financial 
markets and institutions.
    I strongly suggest to the Committee that they bear the distinction 
between objective and subjective measures in mind when discussing the 
use of models in finance. Obtaining a better understanding of the role 
of inserting subjectivity into models is critical for distinguishing 
between useful deployments of modeling to manage risk and situations 
where models are the primary failure pathway towards creating systemic 
risk and thus affect economic stability and public policy.
    Used as both a noun and a verb, the word ``model'' has become the 
symbol for the latest financial crisis because of the use, or more 
precisely, the misuse of such simulations to price unregistered, 
illiquid securities such as sub-prime mortgage backed securities and 
derivatives of such securities. The anecdotal cases where errant models 
have led to mischief are many and are not limited to the world of 
finance alone.

The Trouble with Models

    The problem is not with models themselves. The trouble happens when 
they are (a) improperly constructed and then (b) deliberately 
misapplied by individuals working in the financial markets.
    In the physical sciences, models can be very usefully employed to 
help analysts understand complex systems such as disease, buildings and 
aircraft. These models tend to use observable data as inputs, can be 
scientifically validated and are codified in a manner that is 
transparent to all involved in the process. Models used in the physical 
world share one thing in common that financial models do not: they are 
connected to and are confirmed or refuted by the physical world they 
describe.
    Financial models, on the other hand, are all intellectual 
abstractions designed to manipulate arbitrarily chosen, human invented 
concepts. The chief reason for this digression from the objective use 
of models observed in the physical sciences is the injection of 
economics into the world of finance. Whereas financial models were once 
merely arithmetic expressions of expected cash flows, today in the 
world of financial economics, models have become vehicles for rampant 
speculation and outright fraud.\2\
---------------------------------------------------------------------------
    \2\ See ``New Hope for Financial Economics: Interview with Bill 
Janeway,'' The Institutional Risk Analyst, November 17, 2008.
---------------------------------------------------------------------------
    In the world of finance, modeling has been an important part of the 
decision-making toolkit of executives and analysts for centuries, 
helping them to understand the various components in a company or a 
market and thereby adjust to take advantage of the circumstances. These 
decision analysis models seek to measure and report on key indicators 
of actual performance and confirm the position of the entity with 
respect to its' competitive environment. For instance, the arithmetic 
calculation of cash flows adheres to the scientific method of 
structures and dynamics, and is the foundation of modern finance as 
embodied by the great theorists such as Benjamin Graham and David Dodd.
    At our firm, we employ a ``measure and report'' model called The 
IRA Bank Monitor to survey and stress test all FDIC insured banks each 
quarter. By bench-marking the performance of banks with a consistent 
set of tests, we are able to not only characterize the relative safety 
and soundness of each institution, but can drawn reasonable inferences 
about the bank's future performance.
    But when the world of finance marries the world of outcome driven 
economics--the world of ``what if'' and ``I want''--models cease to be 
mechanistic tools for validating current outcomes with hard data and 
assessing a reasonable range of possible future events. Instead models 
become enablers for speculation, for the use of skillful canards and 
legal subterfuge that ultimately cheat investors and cause hundreds of 
billions of dollars in losses to private investors and insured 
depository institutions.
    Take the world of mortgage backed securities or MBS. For decades 
the investment community had been using relatively simple models to 
predict the cash flow of MBS in various interest rate scenarios. These 
predictions have been relatively simple and are validated against the 
monthly mortgage servicer data available to the analyst community. The 
MBS securitization process was simple as well. A bank would sell 
conforming loans to GNMA and FNMA, and sell inferior collateral to a 
handful of investment banks on Wall Street to turn in the loans into 
private MBS issues.
    At the beginning of the 1990's, however, Wall Street's private MBS 
secret sauce escaped. A firm named Drexel, Burnham, Lambert went 
bankrupt and the bankruptcy court sold copies of Drexel's structured 
finance software to anyone and everyone. It eventually wound up in the 
hands of the mortgage issuers themselves. These banks and non-banks 
naturally began to issue private MBS by themselves and discovered they 
could use the mathematics of modeling to grow their mortgage conduit 
businesses into massive cash flow machines. When brought to market, 
these private MBS were frequently under-collateralized and could 
therefore be described as a fraud.
    Wall Street, in turn, created even more complex modeling systems to 
squeeze even more profits from the original MBS template. The expanding 
bubble of financial innovation caught the eye of policy-makers in the 
Congress, who then created political models envisioning the possibility 
that ``innovation'' could be used to make housing accessible to more 
Americans.
    Spurred on to chase the ``policy outcome'' of affordable housing, 
an entire range of deliberately opaque and highly leveraged financial 
instruments were born with the full support of Washington, the GSEs and 
the Congress. Their purpose now was to use the alchemy of financial 
modeling to create the appearance of mathematical safety out of 
dangerous toxic ingredients. Wall Street firms paid the major rating 
agencies to award ``AAA'' ratings to derivative assets that were 
ultimately based on sub-prime mortgage debt. And the stage was set for 
a future economic disaster.
    In the case of sub-prime toxic waste, the models became so complex 
that all transparency was lost. The dealers of unregulated, 
unregistered complex structured assets used proprietary models to price 
and sell deals, but since the ``underlying'' for these derivative 
securities was invisible, none of the investment or independent ratings 
community could model the security. There was no validation, no market 
discipline. Buy Side customers were dependent upon the dealer who sold 
them the toxic waste for valuation. The dealers that controlled the 
model often time would not even make a market in the security.
    Clearly we have now many examples where a model or the pretense of 
a model was used as a vehicle for creating risk and hiding it. More 
important, however, is the role of financial models for creating 
opportunities for deliberate acts of securities fraud. These acts of 
fraud have caused hundreds of billions of dollars in losses to 
depository institutions and investors.
    Whether you talk about toxic mortgage assets or credit default 
swaps, the one common element that the misuse of models seems to 
contain is a lack of a visible underlying market against which to judge 
or ``mark'' the model. Indeed, the use of models in a subjective 
context seems to include the simulation of a nonexistent market as the 
primary role for the financial model.
    In single-name credit default swaps or ``CDS'' for example, there 
is often insufficient trading in the supposed underlying corporate debt 
security to provide true price discovery. In the case of CDS on complex 
structured assets, there is no underlying market to observe at all. The 
subjective model becomes the market in terms of pricing the security.
    In the spring of 2007, however, the fantasy land consensus that 
allowed people to believe that a model is a market came undone. We have 
been dealing with the consequences of the decisions that originally 
built the house of cards since that time.

An Objective Basis for Finance and Regulation

    The term ``model'' as it applies to finance can be a simulation of 
reality in terms of predicting future financial outcomes. The author 
Nassim Taleb, who is appearing at this hearing, says the term ``VaR'' 
or value at risk describes a statistical estimate of ``the expected 
maximum loss (or worst loss) over a target horizon within a given 
confidence interval.'' \3\
---------------------------------------------------------------------------
    \3\ See Taleb, Nassim, ``Against Value-at-Risk: Nassim Taleb 
Replies to Philippe Jorion,'' 1997.
---------------------------------------------------------------------------
    VaR models and similar statistical methods pretend to estimate the 
largest possible loss that an investor might experience over a given 
period of time to a given degree of certainty. The use of VaR type 
models, including the version embedded in the Basel II agreement, 
involves a number of assumptions about risk and outcomes that are 
speculative. More important, the widespread use of these statistical 
models for risk management suggest that financial institutions are 
subject to occasional ``Black Swans'' in the form of risk events that 
cannot be anticipated.
    We take a different view. We don't actually believe there is such a 
thing as a ``Black Swan.'' Our observations tell us that a more likely 
explanation is that leaders in finance and politics simply made the 
mistake of, again, believing in what were in fact flawed models and 
blinded themselves to what should have been plainly calculable 
innovation risks destined to be unsustainable. Or worse, our leaders in 
Washington and on Wall Street decided to be short sighted and not care 
about the inevitable debacle.
    We suggest that going forward our national interest needs to demand 
a higher standard of tangible proof from ``outcome designers'' of 
public policies. If financial markets and the models used to describe 
them are limited to those instruments that can be verified objectively, 
then we no longer need to fear from the ravages of Black Swans or 
systemic risk. The source of systemic risk in the financial markets is 
fear born from the complexity of opaque securities for which there is 
no underlying basis. The pretext for issuing these ersatz securities 
depends on subjectivity injected into a flawed model.
    If we accept that the sudden change in market conditions or the 
``Black Swan'' event that Taleb and other theorists have so elegantly 
described arises from a breakdown in prudential regulation and basic 
common sense, and not from some unknowable market mechanism, then we no 
longer need to fear surprises or systemic risk. We need to simply 
ensure that all of the financial instruments in our marketplace have an 
objective basis, including a visible, cash basis market that is visible 
to all market participants. If investors cannot price a security 
without reference to subjective models, then the security should be 
banned from the U.S. markets as a matter of law and regulation. To do 
otherwise is to adopt deception as the public policy goal of the U.S. 
when it comes to financial markets regulation.
    As Graham and Dodd wrote nearly a century ago, the more speculative 
the inputs the less the analysis matters. Models only have real value 
to society when their workings are disciplined by the real world. When 
investors, legislators and regulators all mistook models for markets, 
and even accepted such speculations as a basis for regulating banks and 
governing over-the-counter or OTC markets for all types of securities, 
we as a nation were gambling with our patrimony. If the Committee and 
the Congress want to bring an end to the financial crisis, we must 
demand higher standards from our citizens who work in and regulate our 
financial markets.
    As we discussed in a commentary last month, ``Systemic Risk: Is it 
Black Swans or Market Innovations?,'' published in The Institutional 
Risk Analyst, ``were the failures of Bear Stearns, Lehman Brothers, 
Washington Mutual or the other ``rare'' events really anomalous? Or are 
we just making excuses for our collective failure to identify and 
manage risk? A copy of our commentary follows this testimony. I look 
forward to your questions.

                    Systemic Risk: Is it Black Swans

                         or Market Innovations?

                            August 18, 2009

         ``Whatever you think you know about the distribution changes 
        the distribution.''

                                         Alex Pollock
                                         American Enterprise Institute

    In this week's issue of The IRA, our friend and colleague Richard 
Alford, a former Fed of New York economist, and IRA founders Dennis 
Santiago and Chris Whalen, ask us whether we really see Black Swans in 
market crises or our own expectations. Of note, we will release our 
preliminary Q2 Banking Stress Index ratings on Monday, August 24, 2009. 
As with Q1, these figures represent about 90 percent of all FDIC 
insured depositories, but exclude the largest money center banks (aka 
the ``Stress Test Nineteen''), thus providing a look at the state of 
the regional and community banks as of the quarter ended June 30, 2009. 
Click here to register for The Institutional Risk Analyst.

    Many popular explanations of recent financial crises cite ``Black 
Swan'' events; extreme, unexpected, ``surprise'' price movements, as 
the causes of the calamity. However, in looking at our crisis wracked 
markets, we might consider that the Black Swan hypothesis doesn't fit 
the facts as well an alternative explanation: namely that the 
speculative outburst of financial innovation and the artificially low, 
short-run interest rate environment pursued by the Federal Open Market 
Committee, combined to change the underlying distribution of potential 
price changes. This shift in the composition of the distribution made 
likely outcomes that previously seemed impossible or remote. This shift 
in possible outcomes, in turn, generated surprise in the markets and 
arguably led to the emergence of ``systemic risk'' as a metaphor to 
explain these apparent ``anomalies.''
    But were the failures of Bear Stearns, Lehman Brothers, Washington 
Mutual or the other ``rare'' events really anomalous? Or are we just 
making excuses for our collective failure to identify and manage risk?
    The choice of which hypothesis to ultimately accept in developing 
the narrative description of the causation of the financial crisis has 
strategic implications for understanding as well as reducing the 
likelihood of future crisis, including the effect on the safety and 
soundness of financial institutions. To us, the hard work is not trying 
to specifically limit the range of possibilities with artificial 
assumptions, but to model risk when you must assume as a hard rule, 
like the rules which govern the physical sciences, that the event 
distribution is in constant flux.
    If we as financial and risk professional are serious in claims to 
model risk proactively, then change, not static assumptions, must be 
the rule in terms of the possible outcomes. Or ``paranoid and nimble'' 
in practical terms. After all, these modeling exercises ultimately 
inform and support risk assumptions for decisions that are used in 
value-at-risk (VaR) assessments for investors and for capital adequacy 
bench-marking for financial institutions.
    Even before the arrival of Benoit Mandelbrot in the 1960s, 
researchers had observed that distributions of price changes in various 
markets were not normally distributed. The observed distributions of 
price changes had fatter tails than the normal distribution. Nassim 
Nicolas Taleb, author of The Black Swan and Fooled by Randomness, and 
others have dubbed significantly larger extreme price moves than those 
predicted by a normal distribution as ``Black Swans.'' Indeed, Taleb 
and others have linked Black Swan price change events to the recent 
financial crisis, suggesting in effect that we all collectively 
misunderstood on which side of the distribution of possible risk 
outcomes we stood.
    The argument is as follows: Current risk management and derivative 
pricing regimes are based upon normal distributions. Price movements in 
the recent financial crises were unpredictable/low probability events 
that were also greater than predicted by normal distribution models. 
Hence our collective failure to anticipate Black Swan events is 
``responsible'' for the recent crises as mis-specified risk management 
models failed due to fatter than normal tails.
    The alternative explanation, however, links the extreme price 
movements not to aberrations with respect to a stable, observable mean, 
but instead to the activation of alternate stable means as a result of 
jumping discontinuously through tipping points--much in the same way 
particles jump quantum levels in energy states when subjected to the 
cumulative effects of energy being added to or removed from their 
environments. These tipping points are as predictable as the annual 
migrations of ducks. Swans, alas, rarely migrate, preferring to stay in 
their summer feeding grounds until the water freezes, then move only 
far enough to find open water. Sound familiar?
    Force feed a system with enough creative energy via permissive 
public policies and the resulting herd behaviors, and the system will 
change to align around these new norms, thereby erasing the advantages 
of the innovators and creating unforeseen hazards. ``Advances'' such as 
OTC derivatives and complex structured assets, and very accommodating 
Fed interest rate policy, resulted in unprecedented leverage and 
maturity mismatches by institutions and in markets that are the perfect 
quantum fuel to brew such change.
    While the exact timing of each tipping point and magnitude of the 
crises remains somewhat inexact, the waves of change and the ultimate 
crisis borne shift are broadly predictable. The probabilities attached 
to extreme price moves are calculable as the cost of deleveraging an 
accumulation of innovation risk that must be shed as the system 
realigns. The ``Black Swan'' approach assumes a stable distribution of 
price changes with fatter than ``normal'' tails. The alternative posits 
that the distribution of possible price changes was altered by 
innovation and the low cost of leverage. It also posits that the new 
distributions allowed, indeed require, more extreme price movements. 
Two examples will illustrate the alternative hypothesis.
    Once upon a time, the convertible bond market was relatively quiet. 
The buy side was dominated by real money (unleveraged) players who 
sought the safety of bonds, but were willing to give up some return for 
some upside risk (the embedded equity call option).
    More recently the market has been dominated by leveraged hedge 
funds doing convertible bond arbitrage. They bought the bonds, hedging 
away the various risks. In response to the advent of the arbitrageurs, 
the spread between otherwise similar conventional and convertible bonds 
moved to more accurately reflect the value of the embedded option and 
became less volatile.
    When the financial crises hit, however, arbitrageurs were forced to 
liquidate their positions as losses mounted and it became difficult to 
fund the leveraged positions. Prices for convertible bonds declined and 
for a period were below prices for similar conventional bonds--
something that had been both unheard of and considered impossible as 
the value of an option cannot be negative.
    Was this a Black Swan type event, or had the market for convertible 
bonds and the underlying distribution of price changes, been altered? 
The mean spread between otherwise similar conventional and convertible 
bonds had changed. The volatility of the spread had changed. Forced 
sales and the public perception of possible future forced sales 
generated unprecedented behavior of the heretofore stable spread. The 
emergence and then dominance of leveraged arbitrage positions altered 
the market in fundamental ways. What had not been possible had become 
possible.
    Now consider bank exposures to commercial real estate. Numerous 
financial institutions, hedge funds (e.g., at Bear Stearns), sellers of 
CDS protection (e.g., AIG) and banks (many of them foreign as reflected 
in the Fed swap lines with foreign central banks) suffered grievous 
losses when the real estate bubble popped. Much of these losses remain 
as yet unrealized.
    As investors and regulators demanded asset-write downs and loss 
realization, many of these institution expressed dismay. They had 
stressed tested their portfolios, the large banks complained, often 
with the support of regulators. The large banks thought their 
geographically diversified portfolios of MBSs immunize them from falls 
in real estate prices as the US had experienced regional, but never 
(except for the 1930s) nationwide declines in housing prices. These 
sophisticated banks incorporated that assumption into their stress test 
even as they and the securitization process were nationalizing--that 
is, changing--the previously regional and local mortgage markets.
    Was the nationwide decline in housing prices an unpredictable Black 
Swan event or the foreseeable result of lower lending standards, a 
supportive interest rate environment, and financial innovation the led 
to the temporary nationalization of the mortgage market? Risk 
management regimes failed and banks have been left with unrealized 
losses that still threaten the solvency of the entire system in Q3 
2009.
    However useful or necessary ``normal'' statistical measures such as 
VaR might be, it will not be sufficient to insulate institutions or the 
system from risk arising from rapidly evolving market structures and 
practices. Furthermore, insofar as models such as VaR, which are now 
enshrined in the bank regulatory matrix via Basel II, were the binding 
constraint on risk taking, it acted perversely, allowing ever greater 
leverage as leveraged trading acted to reduce measured volatility! 
Remember, the convertible bond market at first looked placid as a lake 
as leverage grew--but then imploded in a way few thought possible. Is 
this a Black Swan event or a failure of the stated objectives of risk 
management and prudential oversight?
    We all know that risk management systems based solely on analysis 
of past price moves will at some point fall if financial markets 
continue to change. The problem with current risk management systems 
cannot be fixed by fiddling with VaR or other statical models. Risk 
management regimes must incorporate judgments about the evolution of 
the underlying markets, distribution of possible price changes and 
other dynamic sources of risk.
    Indeed, as we discussed last week (``Are You Ready for the Next 
Bank Stress Tests''), this is precisely why IRA employs quarterly 
surveys of bank stress tests to benchmark the US banking industry. 
Think of the banking industry as a school of fish, moving in generally 
the same direction, but not uniformly or even consistently. There is 
enormous variation in the past of each member of the school, even 
though from a distance the group seems to move in unison.
    Stepping back from the narrow confines of finance for a moment, 
consider that the most dramatic changes in the world are arguably 
attributable to asymmetric confluences of energy changing the direction 
of human history. It's happened over and over again. The danger has and 
always will be the immutable law of unintended consequences, which 
always comes back to bite the arrogant few who believe they can control 
the future outcome. And it is always the many of us who pay the price 
for these reckless leaps of faith.
    If the recent financial crises were truly highly infrequent random 
events, then any set of policies that can continuously prevent their 
reoccurrence seemingly will be very expensive in terms of idle capital 
and presumably less efficient markets required to avoid them. If, on 
the other hand, the crisis was the result of financial innovation and 
the ability to get leveraged cheaply, then society need not 
continuously bare all the costs associated with preventing market 
events like the bursting of asset bubbles.
    Policy-makers would like everyone to believe that the recent crises 
were random unpredictable Black Swan events. How can they be blamed for 
failing to anticipate a low probability, random, and unpredictable 
event? If on the other hand, the crises had observable antecedents, 
e.g., increased use of leverage, maturity mismatches, near zero default 
rates, and spikes in housing price to rental rates and housing price to 
income ratios, then one must ask: why policy-makers did not connect the 
dots, attach significant higher than normal probabilities to the 
occurrence of severe financial disturbances, and fashion policies 
accordingly? Ultimately, that is a question that Ben Bernanke and the 
rest of the federal financial regulatory community still have yet to 
answer.
    Questions? Comments? [email protected]

                    Biography for Christopher Whalen

    Christopher is co-founder of Institutional Risk Analytics, the Los 
Angeles based provider of risk management tools and consulting services 
for auditors, regulators and financial professionals. Christopher leads 
IRA's risk advisory practice and consults for global companies on a 
variety of financial and regulatory issues. He is a Fellow of the 
Networks Financial Institute at Indiana State University. Christopher 
volunteers as a regional director of Professional Risk Managers 
International Association (www.prmia.org) and is a board adviser to I-
OnAsia Limited (www.ionasia.com.hk), a global business security and 
risk consultancy based in Hong Kong. Christopher currently edits The 
Institutional Risk Analyst, a weekly news report and commentary on 
significant developments in and around the global financial markets. 
Christopher has testified before the Congress and the SEC on a variety 
of issues and contributes articles and commentaries to publications 
such as The International Economy, American Banker and The Big Picture.

    Chairman Miller. Thank you.
    Dr. Colander.

     STATEMENT OF DR. DAVID COLANDER, CHRISTIAN A. JOHNSON 
    DISTINGUISHED PROFESSOR OF ECONOMICS, MIDDLEBURY COLLEGE

    Dr. Colander. Mr. Chairman, thanks for the opportunity to 
testify. I am Dave Colander, the Christian A. Johnson 
Distinguished Professor of Economics at Middlebury College. I 
was invited here because I was one of the authors of the Dahlem 
Report in which we chided the economics profession for its 
failure to warn society about the impending financial crisis.
    Some non-economists have blamed the financial crisis on 
economists' highly technical models. My argument is that the 
problem isn't the models, the problem is the way the economic 
models are used, and I think a number of the other panelists 
have made that point. Where I am going to lead or go with that 
is that the issue goes much deeper than just with VaR and the 
various models you are looking at, and it goes very much to the 
general arguments about science and technology and the way in 
which economists approach problems, and I think, you know, Mr. 
Whalen had it directly right: we live in the world of 
supposition. Why? Because that is what our incentives are. We 
write articles. We advance through writing articles, we don't 
advance by designing something positive. If we are working for 
a business, we do, but within academics it is very much 
directed towards, you know, sort of what can we publish, and so 
I think Value-at-Risk models are part of a much broader 
economic problem, you know, sort of in terms of what economists 
accept and how they go about doing what they are doing.
    An example I want to give is really about macroeconomics, 
you know, sort of in the dominant model in macroeconomics, 
which is the dynamic stochastic general equilibrium (DSGE) 
model, which is a big model designed very much along the same 
lines about efficient markets. It sort of took efficient 
markets and said, what if we had efficient markets in the 
entire economy? To get that model, you have to assume there is 
one individual, because we can't solve it unless there is only 
one individual. We have to assume that person is globally 
rational, understands everything and he has complete knowledge 
in looking into the infinite future, and then we can actually 
solve it for a very small case.
    By definition, this model rules out strategic coordination 
problems. What would happen if somebody else did something 
else? That is obviously the likely cause of the recent crisis, 
but it was simply assumed away in the macroeconomic model and 
that macroeconomic model has been dominant for the last 30 
years and has been funded by NSF, the research, you need to be 
looking into that.
    If the DSGE model had been seen as an aid to common sense, 
it could have been a useful model. It improved some of the 
problems that some earlier models had. But for a variety of 
sociological reasons that I don't have time to go into here, a 
majority of macroeconomists started believing the DSGE model 
was useful, not just as an aid to our understanding but as the 
model of the macroeconomy. As that DSGE model became dominant, 
really important research on the whole set of broader non-
linear and complex dynamic models that would have really served 
some foundation for thinking about these issues just wasn't 
done. It just wasn't allowed. You couldn't get anything 
published on it in the main macro journals.
    Similar developments occurred with the efficient market 
finance models, which made assumptions very similar to the DSGE 
model. And so, again, at first these served a useful purpose. 
They led to technological advances in risk management and 
financial markets. But as happened in macro, the users of these 
financial models forgot that the models provide, at best, half-
truths. They stopped using models with common sense and 
judgment. What that means is that warning labels should be put 
on models, and that should be in bold print, `these models are 
based on assumptions that do not fit the real world and thus 
these models should be not relied on very heavily.' Those 
warning labels haven't been there.
    How did something so stupid like this happen in economics? 
It didn't happen because economists are stupid, and I 
appreciate the people before who said we are not. We are very 
bright. It happened because of incentives within the economics 
profession and those incentives lead researchers to dot i's and 
cross t's of existing models. It is a lot easier to do that 
than to design a whole new model that nobody else, a peer, can 
really review. So they don't explore the wide range of 
alternative models, and they don't focus their research on 
interpreting and seeing that models are used in policy in a 
common sense fashion.
    So let me conclude with just two brief suggestions which 
relate to issues under the jurisdiction of this committee that 
might decrease the probability of such events happening in the 
future, and these are far off but it has to do with, you know, 
sort of the incentives for economists. The first is a proposal 
that might add some common sense check on models. Such a check 
is needed because currently there is a nature of the internal 
to the sub-field peer review system, that works within NSF and 
within the system, that allows for what can only be called an 
incestuous mutual reinforcement of researchers' views with no 
common sense filter on those views. My proposal is to include a 
wider range of peers in the reviewing process for the National 
Science Foundation grants in the social sciences. For example, 
physicists, mathematicians, statisticians and even business and 
government representatives could serve on reviewing those, and 
it would serve as a useful common sense check, you know, about 
what is going on.
    The second is a proposal to increase the number of 
researchers trained in interpreting models, rather than 
developing models, by providing research grants to do precisely 
that. In a sense, what I am suggesting is an applied science 
division of the National Science Foundation, a social science 
component. This division would fund work on the usefulness of 
models and would be responsible for adding the warning labels 
that should have been attached to those models.
    The applied research would not be highly technical and 
would involve a quite different set of skills than the standard 
scientific research requires. It would require researchers to 
have an intricate knowledge--consumer's knowledge of the 
theory, but not a producer's knowledge of that theory. In 
addition, it would require a knowledge of institutions, 
methodology, previous literature and a sensibility of how the 
system works. These are all skills that are not taught in 
graduate economics today, but they are skills that underlie 
judgment and common sense. By providing NSF grants for this 
work, the NSF would encourage the development of a group of 
economists who specialize in interpreting models and applying 
models to the real world. The development of such a group would 
go a long way toward placing the necessary warning labels on 
models. Thank you.
    [The prepared statement of Dr. Colander follows:]

                  Prepared Statement of David Colander

    Mr. Chairman and Members of the Committee: I thank you for the 
opportunity to testify. My name is David Colander. I am the Christian 
A. Johnson Distinguished Professor of Economics at Middlebury College. 
I have written or edited over forty books, including a top-selling 
principles of economics textbook, and 150 articles on various aspects 
of economics. I was invited to speak because I was one of the authors 
of the Dahlem Report in which we chided the economics profession for 
its failure to warn society about the impending financial crisis, and I 
have been asked to expand on some of the themes that we discussed in 
that report. (I attach that report as an appendix to this testimony.)

Introduction

    One year ago, almost to the day, the U.S. economy had a financial 
heart attack, from which it is still recovering. That heart attack, 
like all heart attacks, was a shock, and it has caused much discussion 
about who is to blame, and how can we avoid such heart attacks in the 
future. In my view much of that discussion has been off point. To make 
an analogy to a physical heart attack, the U.S. had a heart attack 
because it is the equivalent of a 450-pound man with serious ailments 
too numerous to list, who is trying to live as if he were still a 20-
year-old who can party 24-7. It doesn't take a rocket economist to know 
that that will likely lead to trouble. The questions I address in my 
testimony are: Why didn't rocket economists recognize that, and warn 
society about it? And: What changes can be made to see that it doesn't 
happen in the future?
    Some non-economists have blamed the financial heart attack on 
economist's highly technical models. In my view the problem is not the 
models; the problem is the way economic models are used. All too often 
models are used in lieu of educated common sense, when in fact models 
should be used as an aid to educated common sense. When models replace 
common sense, they are a hindrance rather than a help.

Modeling the Economy as a Complex System

    Using models within economics or within any other social science, 
is especially treacherous. That's because social science involves a 
higher degree of complexity than the natural sciences. The reason why 
social science is so complex is that the basic unit in social science, 
which economists call agents, are strategic, whereas the basic unit of 
the natural sciences are not. Economics can be thought of the physics 
with strategic atoms, who keep trying to foil any efforts to understand 
them and bring them under control. Strategic agents complicate modeling 
enormously; they make it impossible to have a perfect model since they 
increase the number of calculations one would have to make in order to 
solve the model beyond the calculations the fastest computer one can 
hypothesize could process in a finite amount of time.
    Put simply, the formal study of complex systems is really, really, 
hard. Inevitably, complex systems exhibit path dependence, nested 
systems, multiple speed variables, sensitive dependence on initial 
conditions, and other non-linear dynamical properties. This means that 
at any moment in time, right when you thought you had a result, all 
hell can break loose. Formally studying complex systems requires 
rigorous training in the cutting edge of mathematics and statistics. 
It's not for neophytes.
    This recognition that the economy is complex is not a new 
discovery. Earlier economists, such as John Stuart Mill, recognized the 
economy's complexity and were very modest in their claims about the 
usefulness of their models. They carefully presented their models as 
aids to a broader informed common sense. They built this modesty into 
their policy advice and told policy-makers that the most we can expect 
from models is half-truths. To make sure that they did not claim too 
much for their scientific models, they divided the field of economics 
into two branches-one a scientific branch, which worked on formal 
models, and the other political economy, which was the branch of 
economics that addressed policy. Political economy was seen as an art 
which did not have the backing of science, but instead relied on the 
insights from models developed in the scientific branch supplemented by 
educated common sense to guide policy prescriptions.
    In the early 1900s that two-part division broke down, and 
economists became a bit less modest in their claims for models, and 
more aggressive in their application of models directly to policy 
questions. The two branches were merged, and the result was a tragedy 
for both the science of economics and for the applied policy branch of 
economics.
    It was a tragedy for the science of economics because it led 
economists away from developing a wide variety of models that would 
creatively explore the extraordinarily difficult questions that the 
complexity of the economy raised, questions for which new analytic and 
computational technology opened up new avenues of investigation.\1\ 
Instead, the economics profession spent much of its time dotting i's 
and crossing t's on what was called a Walrasian general equilibrium 
model which was more analytically tractable. As opposed to viewing the 
supply/demand model and its macroeconomic counterpart, the Walrasian 
general equilibrium model, as interesting models relevant for a few 
limited phenomena, but at best a stepping stone for a formal 
understanding of the economy, it enshrined both models, and acted as if 
it explained everything. Complexities were just assumed away not 
because it made sense to assume them away, but for tractability 
reasons. The result was a set of models that would not even pass a 
perfunctory common sense smell test being studied ad nauseam.
---------------------------------------------------------------------------
    \1\ Some approaches working outside this Walrasian general 
equilibrium framework that I see as promising includes approaches using 
adaptive network analysis, agent based modeling, random graph theory, 
ultrametrics, combinatorial stochastic processes, co-integrated vector 
auto-regression, and the general study of non-linear dynamic models.
---------------------------------------------------------------------------
    Initially macroeconomics stayed separate from this broader unitary 
approach, and relied on a set of rough and ready models that had little 
scientific foundation. But in the 1980s, macroeconomics and finance 
fell into this ``single model'' approach. As that happened it caused 
economists to lose sight of the larger lesson that complexity conveys--
that models in a complex system can be expected to continually break 
down. This adoption by macroeconomists of a single-model approach is 
one of the reasons why the economics profession failed to warn society 
about the financial crisis, and some parts of the profession assured 
society that such a crisis could not happen. Because they focused on 
that single model, economists simply did not study and plan for the 
inevitable breakdown of systems that one would expect in a complex 
system, because they had become so enamored with their model that they 
forgot to use it with common sense judgment.

Models and Macroeconomics

    Let me be a bit more specific. The dominant model in macroeconomics 
is the dynamic stochastic general equilibrium (DSGE) model. This is a 
model that assumes there is a single globally rational representative 
agent with complete knowledge who is maximizing over the infinite 
future. In this model, by definition, there can be no strategic 
coordination problem--the most likely cause of the recent crisis--such 
problems are simply assumed away. Yet, this model has been the central 
focus of macro economists' research for the last thirty years.
    Had the DSGE model been seen as an aid to common sense, it could 
have been a useful model. When early versions of this model first 
developed back in the early 1980s, it served the useful purpose of 
getting some inter-temporal issues straight that earlier macroeconomic 
models had screwed up. But then, for a variety of sociological reasons 
that I don't have time to go into here, a majority of macroeconomists 
started believing that the DSGE model was useful not just as an aid to 
our understanding, but as the model of the macroeconomy. That doesn't 
say much for the common sense of rocket economists. As the DSGE model 
became dominant, important research on broader non-linear dynamic 
models of the economy that would have been more helpful in 
understanding how an economy would be likely to crash, and what 
government might do when faced with a crash, was not done.\2\
---------------------------------------------------------------------------
    \2\ Among well known economists, Robert Solow stands out in having 
warned about the use of DSGE models for policy. (See Solow, in 
Colander, 2007, pg. 235.) He called them ``rhetorical swindles.'' Other 
economists, such as Post Keynesians, and economic methodologists also 
warned about the use of these models. For a discussion of alternative 
approaches, see Colander, ed. (2007). So alternative approaches were 
being considered, and concern about the model was aired, but those 
voices were lost in the enthusiasm most of the macroeconomics community 
showed for these models.
---------------------------------------------------------------------------
    Similar developments occurred with efficient market finance models, 
which make similar assumptions to DSGE models. When efficient market 
models first developed, they were useful; they led to technological 
advances in risk management and financial markets. But, as happened 
with macro, the users of these financial models forgot that models 
provide at best half truths; they stopped using models with common 
sense and judgment. The modelers knew that there was uncertainty and 
risk in these markets that when far beyond the risk assumed in the 
models. Simplification is the nature of modeling. But simplification 
means the models cannot be used directly, but must be used judgment and 
common sense, with a knowledge of the limitations of use that the 
simplifications require. Unfortunately, the warning labels on the 
models that should have been there in bold print--these models are 
based on assumptions that do not fit the real world, and thus the 
models should not be relied on too heavily--were not there. They should 
have been, which is why in the Dahlem Report we suggested that economic 
researchers who develop these models be subject to a code of ethics 
that requires them to warn society when economic models are being used 
for purposes for which they were not designed.
    How did something so stupid happen in economics? It did not happen 
because economists are stupid; they are very bright. It happened 
because of incentives in the academic profession to advance lead 
researchers to dot i's and cross t's of existing models, rather than to 
explore a wide range of alternative models, or to focus their research 
on interpreting and seeing that models are used in policy with common 
sense. Common sense does not advance one very far within the economics 
profession. The over-reliance on a single model used without judgment 
is a serious problem that is built into the institutional structure of 
academia that produces economic researchers. That system trains show 
dogs, when what we need are hunting dogs.
    The incorrect training starts in graduate school, where in their 
core courses students are primarily trained in analytic techniques 
useful for developing models, but not in how to use models creatively, 
or in how to use models with judgment to arrive at policy conclusions. 
For the most part policy issues are not even discussed in the entire 
core macroeconomics course. As students at a top graduate school said, 
``Monetary and fiscal policy are not abstract enough to be a question 
that would be answered in a macro course'' and ``We never talked about 
monetary or fiscal policy, although it might have been slipped in as a 
variable in one particular model.'' (Colander, 2007, pg. 169).

Suggestions

    Let me conclude with a brief discussion of two suggestions, which 
relate to issues under the jurisdiction of this committee, that might 
decrease the probability of such events happening in the future.

Include a wider range of peers in peer review
    The first is a proposal that might help add a common sense check on 
models. Such a check is needed because, currently, the nature of 
internal-to-the-subfield peer review allows for an almost incestuous 
mutual reinforcement of researcher's views with no common sense filter 
on those views. The proposal is to include a wider range of peers in 
the reviewing process of NSF grants in the social sciences. For 
example, physicists, mathematician, statisticians, and even business 
and governmental representatives, could serve, along with economists, 
on reviewing committees for economics proposals. Such a broader peer 
review process would likely both encourage research on much wider range 
of models and would also encourage more creative work.

Increase the number of researchers trained to interpret models
    The second is a proposal to increase the number of researchers 
trained in interpreting models rather than developing models by 
providing research grants to do that. In a sense, what I am suggesting 
is an applied science division of the National Science Foundation's 
social science component. This division would fund work on the 
usefulness of models, and would be responsible for adding the warning 
labels that should have been attached to the models.
    This applied research would not be highly technical and would 
involve a quite different set of skills than the standard scientific 
research would require. It would require researchers who had an 
intricate consumer's knowledge of theory but not a producer's 
knowledge. In addition it would require a knowledge of institutions, 
methodology, previous literature, and a sensibility about how the 
system works. These are all skills that are currently not taught in 
graduate economics programs, but they are the skills that underlie 
judgment and common sense. By providing NSF grants for this work, the 
NSF would encourage the development of a group of economists who 
specialized in interpreting models and applying models to the real 
world. The development of such a group would go a long way toward 
placing the necessary warning labels on models, and make it less likely 
that fiascoes like a financial crisis would happen again.

Bibliography

Colander, David. 2006. (ed.) Post Walrasian Macroeconomics: Beyond the 
        Dynamic Stochastic General Equilibrium Model. Cambridge, UK. 
        Cambridge University Press.

Colander, David. 2007. The Making of an Economist Redux. Princeton, New 
        Jersey, Princeton University Press.

Solow, Robert. 2007. ``Reflections on the Survey'' in Colander (2007).

Appendix




1. Introduction

    The global financial crisis has revealed the need to rethink 
fundamentally how financial systems are regulated. It has also made 
clear a systemic failure of the economics profession. Over the past 
three decades, economists have largely developed and come to rely on 
models that disregard key factors--including heterogeneity of decision 
rules, revisions of forecasting strategies, and changes in the social 
context--that drive outcomes in asset and other markets. It is obvious, 
even to the casual observer that these models fail to account for the 
actual evolution of the real-world economy. Moreover, the current 
academic agenda has largely crowded out research on the inherent causes 
of financial crises. There has also been little exploration of early 
indicators of system crisis and potential ways to prevent this malady 
from developing. In fact, if one browses through the academic 
macroeconomics and finance literature, ``systemic crisis'' appears like 
an otherworldly event that is absent from economic models. Most models, 
by design, offer no immediate handle on how to think about or deal with 
this recurring phenomenon.\3\ In our hour of greatest need, societies 
around the world are left to grope in the dark without a theory. That, 
to us, is a systemic failure of the economics profession.
---------------------------------------------------------------------------
    \3\ Reinhart and Rogoff (2008) argue that the current financial 
crisis differs little from a long chain of similar crises in developed 
and developing countries. We certainly share their view. The problem is 
that the received body of models in macro finance to which the above 
authors have prominently contributed provides no room whatsoever for 
such recurrent boom and bust cycles. The literature has, therefore, 
been a major source of the illusory `this time it is different' view 
that the authors themselves criticize.
---------------------------------------------------------------------------
    The implicit view behind standard equilibrium models is that 
markets and economies are inherently stable and that they only 
temporarily get off track. The majority of economists thus failed to 
warn policy-makers about the threatening system crisis and ignored the 
work of those who did. Ironically, as the crisis has unfolded, 
economists have had no choice but to abandon their standard models and 
to produce hand-waving common sense remedies. Common sense advice, 
although useful, is a poor substitute for an underlying model that can 
provide much-needed guidance for developing policy and regulation. It 
is not enough to put the existing model to one side, observing that one 
needs, ``exceptional measures for exceptional times.'' What we need are 
models capable of envisaging such ``exceptional times.''
    The confinement of macroeconomics to models of stable states that 
are perturbed by limited external shocks and that neglect the intrinsic 
recurrent boom-and-bust dynamics of our economic system is remarkable. 
After all, worldwide financial and economic crises are hardly new and 
they have had a tremendous impact beyond the immediate economic 
consequences of mass unemployment and hyper inflation. This is even 
more surprising, given the long academic legacy of earlier economists' 
study of crisis phenomena, which can be found in the work of Walter 
Bagehot (1873), Axel Leijonhuvfud (2000), Charles Kindleberger (1989), 
and Hyman Minsky (1986), to name a few prominent examples. This 
tradition, however, has been neglected and even suppressed.
    The most recent literature provides us with examples of blindness 
against the upcoming storm that seem odd in retrospect. For example, in 
their analysis of the risk management implications of CDOs, Krahnen 
(2005) and Krahnen and Wilde (2006) mention the possibility of an 
increase of `systemic risk.' But, they conclude that this aspect should 
not be the concern of the banks engaged in the CDO market, because it 
is the governments' responsibility to provide costless insurance 
against a system-wide crash. We do not share this view. On the more 
theoretical side, a recent and prominent strand of literature 
essentially argues that consumers and investors are too risk averse 
because of their memory of the (improbable) event of the Great 
Depression (e.g., Cogley and Sargent, 2008). Much of the motivation for 
economics as an academic discipline stems from the desire to explain 
phenomena like unemployment, boom and bust cycles, and financial 
crises, but dominant theoretical models exclude many of the aspects of 
the economy that will likely lead to a crisis. Confining theoretical 
models to `normal' times without consideration of such defects might 
seem contradictory to the focus that the average taxpayer would expect 
of the scientists on his payroll.
    This failure has deep methodological roots. The often heard 
definition of economics--that it is concerned with the `allocation of 
scarce resources'--is short-sighted and misleading. It reduces 
economics to the study of optimal decisions in well-specified choice 
problems. Such research generally loses track of the inherent dynamics 
of economic systems and the instability that accompanies its complex 
dynamics. Without an adequate understanding of these processes, one is 
likely to miss the major factors that influence the economic sphere of 
our societies. This insufficient definition of economics often leads 
researchers to disregard questions about the coordination of actors and 
the possibility of coordination failures. Indeed, analysis of these 
issues would require a different type of mathematics than that which is 
generally used now by many prominent economic models.
    Many of the financial economists who developed the theoretical 
models upon which the modern financial structure is built were well 
aware of the strong and highly unrealistic restrictions imposed on 
their models to assure stability. Yet, financial economists gave little 
warning to the public about the fragility of their models,\4\ even as 
they saw individuals and businesses build a financial system based on 
their work. There are a number of possible explanations for this 
failure to warn the public. One is a ``lack of understanding'' 
explanation--the researchers did not know the models were fragile. We 
find this explanation highly unlikely; financial engineers are 
extremely bright, and it is almost inconceivable that such bright 
individuals did not understand the limitations of the models. A second, 
more likely explanation, is that they did not consider it their job to 
warn the public. If that is the cause of their failure, we believe that 
it involves a misunderstanding of the role of the economist, and 
involves an ethical breakdown. In our view, economists, as with all 
scientists, have an ethical responsibility to communicate the 
limitations of their models and the potential misuses of their 
research. Currently, there is no ethical code for professional economic 
scientists. There should be one.
---------------------------------------------------------------------------
    \4\ Indeed, few researchers explored the consequences of a 
breakdown of their assumptions, even though this was rather likely.
---------------------------------------------------------------------------
    In the following pages, we identify some major areas of concern in 
theory and applied methodology and point out their connection to crisis 
phenomena. We also highlight some promising avenues of study that may 
provide guidance for future researchers.

2. Models (or the Use of Models) as a Source of Risk

    The economic textbook models applied for allocation of scarce 
resources are predominantly of the Robinson Crusoe (representative 
agent) type. Financial market models are obtained by letting Robinson 
manage his financial affairs as a sideline to his well-considered 
utility maximization over his (finite or infinite) expected lifespan 
taking into account with correct probabilities all potential future 
happenings. This approach is mingled with insights from Walrasian 
general equilibrium theory, in particular the finding of the Arrrow-
Debreu two-period model that all uncertainty can be eliminated if only 
there are enough contingent claims (i.e., appropriate derivative 
instruments). This theoretical result (a theorem in an extremely 
stylized model) underlies the common belief that the introduction of 
new classes of derivatives can only be welfare increasing (a view 
obviously originally shared by former Fed Chairman Greenspan). It is 
worth emphasizing that this view is not an empirically grounded belief 
but an opinion derived from a benchmark model that is much too abstract 
to be confronted with data.
    On the practical side, mathematical portfolio and risk management 
models have been the academic backbone of the tremendous increase of 
trading volume and diversification of instruments in financial markets. 
Typically, new derivative products achieve market penetration only if a 
certain industry standard has been established for pricing and risk 
management of these products. Mostly, pricing principles are derived 
from a set of assumptions on an `appropriate' process for the 
underlying asset, (i.e., the primary assets on which options or 
forwards are written) together with an equilibrium criterion such as 
arbitrage-free prices. With that mostly comes advice for hedging the 
inherent risk of a derivative position by balancing it with other 
assets that neutralize the risk exposure. The most prominent example is 
certainly the development of a theory of option pricing by Black and 
Scholes that eventually (in the eighties) could even be implemented on 
pocket calculators. Simultaneously with Black-Scholes option pricing, 
the same principles led to the widespread introduction of new 
strategies under the heading of portfolio insurance and dynamic hedging 
that just tried to implement a theoretically risk-free portfolio 
composed of both assets and options and keep it risk-free by frequent 
rebalancing after changes of its input data (e.g., asset prices). For 
structured products for credit risk, the basic paradigm of derivative 
pricing--perfect replication--is not applicable so that one has to rely 
on a kind of rough-and-ready evaluation of these contracts on the base 
of historical data. Unfortunately, historical data were hardly 
available in most cases which meant that one had to rely on simulations 
with relatively arbitrary assumptions on correlations between risks and 
default probabilities. This makes the theoretical foundations of all 
these products highly questionable--the equivalent to building a 
building of cement of which you weren't sure of the components. The 
dramatic recent rise of the markets for structured products (most 
prominently collateralized debt obligations and credit default swaps--
CDOs and CDSs) was made possible by development of such simulation-
based pricing tools and the adoption of an industry-standard for these 
under the lead of rating agencies. Barry Eichengreen (2008) rightly 
points out that the ``development of mathematical methods designed to 
quantify and hedge risk encouraged commercial banks, investment banks 
and hedge funds to use more leverage'' as if the very use of the 
mathematical methods diminished the underlying risk. He also notes that 
the models were estimated on data from periods of low volatility and 
thus could not deal with the arrival of major changes. Worse, it is our 
contention that such major changes are endemic to the economy and 
cannot be simply ignored.
    What are the flaws of the new unregulated financial markets which 
have emerged? As we have already pointed out in the introduction, the 
possibility of systemic risk has not been entirely ignored but it has 
been defined as lying outside the responsibility of market 
participants. In this way, moral hazard concerning systemic risk has 
been a necessary and built-in attribute of the system. The neglect of 
the systemic part in the `normal mode of operation,' of course, implies 
that external effects are not taken properly into account and that in 
tendency, market participants will ignore the influence of their own 
behavior on the stability of the system. The interesting aspect is more 
that this was a known and accepted element of operations. Note that the 
blame should not only fall on market participants, but also on the 
deliberate ignoring of the systemic risk factors or the failure to at 
least point them out to the public amounts to a sort of academic `moral 
hazard.'
    There are some additional aspects as well: asset-pricing and risk 
management tools are developed from an individualistic perspective, 
taking as given (ceteris paribus) the behavior of all other market 
participants. However, popular models might be used by a large number 
or even the majority of market participants. Similarly, a market 
participant (e.g., the notorious Long-Term Capital Management) might 
become so dominant in certain markets that the ceteris paribus 
assumption becomes unrealistic. The simultaneous pursuit of identical 
micro strategies leads to synchronous behavior and mechanic contagion. 
This simultaneous application might generate an unexpected macro 
outcome that actually jeopardizes the success of the underlying micro 
strategies. A perfect illustration is the U.S. stock market crash of 
October 1987. Triggered by a small decrease of prices, automated 
hedging strategies produced an avalanche of sell orders that out of the 
blue led to a fall in U.S. stock indices of about 20 percent within one 
day. With the massive sales to rebalance their portfolios (along the 
lines of Black and Scholes), the relevant actors could not realize 
their attempted incremental adjustments, but rather suffered major 
losses from the ensuing large macro effect.
    A somewhat different aspect is the danger of a control illusion: 
The mathematical rigor and numerical precision of risk management and 
asset pricing tools has a tendency to conceal the weaknesses of models 
and assumptions to those who have not developed them and do not know 
the potential weakness of the assumptions and it is indeed this that 
Eichengreen emphasizes. Naturally, models are only approximations to 
the real world dynamics and partially built upon quite heroic 
assumptions (most notoriously: Normality of asset price changes which 
can be rejected at a confidence level of 99.9999 . . .. Anyone who has 
attended a course in first-year statistics can do this within minutes). 
Of course, considerable progress has been made by moving to more 
refined models with, e.g., `fat-tailed' Levy processes as their driving 
factors. However, while such models better capture the intrinsic 
volatility of markets, their improved performance, taken at face value, 
might again contribute to enhancing the control illusion of the naive 
user.
    The increased sophistication of extant models does, however, not 
overcome the robustness problem and should not absolve the modelers 
from explaining their limitations to the users in the financial 
industry. As in nuclear physics, the tools provided by financial 
engineering can be put to very different uses so that what is designed 
as an instrument to hedge risk can become a weapon of `financial mass 
destruction' (in the words of Warren Buffet) if used for increased 
leverage. In fact, it appears that derivative positions have been built 
up often in speculative ways to profit from high returns as long as the 
downside risk does not materialize. Researchers who develop such models 
can claim they are neutral academics--developing tools that people are 
free to use or not. We do not find that view credible. Researchers have 
an ethical responsibility to point out to the public when the tool that 
they developed is misused. It is the responsibility of the researcher 
to make clear from the outset the limitations and underlying 
assumptions of his models and warn of the dangers of their mechanic 
application.
    What follows from our diagnosis? Market participants and regulators 
have to become more sensitive towards the potential weaknesses of risk 
management models. Since we do not know the `true' model, robustness 
should be a key concern. Model uncertainty should be taken into account 
by applying more than a single model. For example, one could rely on 
probabilistic projections that cover a whole range of specific models 
(cf., Follmer, 2008). The theory of robust control provides a toolbox 
of techniques that could be applied for this purpose, and it is an 
approach that should be considered.

3. Unrealistic Model Assumptions and Unrealistic Outcomes

    Many economic models are built upon the twin assumptions of 
`rational expectations' and a representative agent. ``Rational 
expectations'' instructs an economist to specify individuals' 
expectations to be fully consistent with the structure of his own 
model. This concept can be thought of as merely a way to close a model. 
A behavioral interpretation of rational expectations would imply that 
individuals and the economist have a complete understanding of the 
economic mechanisms governing the world. In this sense, rational 
expectations models do not attempt to formalize individuals' actual 
expectations: specifications are not based on empirical observation of 
the expectations formation process of human actors. Thus, even when 
applied economics research or psychology provide insights about how 
individuals actually form expectations, they cannot be used within RE 
models. Leaving no place for imperfect knowledge and adaptive 
adjustments, rational expectations models are typically found to have 
dynamics that are not smooth enough to fit economic data well.\5\
---------------------------------------------------------------------------
    \5\ For a critique of rational expectations models on 
epistemological grounds, see Frydman and Goldberg (2007, 2008) and 
references therein.
---------------------------------------------------------------------------
    Technically, rational expectations models are often framed as 
dynamic programming problems in macroeconomics. But, dynamic 
programming models have serious limitations. Specifically, to make them 
analytically tractable, not more than one dynamically maximizing agent 
can be considered, and consistent expectations have to be imposed. 
Therefore, dynamic programming models are hardly imaginable without the 
assumptions of a representative agent and rational expectations. This 
has generated a vicious cycle by which the technical tools developed on 
the base of the chosen assumptions prevent economists from moving 
beyond these restricted settings and exploring more realistic 
scenarios. Note that such settings also presume that there is a single 
model of the economy, which is odd given that even economists are 
divided in their views about the correct model of the economy. While 
other currents of research do exist, economic policy advice, 
particularly in financial economics, has far too often been based 
(consciously or not) on a set of axioms and hypotheses derived 
ultimately from a highly limited dynamic control model, using the 
Robinson approach with `rational' expectations.
    The major problem is that despite its many refinements, this is not 
at all an approach based on, and confirmed by, empirical research.\6\ 
In fact, it stands in stark contrast to a broad set of regularities in 
human behavior discovered both in psychology and what is called 
behavioral and experimental economics. The corner stones of many models 
in finance and macroeconomics are rather maintained despite all the 
contradictory evidence discovered in empirical research. Much of this 
literature shows that human subjects act in a way that bears no 
resemblance to the rational expectations paradigm and also have 
problems discovering `rational expectations equilibria' in repeated 
experimental settings. Rather, agents display various forms of `bounded 
rationality' using heuristic decision rules and displaying inertia in 
their reaction to new information. They have also been shown in 
financial markets to be strongly influenced by emotional and hormonal 
reactions (see Lo et al., 2005, and Coates and Herbert, 2008). Economic 
modeling has to take such findings seriously.
---------------------------------------------------------------------------
    \6\ The historical emergence of the representative agent paradigm 
is a mystery. Ironically, it appeared over the 70s after a period of 
intense discussions on the problem of aggregation in economics (that 
basically yielded negative results such as the impossibility to 
demonstrated `nice' properties of aggregate demand or supply functions 
without imposing extreme assumptions on individual behavior). The 
representative agent appeared without methodological discussion. In the 
words of Deirdre McCloskey: ``It became a rule in the conversation of 
some economists because Tom and Bob said so.'' (personal 
communication). Today, this convention has become so strong that many 
young economists wouldn't know of an alternative way to approach 
macroeconomic issues.
---------------------------------------------------------------------------
    What we are arguing is that as a modeling requirement, internal 
consistency must be complemented with external consistency: Economic 
modeling has to be compatible with insights from other branches of 
science on human behavior. It is highly problematic to insist on a 
specific view of humans in economic settings that is irreconcilable 
with evidence.
    The `representative agent' aspect of many current models in 
macroeconomics (including macro finance) means that modelers subscribe 
to the most extreme form of conceptual reductionism (Lux and 
Westerhoff, 2009): by assumption, all concepts applicable to the macro 
sphere (i.e., the economy or its financial system) are fully reduced to 
concepts and knowledge for the lower-level domain of the individual 
agent. It is worth emphasizing that this is quite different from the 
standard reductionist concept that has become widely accepted in 
natural sciences. The more standard notion of reductionism amounts to 
an approach to understanding the nature of complex phenomena by 
reducing them to the interactions of their parts, allowing for new, 
emergent phenomena at the higher hierarchical level (the concept of 
`more is different,' cf. Anderson, 1972).
    Quite to the contrary, the representative agent approach in 
economics has simply set the macro sphere equal to the micro sphere in 
all respects. One could, indeed, say that this concept negates the 
existence of a macro sphere and the necessity of investigating 
macroeconomic phenomena in that it views the entire economy as an 
organism governed by a universal will.\7\ Any notion of ``systemic 
risk'' or ``coordination failure'' is necessarily absent from, and 
alien to, such a methodology.
---------------------------------------------------------------------------
    \7\ The conceptual reductionist approach of the representative 
agent is also remarkably different from the narrative of the `invisible 
hand' which has more the flavor of `more is different'.
---------------------------------------------------------------------------
    For natural scientists, the distinction between micro-level 
phenomena and those originating on a macro, system-wide scale from the 
interaction of microscopic units is well-known. In a dispersed system, 
the current crisis would be seen as an involuntary emergent phenomenon 
of the microeconomic activity. The conceptual reductionist paradigm, 
however, blocks from the outset any understanding of the interplay 
between the micro and macro levels. The differences between the overall 
system and its parts remain simply incomprehensible from the viewpoint 
of this approach.
    In order to develop models that allow us to deduce macro events 
from microeconomic regularities, economists have to rethink the concept 
of micro foundations of macroeconomic models. Since economic activity 
is of an essentially interactive nature, economists' micro foundations 
should allow for the interactions of economic agents. Since interaction 
depends on differences in information, motives, knowledge and 
capabilities, this implies heterogeneity of agents. For instance, only 
a sufficiently rich structure of connections between firms, households 
and a dispersed banking sector will allow us to get a grasp on 
``systemic risk,'' domino effects in the financial sector, and their 
repercussions on consumption and investment. The dominance of the 
extreme form of conceptual reductionism of the representative agent has 
prevented economists from even attempting to model such all important 
phenomena. It is the flawed methodology that is the ultimate reason for 
the lack of applicability of the standard macro framework to current 
events.
    Since most of what is relevant and interesting in economic life has 
to do with the interaction and coordination of ensembles of 
heterogeneous economic actors, the methodological preference for single 
actor models has extremely handicapped macroeconomic analysis and 
prevented it from approaching vital topics. For example, the recent 
surge of research in network theory has received relatively scarce 
attention in economics. Given the established curriculum of economic 
programs, an economist would find it much more tractable to study 
adultery as a dynamic optimization problem of a representative husband, 
and derive the optimal time path of marital infidelity (and publish his 
exercise) rather than investigating financial flows in the banking 
sector within a network theory framework. This is more than unfortunate 
in view of the network aspects of interbank linkages that have become 
apparent during the current crisis.
    In our view, a change of focus is necessary that takes seriously 
the regularities in expectation formation revealed by behavioral 
research and, in fact, gives back an independent role to expectations 
in economic models. It would also be fallacious to only replace the 
current paradigm by a representative `non-rational' actor (as it is 
sometimes done in recent literature). Rather, an appropriate micro 
foundation is needed that considers interaction at a certain level of 
complexity and extracts macro regularities (where they exist) from 
microeconomic models with dispersed activity.
    Once one acknowledges the importance of empirically based 
behavioral micro foundations and the heterogeneity of actors, a rich 
spectrum of new models becomes available. The dynamic co-evolution of 
expectations and economic activity would allow one to study out-of-
equilibrium dynamics and adaptive adjustments. Such dynamics could 
reveal the possibility of multiplicity and evolution of equilibria 
(e.g., with high or low employment) depending on agents' expectations 
or even on the propagation of positive or negative `moods' among the 
population. This would capture the psychological component of the 
business cycle which--though prominent in many policy-oriented 
discussions--is never taken into consideration in contemporary 
macroeconomic models.
    It is worth noting that understanding the formation of such low-
level equilibria might be much more valuable in coping with major 
`efficiency losses' by mass unemployment than the pursuit of small 
`inefficiencies' due to societal decisions on norms such as shop 
opening times. Models with interacting heterogeneous agents would also 
open the door to the incorporation of results from other fields: 
network theory has been mentioned as an obvious example (for models of 
networks in finance see Allen and Babus, 2008). `Self-organized 
criticality' theory is another area that seems to have some appeal for 
explaining boom-and-bust cycles (cf. Scheinkman and Woodford, 1992). 
Incorporating heterogeneous agents with imperfect knowledge would also 
provide a better framework for the analysis of the use and 
dissemination of information through market operations and more direct 
links of communication. If one accepts that the dispersed economic 
activity of many economic agents could be described by statistical 
laws, one might even take stock of methods from statistical physics to 
model dynamic economic systems (cf. Aoki and Yoshikawa, 2007; Lux, 
2009, for examples).

4. Robustness and Data-Driven Empirical Research

    Currently popular models (in particular: dynamic general 
equilibrium models) do not only have weak micro foundations, their 
empirical performance is far from satisfactory (Juselius and Franchi, 
2007). Indeed, the relevant strand of empirical economics has more and 
more avoided testing their models and has instead turned to calibration 
without explicit consideration of goodness-of-fit.\8\ This calibration 
is done using ``deep economic parameters'' such as parameters of 
utility functions derived from microeconomic studies. However, at the 
risk of being repetitive, it should be emphasized that micro parameters 
cannot be used directly in the parameterization of a macroeconomic 
model. The aggregation literature is full of examples that point out 
the possible ``fallacies of composition.'' The ``deep parameters'' only 
seem sensible if one considers the economy as a universal organism 
without interactions. If interactions are important (as it seems to us 
they are), the restriction of the parameter space imposed by using 
micro parameters is inappropriate.
---------------------------------------------------------------------------
    \8\ It is pretty obvious how the currently popular class of dynamic 
general equilibrium models would have to `cope' with the current 
financial crisis. It will be covered either by a dummy or it will have 
to be interpreted as a very large negative stochastic shock to the 
economy, i.e., as an event equivalent to a large asteroid strike.
---------------------------------------------------------------------------
    Another concern is nonstationarity and structural shifts in the 
underlying data. Macro models, unlike many financial models, are often 
calibrated over long time horizons which include major changes in the 
regulatory framework of the countries investigated. Cases in question 
are the movements between different exchange rate regimes and the 
deregulation of financial markets over the 70s and 80s. In summary, it 
seems to us that much of contemporary empirical work in macroeconomics 
and finance is driven by the pre-analytic belief in the validity of a 
certain model. Rather than (mis)using statistics as a means to 
illustrate these beliefs, the goal should be to put theoretical models 
to scientific test (as the naive believer in positive science would 
expect).
    The current approach of using pre-selected models is problematic 
and we recommend a more data-driven methodology. Instead of starting 
out with an ad-hoc specification and questionable ceteris paribus 
assumptions, the key features of the data should be explored via data-
analytical tools and specification tests. David Hendry provides a well-
established empirical methodology for such exploratory data analysis 
(Hendry, 1995, 2009) as well as a general theory for model selection 
(Hendry and Krolzig, 2005); clustering techniques such as projection 
pursuit (e.g., Friedman, 1987) might provide alternatives for the 
identification of key relationships and the reduction of complexity on 
the way from empirical measurement to theoretical models. Co-integrated 
VAR models could provide an avenue towards identification of robust 
structures within a set of data (Juselius, 2006), for example, the 
forces that move equilibria (pushing forces, which give rise to 
stochastic trends) and forces that correct deviations from equilibrium 
(pulling forces, which give rise to long-run relations). Interpreted in 
this way, the `general-to-specific' empirical approach has a good 
chance of nesting a multi-variate, path-dependent data-generating 
process and relevant dynamic macroeconomic theories. Unlike approaches 
in which data are silenced by prior restrictions, the Co-integrated VAR 
model gives the data a rich context in which to speak freely (Hoover et 
al., 2008).
    A chain of specification tests and estimated statistical models for 
simultaneous systems would provide a benchmark for the subsequent 
development of tests of models based on economic behavior: significant 
and robust relations within a simultaneous system would provide 
empirical regularities that one would attempt to explain, while the 
quality of fit of the statistical benchmark would offer a confidence 
band for more ambitious models. Models that do not reproduce (even) 
approximately the quality of the fit of statistical models would have 
to be rejected (the majority of currently popular macroeconomic and 
macro finance models would not pass this test). Again, we see here an 
aspect of ethical responsibility of researchers: Economic policy models 
should be theoretically and empirically sound. Economists should avoid 
giving policy recommendations on the base of models with a weak 
empirical grounding and should, to the extent possible, make clear to 
the public how strong the support of the data is for their models and 
the conclusions drawn from them.

5. A Research Agenda to Cope with Financial Fragility

    The notion of financial fragility implies that a given system might 
be more or less susceptible to produce crises. It seems clear that 
financial innovations have made the system more fragile. Apparently, 
the existing linkages within the worldwide, highly connected financial 
markets have generated the spill-overs from the U.S. sub-prime problem 
to other layers of the financial system. Many financial innovations had 
the effect of creating links between formerly unconnected players. All 
in all, the degree of connectivity of the system has probably increased 
enormously over the last decades. As is well known from network theory 
in natural sciences, a more highly connected system might be more 
efficient in coping with certain tasks (maybe distributing risk 
components), but will often also be more vulnerable to shocks and--
systemic failure! The systematic analysis of network vulnerability has 
been undertaken in the computer science and operations research 
literature (see e.g., Criado et al., 2005). Such aspects have, however, 
been largely absent from discussions in financial economics. The 
introduction of new derivatives was rather seen through the lens of 
general equilibrium models: more contingent claims help to achieve 
higher efficiency. Unfortunately, the claimed efficiency gains through 
derivatives are merely a theoretical implication of a highly stylized 
model and, therefore, have to count as a hypothesis. Since there is 
hardly any supporting empirical evidence (or even analysis of this 
question), the claimed real-world efficiency gains from derivatives are 
not justified by true science. While the economic argument in favor of 
ever new derivatives is more one of persuasion rather than evidence, 
important negative effects have been neglected. The idea that the 
system was made less risky with the development of more derivatives led 
to financial actors taking positions with extreme degrees of leverage 
and the danger of this has not been emphasized enough.
    As we have mentioned, one neglected area is the degree of 
connectivity and its interplay with the stability of the system (see 
Boesch et al., 2006). We believe that it will be necessary for 
supervisory authorities to develop a perspective on the network aspects 
of the financial system, collect appropriate data, define measures of 
connectivity and perform macro stress testing at the system level. In 
this way, new measures of financial fragility would be obtained. This 
would also require a new area of accompanying academic research that 
looks at agent-based models of the financial system, performs scenario 
analyses and develops aggregate risk measures. Network theory and the 
theory of self-organized criticality of highly connected systems would 
be appropriate starting points.
    The danger of systemic risk means that regulation has to be 
extended from individualistic (regulation of single institutions which 
of course, is still crucial) to system wide regulation. In the sort of 
system which is prone to systemic crisis, regulation also has to have a 
systemic perspective. Academic researchers and supervisory authorities 
thus have to look into connections within the financial sector and to 
investigate the repercussions of problems within one institute on other 
parts of the system (even across national borders). Certainly, before 
deciding about the bail-out of a large bank, this implies an 
understanding of the network. One should know whether its bankruptcy 
would lead to widespread domino effects or whether contagion would be 
limited. It seems to us that what regulators provide currently is far 
from a reliable assessment of such after effects.
    Such analysis has to be supported by more traditional approaches: 
Leverage of financial institutions rose to unprecedented levels prior 
to the crisis, partly by evading Basle II regulations through special 
investment vehicles (SIVs). The hedge fund market is still entirely 
unregulated. The interplay between leverage, connectivity and system 
risk needs to be investigated at the aggregate level. It is highly 
likely, that extreme leverage levels of interconnected institutions 
will be found to impose unacceptable social risk on the public. Prudent 
capital requirements would be necessary and would require a solid 
scientific investigation of the above aspects rather than a pre-
analytic laissez-faire attitude.
    We also have to re-investigate the informational role of financial 
prices and financial contracts. While trading in stock markets is 
usually interpreted as at least in part transmitting information, this 
information transmission seems to have broken down in the case of 
structured financial products. It seems that securitization has rather 
led to a loss of information by anonymous intermediation (often 
multiple) between borrowers and lenders. In this way, the informational 
component has been outsourced to rating agencies and typically, the 
buyer of CDO tranches would not have spent any effort himself on 
information acquisition concerning his far away counterparts. However, 
this centralized information processing instead of the dispersed one in 
traditional credit relationships might lead to a severe loss of 
information. As it turned out, standard loan default models failed 
dramatically in recent years (Rajan et al., 2008). It should also be 
noted that the price system itself can exacerbate the difficulties in 
the financial market (see Hellwig, 2008). One of the reasons for the 
sharp fall in the asset valuations of major banks was not only the loss 
on the assets on which their derivatives were based, but also the 
general reaction of the markets to these assets. As markets became 
aware of the risk involved, all such assets were written down and it 
was in this way that a small sector of the market ``contaminated'' the 
rest. Large parts of the asset holdings of major banks abruptly lost 
much of their value. Thus the price system itself can be destabilizing 
as expectations change.
    On the macroeconomic level, it would be desirable to develop early 
warning schemes that indicate the formation of bubbles. Combinations of 
indicators with time series techniques could be helpful in detecting 
deviations of financial or other prices from their long-run averages. 
Indication of structural change (particularly towards non-stationary 
trajectories) would be a signature of changes of the behavior of market 
participants of a bubble-type nature.

6. Conclusions

    The current crisis might be characterized as an example of the 
final stage of a well-known boom-and-bust pattern that has been 
repeated so many times in the course of economic history. There are, 
nevertheless, some aspects that make this crisis different from its 
predecessors: First, the preceding boom had its origin--at least to a 
large part--in the development of new financial products that opened up 
new investment possibilities (while most previous crises were the 
consequence of over-investment in new physical investment 
possibilities). Second, the global dimension of the current crisis is 
due to the increased connectivity of our already highly interconnected 
financial system. Both aspects have been largely ignored by academic 
economics. Research on the origin of instabilities, over-investment and 
subsequent slumps has been considered as an exotic side track from the 
academic research agenda (and the curriculum of most economics 
programs).This, of course, was because it was incompatible with the 
premise of the rational representative agent. This paradigm also made 
economics blind with respect to the role of interactions and 
connections between actors (such as the changes in the network 
structure of the financial industry brought about by deregulation and 
introduction of new structured products). Indeed, much of the work on 
contagion and herding behavior (see Banerjee, 1992, and Chamley, 2002) 
which is closely connected to the network structure of the economy has 
not been incorporated into macroeconomic analysis.
    We believe that economics has been trapped in a sub-optimal 
equilibrium in which much of its research efforts are not directed 
towards the most prevalent needs of society. Paradoxically self-
reinforcing feedback effects within the profession may have led to the 
dominance of a paradigm that has no solid methodological basis and 
whose empirical performance is, to say the least, modest. Defining away 
the most prevalent economic problems of modern economies and failing to 
communicate the limitations and assumptions of its popular models, the 
economics profession bears some responsibility for the current crisis. 
It has failed in its duty to society to provide as much insight as 
possible into the workings of the economy and in providing warnings 
about the tools it created. It has also been reluctant to emphasize the 
limitations of its analysis. We believe that the failure to even 
envisage the current problems of the worldwide financial system and the 
inability of standard macro and finance models to provide any insight 
into ongoing events make a strong case for a major reorientation in 
these areas and a reconsideration of their basic premises.

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                      Biography for David Colander
    David Colander has been the Christian A. Johnson Distinguished 
Professor of Economics at Middlebury College, Middlebury, Vermont since 
1982. He has authored, co-authored, or edited over 40 books (including 
a principles and intermediate macro text) and 150 articles on a wide 
range of topics. His books have been, or are being, translated into a 
number of different languages, including Chinese, Bulgarian, Polish, 
Italian, and Spanish.
    He received his Ph.D. from Columbia University and has taught at 
Columbia University, Vassar College, the University of Miami as well as 
Middlebury. He has also been a consultant to Time-Life Films, a 
consultant to Congress, a Brookings Policy Fellow, and a Visiting 
Scholar at Nuffield College, Oxford. In 2001-2002 he was the Kelly 
Professor of Distinguished Teaching at Princeton University.
    He is a former President of both the Eastern Economic Association 
and History of Economic Thought Society and is, or has been, on the 
editorial boards of the Journal of the History of Economic Thought, 
Journal of Economic Methodology, Eastern Economic Journal, and The 
Journal of Socioeconomics, and Journal of Economic Perspectives. He is 
a member of the AEA Committee on Economic Education.

                               Discussion

    Chairman Miller. I want to thank all of you.

                  Appropriate Uses of Financial Models

    Let me begin this panel with a question of the earlier 
panel. Some of those responsible, involved in developing 
economic modeling now say that the fundamental problem was that 
the model was wrong, there is more data. I don't think anyone 
thinks that models should be prohibited or people should be 
prohibited from acting on their models for their investment 
decisions or whatever. The extent to which it can be used, it 
should be used for regulation, safety and soundness regulation. 
Do any of you--what do each of you think about whether the 
models may be improved and will become reliable, sufficiently 
reliable to base capital requirements on--or do you think that 
it is so inherently unpredictable that economic forecasts will 
never become like predicting the movements of the planets, that 
it may be useful for seeing if a financial institution is 
headed towards trouble, but not to say it is got nothing to 
worry about? Any of you. Dr. Berman.
    Dr. Berman. Thank you. I think models definitely have a 
very significant role, not just in finance but in society in 
general. The question is, what aspect of a model are you 
looking to use. Certain models are designed to predict the 
future. That is always very difficult to do. We can predict 
where the planets are going to go but it is very difficult to 
predict where the stock market is going to go today. That is a 
very small portion of what financial modeling is about, 
predicting the future. Unfortunately, that is what folks glean 
onto when they start thinking about capital requirements. A 
much larger portion of what modeling is about is understanding: 
if something happens to X, what happens to Y? You don't have to 
predict the future in order to do that, you just need to know 
the relationships between two different things.
    Let's take an excellent example. The world's largest 
insurance company entered into massive amounts of credit 
default swaps that ultimately were responsible for their 
demise. The bet that they took might have turned out to be the 
best bet that they ever could have made. We don't know because 
those CDSs are probably still out there to a certain extent. 
But they failed to account for the fact that, what would happen 
if there was a small dip in the value of these, and my 
counterparty asks for collateral? That's not a matter of 
predicting the future, that's just understanding this is the 
way that market works. When the value falls, your counterparty 
asks for collateral. They missed that aspect of the model. That 
had nothing to do with predicting the future but just in 
understanding how that worked, and that ultimately led to the 
demise. And you see that pervasive through many, many different 
types of models throughout the system.
    Chairman Miller. It does remind me of Yoga Berra's wisdom 
that predictions are difficult, especially about the future. 
Mr. Rickards?
    Mr. Rickards. Mr. Chairman, I think it is interesting that 
a number of Members and the witnesses today have referred to 
planetary motion as an example of models that work, but I will 
remind everyone that from 200 B.C. to 1500 A.D., the model of 
the universe was a geocentric model in which the sun revolved 
around the Earth. And it was obvious because you woke up in the 
morning and the sun came up over here and went down over there, 
and that was not just a religious belief, that was actually a 
scientific belief, and many brilliant mathematicians worked for 
centuries to write the equations. They weren't automated, of 
course, but they wrote those models, and when people began 
observing data from improved telescopes that didn't conform to 
that model, they said well, we just need to tweak the model a 
little bit. Instead of these cycles, they created epicycles. 
They were little twirls within the big twirls, and they kept 
going down that path. The model was completely wrong. Actually, 
the model was right, the paradigm was wrong. The understanding 
of how the world worked was wrong. The sun did not revolve 
around the Earth; the Earth revolved around the sun.
    That is my view of today. You can tweak it, you can improve 
it, you can separate the so-called fat tail and zero in on that 
tail, and there is a complex method called GARCH, Generalized 
Autoregressive Condition Heteroskedasticity and variations on 
that. They are all wrong because the paradigm is wrong, because 
the risk is not normally distributed in the first place. So I 
think these are fatally flawed.
    If a hedge fund that is non-systemically important wants to 
use this model, that is fine. They can use voodoo, as far as I 
am concerned, but if you are talking about a bank or regulated 
financial institution, they should be prohibited because they 
don't work.
    Chairman Miller. Mr. Whalen.
    Mr. Whalen. I agree with them, and also I think Dr. Berman 
made this point. When you are talking about safety and 
soundness, you don't want to look at a tactical short-term loss 
possibility, you want to look at the worst case, and we see 
that now with the banking industry. By next year, I think we 
are going to be looking at a double- or triple-digit deficit in 
the insurance fund, and banks are going to have to pay that 
back. No one anticipated that magnitude of loss. So what you 
have is, on the one hand, a marketplace which is very short 
term. They are working on today's earnings, next quarter's 
earnings, what have you, and yet over time, since Glass-
Steagall, we have slowly eroded the limits on risk taking. So 
the models--whether they worked or not is kind of irrelevant. 
We slowly allowed banks to take more and more risk. So I think 
what we have to do first is say, what risk do we want the 
utility side of this industry, the depository, the lending, the 
cash distribution part of banks, to take, and what part do we 
want to force, for example, into the hedge fund community, 
which is a perfect place for risk taking.
    You know, we can't come up with the answer to your 
question, Mr. Chairman, as to safety and soundness and capital 
adequacy, unless we quantify the risks that the institutions 
take. I will give you an example. Citigroup in 1991 peaked at 
about three and a half percent charge-offs versus total loans. 
I think they are going to get up to about six this time. Now, 
can you imagine the public and market reaction when the large 
money centers get up to something like two, maybe two and a 
half times their 1990 loss rate? But that is how severe of a 
skew we are seeing. In the Depression, we got up to five 
percent losses on total loans, so we are closing in on the 
1930s. I don't think it will be quite that bad, but we will see 
how long we stay there. That is the other question, how long 
will we see those losses? Will it be two quarters or four? This 
is the kind of question you need to answer very precisely but 
the only way you can answer your question about capital and 
safety and soundness is if you first quantify the risk taking, 
because otherwise I don't think you can get an answer.
    And by the way, we wrote about this last week. I don't 
think you can ask the markets to give more capital to banks. I 
think the G-20 and Secretary Geithner are wrong. You have to 
reduce the risk taking, because I don't think the markets would 
let J.P. Morgan have 20 percent capital assets because the 
returns will be too low. It would be low single digits at best 
and on a risk-adjusted basis I think they would be negative. 
This, by the way, is the context you ought to bear in mind. 
Most of the large banks on a risk-adjusted basis really aren't 
that profitable. It is only the super-normal returns that they 
get from OTC derivatives, investment banking, proprietary 
trading that helped the whole enterprise look profitable. If 
you look at the retail side, the cash securities trading, it is 
barely profitable, really, and that is why you have seen the 
changes in the industry that you have.
    Chairman Miller. Dr. Colander.
    Dr. Colander. In answer to your question, in social science 
you will never get the amount of exactness that you will get in 
natural sciences, mainly because the agent in social science is 
not an atom which sort of follows a set of rules, you know, it 
is a human being, it is an agent who will try to do everything 
he can to screw you every time you are trying to control him. 
So the thought that you are going to be able to design any 
model is pretty much impossible. That being said, I think 
models can be used and have to be used. We all use models. How 
can you not sort of picture what is going on? The question is, 
what type of models, and how many different models do you have 
in your mind, and how quickly can you jump from one model to 
another and recognize we have really moved there, and that is 
the issue that I think people are talking about.

        Proposals for Avoiding Recurrences of Financial Problems

    Chairman Miller. Interesting set of answers. Mr. Rickards 
did mention at least three proposals for avoiding a catastrophe 
like what we have had. Do the rest of you have specific 
proposals as well of how we avoid this again? I think the 
financial industry is already treating what happened last 
September, October--we are still in it--as a hiccup, something 
that was a fluke, will not happen again, we don't have to 
change conduct very much. I assume all of you don't agree with 
that, but what is it that we should do?
    Dr. Berman. I think there are two courses of action. I 
think most of the discussion on regulatory capital is trying to 
solve a symptom as opposed to the cure itself. A good portion 
of the funds come from investors who are feeding the big 
bonuses, let's say, at large banks, so while there is lots of 
talk about the restriction on bonuses and whether we should 
hold people legally liable for clawbacks, et cetera, the fact 
is that the fuel is there. That fuel causes crisis. The fuel is 
done generally by greed, but mostly uninformed greed. Probably 
the number one thing that regulators can enforce is better 
transparency and better disclosure on finance itself. If more 
people understood what they were actually buying, less people 
would buy these things. Wall Street is a marketing arm as are 
all commercial companies. Their practices came from the desires 
of people to invest in those products, invest in those 
services, and invest in the companies themselves. If we don't 
like those practices, then we should make it clear what those 
practices are and let investors choose whether or not they want 
to engage in those. That would dampen further--well, certainly 
it would help reverse this crisis a bit, and it would certainly 
dampen the ability for the market to even create these very, 
very large bubbles in the first place.
    Mr. Whalen. One simple thing that I would add to Dr. 
Berman's comment, and speaking as an investment banker, make 
the lawyers your friend. What you want to do is, instead of 
allowing banks to bring these structured assets and derivatives 
in an unregistered forum, you force them to register with the 
SEC, and what that does is two things. First off, the lawyers 
of the deals will not allow more than a certain degree of 
complexity, because once that deal is registered, it can be 
purchased by all investors, and so they will force simplicity 
onto their banks. Because otherwise they will get sued, and the 
trial lawyers will enforce this, believe me. Remember, most of 
the toxic waste, the complex structured assets, were all done 
at private placements. You can't even get a copy of the 
prospectus.
    The second thing I would tell you is that, you know, in 
terms of overall market structure, we've got to decide whether 
or not, going back to my earlier comment, we are going to allow 
people to contrive of any security for any investor that 
doesn't have some rational basis, some objective basis in terms 
of valuation, because that is really the key problem that we 
have all faced over the last couple years, is valuation. When 
the investors realized that they couldn't get a bid from the 
dealer that sold them the CDO and they couldn't value it by 
going to anybody in the cottage community, they just withdrew 
from the market and we had a liquidity problem. If you force 
these deals to be registered, guess what? Every month when the 
servicer data for the underlying becomes available, they will 
have to drop an 8K and then that data will be available to the 
community for free. We won't have to spend hundreds of 
thousands of dollars a year to buy servicer data so that we can 
manually construct models to try and understand how a very 
complicated mortgage security, for example, is going to 
perform. You will open up the transparency so that the cottage 
industry that currently supports valuation for simple 
structures, which are very easy to value--credit card deals, 
auto deals--there is really no problem with these and they are 
coming back, by the way. You are starting to see volume come 
back to that market. It is about disclosure. I think Dr. Berman 
says it very well.

                            Abuse of the VaR

    Chairman Miller. Dr. Colander? You don't have to speak on 
every topic if you don't want to.
    Dr. Berman, everyone agrees that the VaR can be abused, has 
been abused, was certainly used foolishly in lowering capital 
requirements for investment banks. Without revealing 
proprietary information, can you give us some of the ways that 
you have seen firms abuse the VaR, or try to abuse the VaR 
apart from regulatory matters?
    Dr. Berman. Sure. I don't think that VaR in itself was 
purposefully or willfully abused. VaR is a model that requires 
a significant number of assumptions. For example, if I buy a 
product, such as an option, then I should assume that if the 
value of the stock goes down, then the value of the option will 
go down. If I write that option, so I sell it, then if the 
stock goes up, I can lose a lot of money. If I don't have the 
desire or the technology or the capability or the incentive to 
bother being careful about that, then I will assume that, if 
the stock goes up, I will make or lose a limited amount of 
money. That is a very, very poor assumption, which I think we 
have heard a lot today. If you take many of those poor 
assumptions and you add them up, you wind up getting VaR 
numbers, and not just VaR numbers but numbers of all sorts of 
different models that wind up being all but meaningless because 
of so many small poor assumptions that have added up into 
something that is just wildly incorrect. But folks like to 
believe their own numbers, especially when those numbers allow 
them to do things that they weren't able to do before. So it 
wasn't a willful misconduct as much as a carelessness, given 
the incentive structures that are out there today.
    Chairman Miller. Anyone else? Mr. Rickards.

     Past Congressional Attempts to Regulate the Financial Industry

    Mr. Rickards. Yes, Mr. Chairman, I just want to say that my 
recommendations, if we are going back to something like Glass-
Steagall, there was more to that than just a walk down Memory 
Lane. I am not saying, gee, the system today has obviously 
failed, let us go back to what we had before. I actually 
derived these from my own research into the power load 
relationship that I talked about earlier, which is that scale--
as scale goes up, as you triple or quadruple or increase by 
five or ten times the system, you are increasing risk by a 
factor of 100, 1,000, 10,000. That is the non-linear 
relationship that Dr. Taleb talked about earlier, and I very 
quickly came to the conclusion--well, if that is the problem, 
then descaling is the answer, and Glass-Steagall is an example 
of that. There is a little bit, I think, of--you know, easy 
with hindsight, but perhaps some arrogance in the 1998-2001 
period where I think Members looked back at the Congress in the 
1930s and said, you know, they were Neanderthals, they didn't 
understand modern finance, they created this system. The 
Members of Congress in the 1930s had actually lived through 
something very similar to what we are living through now and 
this was their solution. They actually had firsthand 
experience.
    Now, did a Member of Congress in 1934 understand fractal 
mathematics? No, it was invented in the 1960s. But they had an 
intuitive feel for the risks and I think their solution--we had 
a system that worked from 1934 to 1999, for 65 years. When the 
savings & loan (S&L) crisis happened in the early 1990s, it 
didn't take hedge funds with it. When we had the banking crisis 
in the mid-1980s, it didn't affect the S&L industry or it 
didn't affect investment banking. We were compartmented, and 
that is what saved the system. We have torn down all the walls. 
Commercial banks look like hedge funds. Investment banks look 
like hedge funds. Hedge funds originate commercial loans. It is 
a big business for them. So when everyone else is in everyone 
else's business, should it come as any surprise that if one 
part fails, it all fails.
    Chairman Miller. Thank you.

        Should a Government Agency Test Financial Products for 
                              Usefulness?

    Dr. Taleb earlier suggested that there be something like 
the FDA that approves--actually it was not clear to me in the 
earlier panel to what extent they were calling for government 
conduct or setting rules by government that would prohibit 
things, or just people not doing them because they were stupid, 
but assuming we are talking about rules that may be set by 
government, Dr. Taleb suggested that the FDA reviews drugs to 
see if they do any good, they don't allow--the FDA doesn't 
allow patent medicines mixed up in a bathtub to be sold to cure 
cancer anymore. You could do all that you wanted in the 1930s. 
You can't do it now. And a great many of the financial 
instruments that led to all this have no readily apparent 
social utility and create enormous risk that is dimly 
understood by even the people who are selling them, certainly 
the CEOs and the boards of directors of their institutions. 
Should we be reviewing financial instruments for whether they 
have any useful purpose, and can you give examples of 
instruments that have no apparent purpose and have done great 
damage? Mr. Whalen.
    Mr. Whalen. Well, I think the short answer is no. I am not 
a big fan of regulation. I don't think the government has the 
competency to analyze complex securities in the first place. 
You would have to hire the people that do it. I think it is 
better to let the market discipline this behavior. Large buy-
side investors, who I would remind you are probably the 
survivors of this period, they are the ones with the money, 
they tell the sell side what they want and they are going to 
tell the rating agencies what they want to see as well, and if 
you increase the liability to the issuers by forcing 
disclosure, by forcing SEC registration, you are going to see 
simplicity. Because otherwise my friends at the trial bar are 
going to come over the hill like the barbarians, and they are 
going to feast, and I think that is the way you do it. You 
don't want to get the government into a role where they have to 
make judgments about securities, because, frankly, who would 
you ask? The folks at the Fed? I mean, the Fed is populated by 
monetary economists who couldn't even work on Wall Street. I 
mean, I love them dearly, I go fishing with a lot of these 
people but I would not ever let them have any operational 
responsibility because they just don't have the competency.
    So I think we have to try and take a minimalist approach 
that is effective, and the way you do that is by making the 
issuer retain a portion of the deal that they bring so that 
they have to own some of the risk. You make them make a market 
in these securities too. They can't just abandon their clients 
when they bring some complex deal and not even make a bid for 
it. That is a big part of the problem. If you make the dealers 
retain some risk and retain responsibility, then I think you 
will see change.
    Chairman Miller. Dr. Colander.
    Dr. Colander. I wanted to expand a little bit on regulation 
from a different perspective, again, agreeing very much with 
what Mr. Whalen said, that there is a problem with government 
regulation, and we can go back and think about Glass-Steagall. 
You know, people responded to Glass-Steagall and said here is 
the problem, you know, that we deregulated. The problem was, 
during that time there was enormous technological change. We 
had to change the regulations, and now you have to--regulation 
isn't a one-time thing. It has got to be continually changed, 
and here is the problem. My students, when we asked how many 
were going on, you know, sort of--Paul Volker came up and spoke 
and he said, you know, big audience, ``How many of you are 
planning to go on and work for government?'' and I think two 
people raised their hand. Then he said, ``How many people are 
planning to go on to Wall Street?'' You know, you had all this 
large number, and this was a number of years ago. When my 
students coming out of Middlebury College as seniors can earn 
$150,000 to $200,000 in the first or second year and somebody 
coming into government can get, what, as a GS-8 or 9, you know, 
sort of $34,000 or something. You know, where are you going to 
go, how are you going to get the expertise to do it? And so 
what happens is, you know, you have an unfair system there, 
where no matter how much regulation you get, given the pay 
structure, given what's there, the people who are having it 
designed will be able to snow anybody who is trying to regulate 
it, and that is why very much I think you have to design it, 
not so we have to regulate it, but it is self-regulatory, and 
that, I think, is what you are hearing from people, that you 
have responsibility. If it's too big to fail, we have to 
regulate it so therefore let us see that is not too big to fail 
by making it smaller, that we structure it by the people who 
know the institutional structure, so that here you figure why 
you won't make that deal. But not for government to be coming 
in mainly because government will get beat.
    Chairman Miller. Mr. Rickards.
    Mr. Rickards. Mr. Chairman, I think the idea that there 
would be a government panel of some kind that would vet and 
approve financial products in the manner that the FDA approves 
drugs is probably not workable, probably beyond the ability of 
government. But for example, credit default swaps: There is 
actually a use for them. They are socially useful when they are 
used to hedge a position in the underlying bond, but they 
become a casino ultimately underwritten by the taxpayers when 
they are used with no insurable interest. So it is hard enough 
understanding what a credit default swap is, but to get that 
distinction just right, when it may or may not be useful, would 
be extremely difficult. But I do believe there should be a 
quarantine in the sense that--let's have these products in 
hedge funds, in long-run investors or maybe with mild leverage. 
Let us keep them out of FDIC-insured banks and other 
institutions that perform this utility function and are in 
effect gambling with taxpayers' money.
    I also endorse Dr. Colander's suggestion that, in the 
National Science Foundation, in the peer review process, there 
is a rule for looking at these things, perhaps not in the 
regulatory sense of approving them but in the academic sense of 
understanding them. And I believe what Dr. Colander is 
referring to is what I call `cognitive diversity.' Let's just 
not have a bunch of economists or, for that matter, a bunch of 
physicists but let us have physicists, economists, behavioral 
scientists, psychologists and others work together. I think it 
is interesting that Dr. Kahneman at Princeton won the Nobel 
Prize in economics a few years ago. He is the world's leading 
behavioral psychologist. He wouldn't describe himself as an 
economist, but he made very valuable contributions to 
economics.
    If you get 16 Ph.D.'s in a room and they all went to one of 
four schools, let us say Chicago, MIT, Harvard and Stanford, 
and they are all fine schools, you will actually improve the 
decision-making if you ask two or three of them to leave and 
invite in the first couple people who walk down the street. You 
will lower the average IQ, but you will improve the overall 
outcome because those people will know something that the 
Ph.D.'s don't. So at the National Science Foundation level, to 
encourage that kind of collaboration I think is very valuable.
    I have actually--I am involved in a field called 
econophysics which basically is understanding economics using 
some physics tools, and I don't claim it is the answer to all 
these things but it does make some valuable contributions. But 
when I speak to--I have spoken at Los Alamos and the Applied 
Physical Laboratory, and I get a very warm reception. The 
physicists are very intrigued and they see the applications. 
When you talk to economists, they have no interest. They are 
like, what do physicists have to tell us. And I think more 
collaboration would be helpful.

              Identifying Firms That Are `Too Big to Fail'

    Chairman Miller. Mr. Rickards, you know from personal 
experience that it is not just depository institutions that are 
systemically significant. How do we identify--I think you and 
Dr. Colander both have spoken about the problem of scale. How 
do we reduce the size of institutions? How do we identify the 
ones that are systemically important, either because of their 
size or their interconnectedness, as inappropriate for the kind 
of risk taking . . . that if we assume that there are some 
institutions, most hedge funds, that can be born and die 
without any great consequence to the rest of the planet, and 
that if they want to use voodoo, they can. How do we identify 
those that we have different standards for? Mr. Whalen.
    Mr. Whalen. Well, I think there is two simple answers. 
First off, we have to revisit market share limits. You have 
already seen this in process with the FDIC because they have 
started to levy premiums against total assets, less capital, 
instead of domestic deposits. I think that is a very healthy 
change. But perhaps more important, we have to let institutions 
fail, because if you convince investors that you are going to 
put a Lehman Brothers or a Washington Mutual into bankruptcy, 
they are going to change their behavior, and I think both of 
those events were inevitable, by the way. I think it is 
ridiculous to argue that Lehman could be saved. They were for 
sale for almost a year. Nobody wanted to buy it. So, you know, 
at the end of the day, if we don't allow failure, and we don't 
inoculate our population against risk by letting them feel some 
pain from time to time, then we will repeat the mistake.
    Last point, we have got to get the Fed out of bank 
supervision. Monetary economists like big banks. They love 
them. I worked in the applications area of the Fed in New York. 
I can't recall a merger, a large bank merger that they have 
ever said no to. I worked on the ``Manny Hanny'' (Manufacturers 
Hanover Trust) transaction, I worked on the Chemical Bank 
merger, following that with Chase. In each case, you could make 
a very strong case that those were bad mergers. They destroyed 
value. And then look at Bank of America. They had to buy 
Countrywide because they were the chief lender to Countrywide's 
conduit. They had no choice. It was kind of like J.P. Morgan 
buying Bear Stearns. There really was no choice. But then we 
have the Fed slam Merrill Lynch into Bank of America to save a 
primary dealer. These are the monetary economists saying oh, 
dear, we want to have a few big primary dealers we can manage 
and deal with, it is easier for us. Well, I think that is a 
really skewed perspective. I would like to see another agency 
responsible for approving mergers of financial institutions 
that actually looks at it on an objective basis and says, is 
this a good idea, because we have got a couple mergers, Wells, 
Wachovia and Bank of America with Merrill Lynch that I am not 
sure are going to work. I think both of those institutions may 
have to be restructured and downsized significantly in the next 
couple of years.
    Chairman Miller. Mr. Rickards.

         Monitoring and Analyzing Hedge Fund Activity and Risk

    Mr. Rickards. Mr. Chairman, on the issue of what is a 
systemically important hedge fund, at the end of the day there 
will be some element of subjectivity in it--whether a $10 
billion hedge fund is systemically important, but if you have 
$9.8 billion, you are not. It will be a little bit arbitrary 
and it can't be based solely on size. It has to be based on the 
complexity. But the first step is transparency. You will never 
be able to make any informed decisions like that without good 
information, and every hedge fund manager--I have worked in 
hedge funds banks and investment banks--they will resist that 
for various reasons but I don't understand why the United 
States Government couldn't create a facility that would keep 
that information on a secure basis. We keep military secrets, 
we keep intelligence secrets, we keep other information 
confidential. You could have a firm like, you know, IBM Global 
Services that would come in, build a facility. It could be 
secure, get clear people running it, and then just say to all 
hedge funds, look, you have to give us all of your information, 
all of your positions, all of your factors in a standardized 
format, in an automated format once a week, we will keep it in 
a totally secure environment, it will not leak out, but we are 
going to take that and load it into, you know, covariance 
metrics. We are also going to do that for your firm, and we are 
going to have an idea at that point when you are taking 
systemic risks, and at that point there ought to be an ability 
to intervene. And I agree with Mr. Whalen, it should not be the 
Federal Reserve. They do a lousy job with their primary task of 
preventing inflation, and I don't know why they have been given 
all these other jobs. But there certainly would be expertise in 
the government to do that much, and then to intervene when 
necessary.
    Dr. Berman. Adding that, taking all that data, bringing it 
together----
    Chairman Miller. Dr. Berman.
    Dr. Berman.--and putting it into a large covariance matrix 
sort of sounds like VaR. I mean, that is--so you come back to 
those same questions all the time when you say how do we make 
predictions? This may sound like I am answering the question 
with the same exact question, but the best way to protect 
against this is to ask the question to the bank: what would 
happen if you failed? And then determine what the outcome to 
society or to the economy would be. It is not based on the size 
of the bank, it is based on, look at what would happen, not the 
probability but if the bank fails, if a hedge fund fails, what 
actually will wind up happening, what are the knock-on effects. 
That requires an enormous amount of transparency but you don't 
need to necessarily make the predictions about that, you just 
need to follow that thread through.
    Chairman Miller. Dr. Colander.
    Dr. Colander. One of the principles, you know, sort of 
within economics, is taxes have to have reasons and everything 
else. And I think one of the things that thinking of the 
economy as a complex system brings up is that bigness is, per 
se, bad, you know, sort of an interconnection is, so we have 
lost the sense that there can be a tax on `bigness' so that 
people can decide but it is built within that. And to start 
thinking that, here, if you have a complex system, you have got 
to keep a whole number of different elements, and the only way 
you are going to be able to do that--because there is enormous 
pressure to grow--is to somehow design within the system a 
counterweight to that, and so thinking along those lines, I 
think is something that follows thinking of the economy as a 
complex system.
    Chairman Miller. Mr. Whalen.
    Mr. Whalen. I will come back to something Mr. Broun said 
about the community banks because I think it is very important, 
and you all are going to be hearing about this a lot next year. 
If you are going to tax institutions based on risk, and I think 
that is sound, you start with the FDIC. The big banks should 
pay more than the little banks, and when we see the size of the 
hole that we have to fill in over the next, I don't know, 25 
years from this crisis, I think that is going to become a very 
compelling argument. The community bankers are going to be 
living up here next year when they start seeing the estimates 
for what they have to give up in revenue and income to fill in 
this hole. Remember, we are still paying for the S&L crisis. 
There is still debt out there that we are paying interest on. 
We are going to be paying for this crisis for 100 years. That 
is how big the numbers are. So think of that as a load on the 
economy. That is kind of the cost of modeling run amuck, and, 
you know, I am serious about this. We are going to be paying 
for this, the banking industry, consumers, investors in banks 
are going to be paying for this for many, many decades.
    Chairman Miller. We are--Mr. Rickards.
    Mr. Rickards. Just briefly. The inverse of complexity is 
scale. You can have complexity at the small scale, a medium 
scale or a large scale. Failure at the first two will not 
destroy you. Failure at the third may, and so I am not against 
complexity. There is going to be complexity. But, again, Dr. 
Taleb's example, an elephant is a very complex organism, but if 
it dies, the entire ecosystem doesn't crash. And so let us keep 
these things in boxes and reduce the scale as the antidote to 
complexity.
    Chairman Miller. We are at the end of our time, but I want 
to thank all of you for being here. Under the rules of the 
Committee, the record will remain open for two weeks for 
additional statements from Members and for answers to any 
follow-up questions the Committee may have for the witnesses. 
Again, I appreciate your willingness to come and testify, and 
it will be useful to have all of you as resources for the 
future as well. Thank you very much. The witnesses are excused 
and the hearing is now adjourned.
    [Whereupon, at 1:30 p.m., the Subcommittee was adjourned.]
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