[Federal Register Volume 69, Number 207 (Wednesday, October 27, 2004)]
[Notices]
[Pages 62748-62776]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 04-23771]
[[Page 62747]]
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Part II
Department of the Treasury
Office of the Comptroller of the Currency
Federal Reserve System
Federal Deposit Insurance Corporation
Department of the Treasury
Office of Thrift Supervision
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Internal Ratings-Based Systems for Retail Credit Risk for Regulatory
Capital; Notice
Federal Register / Vol. 69, No. 207 / Wednesday, October 27, 2004 /
Notices
[[Page 62748]]
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DEPARTMENT OF THE TREASURY
Office of the Comptroller of the Currency
[Docket No. 04-22]
FEDERAL RESERVE SYSTEM
[Docket No. OP-1215]
FEDERAL DEPOSIT INSURANCE CORPORATION
DEPARTMENT OF THE TREASURY
Office of Thrift Supervision
[No. 2004-48]
Internal Ratings-Based Systems for Retail Credit Risk for
Regulatory Capital
AGENCIES: Office of the Comptroller of the Currency, Treasury (OCC);
Board of Governors of the Federal Reserve System (Board); Federal
Deposit Insurance Corporation (FDIC); and Office of Thrift Supervision,
Treasury (OTS).
ACTION: Proposed supervisory guidance with request for comment.
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SUMMARY: The OCC, Board, FDIC, and OTS (Agencies) are publishing for
industry comment a document that sets forth proposed supervisory
guidance for banks, savings associations, and bank holding companies
(banking organizations) that would use the internal-ratings-based (IRB)
approach to determine their regulatory capital requirements for retail
credit exposures. The Agencies described the IRB approach in general
terms in an advance notice of proposed rulemaking (ANPR) in August 2003
and expect to issue a notice of proposed rulemaking (NPR) in 2005 that
would comprehensively implement the IRB approach and other elements of
the International Convergence of Capital Measurement and Capital
Standards: A Revised Framework, which was adopted by the Basel
Committee on Banking Supervision in June 2004 (Basel II Framework).
Under the IRB approach, banking organizations would use internal
estimates of certain risk parameters as key inputs in the determination
of their regulatory capital requirements. The Agencies intend for this
guidance to provide banking organizations, in anticipation of the NPR,
with a description of the current views of the Agencies regarding (and
an opportunity for interested persons to comment on) the components and
characteristics of a qualifying IRB credit risk measurement, data
maintenance, segmentation, and quantification framework for retail
exposures.
DATES: Comments must be submitted on or before January 25, 2005.
ADDRESSES: Comments should be directed to:
OCC: Office of the Comptroller of the Currency, 250 E Street SW.,
Mail stop 1-5, Washington, DC 20219, Attention: Docket No. [04-22], Fax
number (202) 874-4448 or Internet address: [email protected].
Comments may be inspected and photocopied at the OCC's Public
Information Room, 250 E Street, SW., Washington, DC. You may submit
comments, identified by docket number [04-22], by any of the following
methods:
Federal eRulemaking Portal: http://www.regulations.gov.
Follow the instructions for submitting comments.
OCC Web Site: http://www.occ.treas.gov. Click on ``Contact
the OCC,'' scroll down and click on ``Comments on Proposed
Regulations.''
E-mail address: [email protected]. Please
include docket number [04-22] in the subject line of the message.
Fax: (202) 874-4448.
Mail: Office of the Comptroller of the Currency, 250 E
Street, SW., Public Reference Room, Mail Stop 1-5, Washington, DC
20219.
Hand Delivery/Courier: 250 E Street, SW., Attn: Public
Reference Room, Mail Stop 1-5, Washington, DC 20219
Board: You may submit comments, identified by Docket No. OP-1215,
by any of the following methods:
Agency Web Site: http://www.federalreserve.gov. Follow the
instructions for submitting comments on the http://www.federalreserve.gov/generalinfo/foia/ProposedRegs.cfm.
Federal eRulemaking Portal: http://www.regulations.gov.
Follow the instructions for submitting comments.
E-mail: [email protected]. Include docket
number in the subject line of the message.
Fax: (202) 452-3819 or (202) 452-3102.
Mail: Jennifer J. Johnson, Secretary, Board of Governors
of the Federal Reserve System, 20th Street and Constitution Avenue,
NW., Washington, DC 20551.
All public comments are available from the Board's Web site at
http://www.federalreserve.gov/generalinfo/foia/ProposedRegs.cfm as
submitted, except as necessary for technical reasons. Accordingly, your
comments will not be edited to remove any identifying or contact
information. Public comments may also be viewed electronically or in
paper form in Room MP-500 of the Board's Martin Building (20th and C
Streets, NW.) between 9 a.m. and 5 p.m. on weekdays.
FDIC: You may submit comments by any of the following methods:
Federal eRulemaking Portal: http://www.regulations.gov.
Follow the instructions for submitting comments.
Agency Web site: http://www.FDIC.gov/regulations/laws/federal/propose.html.
Mail: Robert E. Feldman, Executive Secretary, Attention:
Comments/Legal ESS, Federal Deposit Insurance Corporation, 550 17th
Street, NW., Washington, DC 20429.
Hand Delivered/Courier: The guard station at the rear of
the 550 17th Street Building (located on F Street), on business days
between 7 a.m. and 5 p.m.
E-mail: [email protected].
Public Inspection: Comments may be inspected and
photocopied in the FDIC Public Information Center, Room 100, 801 17th
Street, NW., Washington, DC, between 9 a.m. and 4:30 p.m. on business
days.
Instructions: Submissions received must include the agency name and
title for this notice. Comments received will be posted without change
to http://www.FDIC.gov/regulations/laws/federal/propose.html,
including any personal information provided.
OTS: You may submit comments, identified by No. 2004-48, by any of
the following methods:
Federal eRulemaking Portal: http://www.regulations.gov.
Follow the instructions for submitting comments.
E-mail: [email protected]. Please include No.
2004-48 in the subject line of the message, and include your name and
telephone number in the message.
Fax: (202) 906-6518.
Mail: Regulation Comments, Chief Counsel's Office, Office
of Thrift Supervision, 1700 G Street, NW., Washington, DC 20552,
Attention: No. 2004-48.
Hand Delivery/Courier: Guard's Desk, East Lobby Entrance,
1700 G Street, NW., from 9 a.m. to 4 p.m. on business days, Attention:
Regulation Comments, Chief Counsel's Office, Attention: No. 2004-48.
Instructions: All submissions received must include the agency name
and docket number or Regulatory Information Number (RIN) for this
rulemaking. All comments received will be posted without change to
http://www.ots.treas.gov/pagehtml.cfm?catNumber=67&an=1,
[[Page 62749]]
including any personal information provided.
Docket: For access to the docket to read background documents or
comments received, go to http://www.ots.treas.gov/pagehtml.cfm?catNumber=67&an=1. In addition, you may inspect comments at the
Public Reading Room, 1700 G Street, NW., by appointment. To make an
appointment for access, call (202) 906-5922, send an e-mail to
public.info@ots.treas.gov">public.info@ots.treas.gov, or send a facsimile transmission to (202)
906-7755. (Prior notice identifying the materials you will be
requesting will assist us in serving you.) We schedule appointments on
business days between 10 a.m. and 4 p.m. In most cases, appointments
will be available the next business day following the date we receive a
request.
FOR FURTHER INFORMATION CONTACT:
OCC: Mitchell Stengel, Senior Expert, Basel Credit Risk Modeling,
Risk Analysis, (202) 874-5250; Daniel L. Pearson, National Bank
Examiner, Credit Risk, (202) 874-5170; and Ron Shimabukuro, Special
Counsel, Legislative and Regulatory Activities Division, (202) 874-
5190, Office of the Comptroller of the Currency, 250 E Street, SW.,
Washington, DC 20219.
Board: Sabeth Siddique, Manager, (202) 452-3861, Division of
Banking Supervision and Regulation; Mark E. Van Der Weide, Senior
Counsel, (202) 452-2263, Legal Division, Board of Governors of the
Federal Reserve System, 20th Street and Constitution Avenue, NW.,
Washington, DC 20551; and William W. Lang, Vice President, Supervision,
Regulation and Credit, Federal Reserve Bank of Philadelphia, (215) 574-
7225. For users of Telecommunications Device for the Deaf (``TDD'')
only, contact (202) 263-4869.
FDIC: Peter Hirsch, Basel II Project Manager, (202) 898-6751, Jon
Eagar, Senior Examiner, (801) 263-3090, ext. 4726, Division of
Supervision and Consumer Protection; Michael B. Phillips, Counsel,
(202) 898-3581, Legal Division, Federal Deposit Insurance Corporation,
550 17th Street, NW., Washington, DC 20429.
OTS: Fred Phillips-Patrick, Manager, Credit Risk, (202) 906-7295,
Supervision Policy; Karen Osterloh, Special Counsel, (202) 906-6639,
Chief Counsel's Office, Office of Thrift Supervision, 1700 G Street,
NW., Washington, DC 20552.
SUPPLEMENTARY INFORMATION: The Agencies issued an ANPR on August 4,
2003, which sought comment on a substantially revised capital adequacy
framework for large and internationally active U.S. banking
organizations. See 68 FR 45900. The content of the ANPR was based in
large part on the April 2003 version of the Basel II Framework.\1\
Specifically, the ANPR described significant elements of the IRB
approach for computing credit risk capital requirements and the
Advanced Measurement Approaches for computing operational risk capital
requirements (AMA approach). Under the ANPR, certain banking
organizations would be required to adopt the IRB and AMA approaches
(core banks) and other banking organizations that met certain criteria
would have the ability to adopt the IRB and AMA approaches on a
voluntary basis (opt-in banks). Under the IRB and AMA approaches
outlined in the ANPR, core banks and opt-in banks would use internal
estimates of certain risk components as key inputs in the determination
of their regulatory capital requirements.
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\1\ See The New Basel Capital Accord (April 2003) (available at
http://www.bis.org). The Basel II Framework sets out both a
Foundation and Advanced IRB approach. However, for purposes of
domestic U.S. implementation, the ANPR only proposed adoption of the
Advanced IRB approach.
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Contemporaneously with the ANPR, the Agencies also issued for
public comment two proposed supervisory guidance documents relating to
the revised capital framework. See 68 FR 45949. The first document
provided proposed supervisory guidance on IRB systems for corporate
credit risk. This document described then-existing supervisory views on
the credit risk measurement and management systems of banking
organizations that intended to adopt the IRB approach for computing
capital requirements for corporate credit risk exposures. The second
document provided proposed supervisory guidance on AMA approaches for
operational risk.
In June 2004, the Basel Committee on Banking Supervision published
a further revised version of the Basel II Framework.\2\ In light of the
timetable for implementation of the Basel II Framework on an
international basis and the complexity and long-term operational
planning and program implementation needs of the core banks and opt-in
banks, the Agencies are publishing for comment the following proposed
IRB retail guidance document. The issuance of this document, together
with the proposed IRB supervisory guidance on corporate credit risk and
the proposed AMA supervisory guidance on operational risk, is part of
an effort by the Agencies to gather as much industry feedback from
interested parties as possible before the issuance of the NPR, which
the Agencies expect will propose a revised capital adequacy standard
based on the Basel II Framework for large and internationally active
U.S. banking organizations. Issuing this proposed guidance before the
formal issuance of the NPR will facilitate both (i) public input on the
qualifying standards and infrastructure requirements for IRB and AMA
and (ii) understanding of current Agency thinking for those banking
organizations that expect to be core banks or opt-in banks and have
sought additional guidance so that they may voluntarily begin
operational planning to qualify for use of the IRB and AMA approaches
at the earliest possible time.
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\2\ See International Convergence of Capital Measurement and
Capital Standards (June 2004) (available at http://www.bis.org). The
Basel Committee on Banking Supervision is a committee of banking
supervisory authorities that was established by the central bank
governors of the Group of Ten countries in 1975. It consists of
senior representatives of bank supervisory authorities and central
banks from Belgium, Canada, France, Germany, Italy, Japan,
Luxembourg, the Netherlands, Spain, Sweden, Switzerland, the United
Kingdom, and the United States.
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Banking organizations should note, however, that this retail IRB
guidance, like the proposed corporate IRB guidance and the proposed AMA
operational risk guidance, is only a proposal. Although these three
proposed guidance documents reflect the views of the Agencies at the
time of issuance concerning the elements of an appropriate IRB and AMA
risk management infrastructure for core and opt-in banks, the guidance
documents are subject to substantial change based on comments submitted
by banking organizations and other interested parties, further analysis
by the Agencies, results of a Quantitative Impact Study, evolution of
the Basel II Framework, and technological advances in the risk
measurement and management disciplines.
The proposed retail guidance, like the proposed corporate IRB
guidance and the proposed operational risk AMA guidance, includes many
supervisory standards that ultimately may become part of the NPR rule
text as proposed minimum qualifying requirements for use of the IRB and
AMA approaches. The Agencies included these standards in the proposed
guidance documents in order to provide banking organizations with
coherent and comprehensive guidance as to the current views of the
Agencies on the elements of an IRB and AMA risk management
infrastructure. The proposed guidance documents do not reflect any
final decisions by the Agencies about the content of the final rule,
and no such decisions will be made by the Agencies prior to a full
evaluation of the comments on the future NPR.
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Request for Comments
The Agencies request comment on whether any of the standards set
forth in this proposed retail IRB guidance should be revised, deleted,
or supplemented, and which of these standards should be (1) mandatory
minimum qualifying criteria for use of the retail IRB approaches, or
(2) criteria for supervisory guidance purposes only.
We seek comment on all other aspects of the following proposed
retail guidance document as well, including (1) the important
supervisory expectations (referred to as supervisory standards in the
guidance document) that are designated in the document by the prefix
``RS;'' (2) the methodology for the estimation of the three IRB
segment-level credit risk parameters; and (3) the framework for the
evaluation and oversight of retail exposure credit risk, which includes
provisions covering segmentation, quantification, data maintenance, and
control and oversight mechanisms.
In particular, the Agencies are interested in industry comment on
the following issues:
1. Qualifying Revolving Exposures (QRE) Volatility Requirement.
This proposed retail IRB guidance does not set forth criteria for
defining what will constitute a ``low'' ratio of loss rate volatility
to average loss rate for the purpose of qualification for QRE capital
treatment. (See paragraphs 160 to 164 of the proposed guidance.) In
developing the NPR, the Agencies will consider various options for
addressing this concern and will provide additional information
regarding QRE capital treatment. The Agencies seek comment on ways to
implement the low volatility requirement for QRE sub-portfolios.
2. Definition of Default. This proposed retail IRB guidance
(paragraph 98) stipulates that a retail exposure will be considered in
default if any one of three ``loss recognition events'' occurs. One of
these three events is that ``The exposure is put on non-accrual
status.''
The Agencies acknowledge that there is not a requirement for
placing delinquent retail exposures on nonaccrual status for either
Call Report/Thrift Financial Report purposes or for GAAP. Nonetheless,
many banks choose to put certain retail loans on nonaccrual and report
these as such on their Call Reports/Thrift Financial Reports and
financial statements.
The Agencies invite comment on this particular element of the
proposed definition of default, including detailed explanations of why
banking organizations favor or oppose the inclusion of nonaccrual
status in the definition of default.
3. Loss Given Default (LGD) Estimation. When the loss severity of a
retail portfolio exhibits significant cyclical variability, this
proposed retail IRB guidance states that a bank must estimate an LGD
that reflects periods of high credit losses for the particular
portfolio (e.g., mortgages). The period of high credit losses may be
different for each retail portfolio. (See standard RS-22 and paragraph
127.) The Agencies invite comment on various issues related to
estimating LGD for such periods:
How should ``periods of high credit losses'' (also
referred to as periods when credit losses are ``substantially higher
than average'') for a portfolio be defined?
What methods could be used to estimate an LGD appropriate
to such periods?
Should the LGD adjustment for high credit losses reflect
the likely LGD when credit losses are high at the product or portfolio
level for the particular bank (legal entity), or for a nationally
diversified portfolio?
How will a bank ensure that the LGD will reflect any
unique or predictive risk characteristics of individual segments or
small groups of segments if the period of high credit losses is defined
at an aggregated level?
If segments are defined across multiple legal entities,
how will the banking organization ensure that the capital levels
accurately reflect the unique risk of assets held by each legal entity?
The Agencies, through the Basel Committee on Banking Supervision, are
undertaking additional work to clarify LGD estimation.
4. Criteria for Assigning Exposures to Retail Categories. Because
each risk category has its own risk-weight function, assignment to
different risk categories results in different capital requirements. A
variety of loan types, especially real estate loans, could be placed in
more than one retail or corporate IRB risk category. The Agencies
request comment on whether the criteria for assigning exposures to
retail categories are appropriate for the credit risk of the exposures.
For example, is four units the appropriate limit on the number of units
in a residential property to meet the definition of a residential
mortgage loan? In addition, are small business loans appropriately
categorized based on whether they are primarily or partially secured by
residential real estate?
Paperwork Reduction Act
Each of the Agencies is subject to the Paperwork Reduction Act of
1995 (PRA).\3\ The rulemaking initiated by the ANPR likely will impose
requirements for core and opt-in banks, either in the regulations
themselves or as part of interagency implementation guidance, that are
covered by the PRA. This proposed retail IRB guidance describes the
current views of the Agencies as to the components and characteristics
of a qualifying IRB credit risk measurement, data maintenance,
segmentation, and quantification framework for retail exposures. It is
important that banking organizations recognize in reviewing the
proposed guidance that it is subject to substantial change based on the
comments received during the rulemaking process, further analysis by
the Agencies, evolution of the Basel II Framework, and other
developments.
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\3\ 44 U.S.C. 3501 et seq.
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Commenters on this proposed retail IRB guidance are asked to
provide any estimates that they can reasonably determine about the
time, effort, and financial resources that will be required to develop
and maintain the plans, reports, and records discussed in the proposed
guidance. Commenters also are requested to specify whether the
described capital and methodological standards would necessitate the
acquisition or development or new compliance/information systems or the
significant modification of existing compliance/information systems.
The Agencies also invite comment on:
(1) Whether the collections of information contained in the
proposed guidance are necessary for the proper performance of each
agency's functions, including whether the information has practical
utility;
(2) What would be an accurate estimate of the burden of the
proposed information collections;
(3) Ways to enhance the quality, utility, and clarity of the
information to be collected;
(4) Ways to minimize the burden of the information collections on
respondents, including the use of automated collection techniques or
other forms of information technology; and
(5) Estimates of capital or start-up costs and costs of operation,
maintenance, and purchases of services to provide information.
Respondents/recordkeepers are not required to respond to any
collection of information unless it displays a currently valid Office
of Management and Budget (OMB) control number.
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The Agencies have issued the proposed retail IRB guidance to seek
public input on the content of the guidance and information collection
methods used in the guidance. The Agencies have made no determination
regarding the information to be collected, if any. When the Agencies
have developed a firm proposal, they will follow the standard process
to seek public comment on the information collection and to obtain OMB
approval.
The Agencies will use any comments received to evaluate the burden
attendant to the approach set forth in the proposed retail IRB
guidance. Comments on the collections of information should be sent to:
OCC: John Ference or Camille Dixon, OCC Clearance Officer, Office
of the Comptroller of the Currency, 250 E Street, SW., Mail Stop 8-4,
Attention: 1557-IRBG, Washington, DC 20219. Comments also may be sent
by electronic mail to [email protected].
Board: Cindy Ayouch, Federal Reserve Board Clearance Officer, (202)
452-3829, Division of Research and Statistics, Board of Governors of
the Federal Reserve System, 20th and C Streets, NW., Mail Stop 41,
Washington, DC 20551. Comments also may be sent by electronic mail to
[email protected].
FDIC: Leneta Gregorie, Counsel, (202) 898-3907, Legal Division,
Federal Deposit Insurance Corporation, 550 17th Street, NW.,
Washington, DC 20429. Comments also may be sent by electronic mail to
[email protected].
OTS: Marilyn K. Burton, OTS Clearance Officer, (202) 906-6467,
Office of Thrift Supervision, 1700 G Street, NW., Washington, DC 20552.
Comments also may be sent by electronic mail to
[email protected].
The text of the proposed IRB retail guidance document follows:
Proposed Supervisory Guidance on Internal Ratings-Based Systems for
Retail Credit Risk
Table of Contents
I. Introduction
A. Background
B. Scope of Retail Guidance
C. Definition of Retail Exposures
D. Quantifying Retail Exposure Credit Risk
E. Supervisory Expectations
II. Retail Risk Segmentation Systems for IRB
A. Overview
B. Criteria for Retail Segmentation
C. Retail Risk Segmentation Architecture
1. Migration of Exposures Between Retail Segments
2. Frequency of Changes to the Segmentation System
3. Segmentation and the Recognition of the Risk Mitigation
Benefits of Guarantees and Insurance
D. Validation Process
1. Segmentation Systems' Developmental Evidence
2. Ongoing Monitoring
3. Back-testing of the Segmentation System
III. Quantification of IRB Systems
A. Introduction
1. The Four Stages of the Quantification Process
2. Integration of the Four Stages
3. General Standards for Sound IRB Quantification
B. Quantification of the IRB Risk Parameters
1. Quantification of Probability of Default (PD)
a. Data
b. Estimation
1. Seasoning
c. Mapping
d. Application
2. Quantification of Loss Given Default (LGD)
a. Data
b. Estimation
c. Mapping
d. Application
3. Quantification of Exposure at Default (EAD)
a. Introduction
b. Data
c. Estimation d.Mapping e.Application
C. Quantification: Special Cases and Applications
1. Small Business Exposures
2. Retail Leases
3. Purchased Retail Receivables
4. Loan Sales
5. Securitization and Undrawn Balances
6. Multiple Legal Entities
7. QRE Treatment Qualification
8. Stress Testing
D. Validation
1. Introduction
2. Developmental Evidence
3. Ongoing Process Verification and Benchmarking
4. Back-Testing
IV. Data Maintenance
A. Overview
B. General Data Requirements
1. Standards for Refreshed Data
2. Loan Sales
3. Validation and Refinement
4. Data Standards for Outsourced Activities
5. Calculating Capital Ratios and Reporting to the Public
C. Managing Data Quality and Integrity
1. Documentation and Definitions
2. Data Access and Scalability
3. Data Gaps
V. Control and Oversight Mechanisms
A. Overview
B. Controls Over Lending Activities
C. Accountability
D. Independent Review of Retail IRB Processes
E. Transparency
F. Use of Risk Estimates
G. Internal and External Audit
H. Corporate Oversight
Appendix A: Process Analysis Examples
Appendix B: Technical Examples
List of Acronyms
I. Introduction
A. Background
1. This document provides supervisory guidance for banks, thrifts,
and bank holding companies that adopt the advanced internal-ratings-
based (``IRB'') approach for determining regulatory risk-based capital
requirements for retail exposures (``banks'').\4\ As described in the
preamble to the Federal Register publication of this guidance, this
document reflects the current views of the Federal banking agencies
(``agencies'') and is subject to change based on comments submitted by
the banking industry and other interested parties, further analysis by
the agencies, results of the fourth quantitative impact study, and
technological advances in the risk measurement and management
disciplines. This retail guidance includes some supervisory standards
that ultimately may become part of the minimum IRB qualifying
requirements that would be proposed as part of the notice of proposed
rulemaking (``NPR'') that the agencies intend to issue for public
comment in 2005 to comprehensively implement the IRB approach. It was
necessary to include these standards in this proposed guidance document
in order to provide banks with coherent and comprehensive guidance as
to the current views of the agencies on the elements of a retail IRB
risk management infrastructure.
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\4\ Throughout this guidance, the term ``banks'' generally
refers to banks, thrifts, and bank holding companies adopting the
IRB approach.
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2. A central objective of the IRB framework is to enhance the risk
sensitivity of the minimum regulatory capital requirements. Under the
retail IRB approach, banks assign risk parameters to pools of exposures
with similar risk characteristics, that is, to risk segments, rather
than to individual exposures (as in the corporate portfolio). These
parameters are then used for the determination of minimum regulatory
capital. Supervisors will rely on banks, subject to minimum standards,
to use internal risk management systems to differentiate segments of
retail exposures by the credit risk they pose and to quantify the risk
parameters for each segment. Adequate data to support accurate and
reliable credit risk measurements, as well as rigorous management
oversight and controls, including continual monitoring and
[[Page 62752]]
validation, are crucial to the prudent application of the IRB capital
framework.
3. This guidance, which is written for supervisors and banks,
describes the components and characteristics of an IRB credit risk
measurement and management framework for retail exposures. The guidance
explains how to measure the risk of retail exposures, maintain data on
them, segment them, and quantify each segment's risk. The guidance
should help foster accountability, transparency, and oversight and
control mechanisms in the IRB capital framework.
4. With these goals in mind, this guidance sets forth retail
supervisory standards for an IRB credit risk system. These standards
are highlighted in bold and designated by the prefix ``RS.'' To enable
banks to implement the framework flexibly whenever possible, these
regulatory standards typically take the form of general principles
rather than specific requirements. However, when the need for
uniformity outweighs the benefits of flexibility (often for reasons of
prudence), the guidance provides more detailed and specific
expectations. Banks would be expected to have credit risk management
practices that are consistent with the substance and spirit of the
standards in this guidance. Furthermore, nothing in this guidance
should be interpreted as weakening, modifying, or superseding the
safety and soundness principles articulated in the existing statutes,
regulations, or guidance of the agencies.
5. In general, this IRB retail guidance neither dictates the
precise manner by which banks should seek to meet the supervisory
standards nor provides comprehensive technical guidance on how to meet
the standards. This document assumes that readers are familiar with the
proposed IRB approach for the calculation of minimum regulatory capital
requirements in the International Convergence of Capital Measurement
and Capital Standards, published by the Basel Committee on Banking
Supervision in June 2004 (``Basel II'').
6. Under the retail IRB approach, banks first segment retail
exposures and then quantify the risk of each segment by estimating each
segment's probability of default (PD), loss given default (LGD), and
exposure at default (EAD). Consistent with many retail lenders'
internal risk management practices, a bank may also choose to
indirectly obtain an estimate of PD by first obtaining estimates of
average dollar loss rates and loss severity. These quantitative
estimates of risk must be consistent with those used for internal risk
management purposes.
B. Scope of Retail Guidance
7. For the purposes of this guidance, the terms ``retail exposure''
and ``retail loan'' are intended to include retail leases as well as
loans.
8. When the terms ``models'' and ``models-based'' are used in this
guidance, they refer to banks'' use of various types of statistical
modeling techniques solely for the purpose of estimating the risk
parameters PD, LGD, and EAD for IRB retail segments.
9. The agencies expect that this guidance and the standards set
forth below would apply to most retail exposures of banks. Although
banks can designate some retail exposures as nonmaterial and, thus, not
subject to the retail IRB approach, the aggregate amount of these
nonmaterial retail exposures must be small as a percentage of the
bank's total retail exposures, and the aggregate amount of credit risk
in the nonmaterial retail portfolios must be a small percentage of the
bank's total amount of retail exposure credit risk. A bank must
maintain adequate documentation to support its nonmaterial
determinations. Subject to supervisory review, banks will determine
minimum capital requirements for a nonmaterial retail portfolio
according to the risk-based capital standards for non-IRB banks.
10. Some banking organizations have retail portfolios that are
centrally managed, even though the exposures are held by multiple legal
entities. Certain activities, including segmentation and
quantification, can be conducted across multiple legal entities within
the United States, subject to limitations discussed in chapter III and
chapter V. However, each legal entity subject to IRB capital
requirements must document its minimum regulatory capital requirements
on a standalone basis and hold its own separate minimum regulatory
capital in proportion to the risk exposure of its portfolios.
Specifically, the PD, LGD, and EAD estimates used to determine minimum
regulatory capital levels must be applied to exposures at the segment
level, and capital requirements for each relevant legal entity should
be based on the proportionate share of each segment owned by such legal
entity. Furthermore, the board of directors of each such legal entity
must ensure that capital calculations accurately reflect the risk
profile of their individual banks.
11. While the general principles of retail segmentation,
quantification, and data maintenance will apply to all portfolios,
special issues may arise in the case of portfolios outside the United
States. Cross-border issues for retail and other portfolios will be
addressed in future documents.
C. Definition of Retail Exposures
12. An exposure is a retail exposure for IRB purposes if both of
the following conditions are met:
The exposure is managed as part of a pool of similar
exposures rather than as an individual exposure; and
With the exception of small business loans (see below),
the obligor is an individual.
13. Within this general definition, there are three retail risk
categories, each with specific qualifying criteria:
Residential mortgage loans secured by one- to four-family
residential properties. Includes first and subsequent liens, term
loans, lines of credit, and legally binding commitments to lend. This
includes business loans if the loans are primarily secured by one- to
four-family residential properties. No limit on the size of the
exposure.
Qualifying revolving exposures (QREs) whose outstanding
amount fluctuates, determined largely by the borrower's decisions to
borrow and repay, up to a pre-established limit. Must be revolving,
unsecured, and unconditionally cancelable by the bank; maximum
exposure, $100,000. Includes most credit cards to individuals (but not
those issued on behalf of a business) and overdraft lines on individual
checking accounts. Also included are overdraft protection programs,
commonly referred to bounced-check protection programs, that advise
customers of an amount up to which overdrafts may be paid.\5\ To
qualify for QRE status, a sub-portfolio must display low volatility of
loss rates relative to its average level of loss rates.
---------------------------------------------------------------------------
\5\ This sentence is intended to capture bounced-check
protection programs and reflects the reporting and capital standards
proposed in the draft Interagency Guidance on Overdraft Protection
Programs that was published for comment in the Federal Register on
June 7, 2004 (69 FR 31858). However, it should be noted that once
the Interagency Guidance on Overdraft Protection Programs is
finalized, this draft guidance may be amended to reflect changes in
that guidance.
---------------------------------------------------------------------------
Other retail--general and small business. ``General''
applies to all retail exposures to individuals that do not fall into
either of the two previous categories or into the ``small business''
category described immediately below. No limit on size of exposure.
``Small business'' applies to small loans of any kind to individuals or
companies for business purposes. However, if a small business loan is
primarily secured by 1-4 family residential property, it should
[[Page 62753]]
be included in the residential mortgage category above. For small
business loans, total exposure to a single borrower is limited to $1
million, on a fully consolidated basis, although supervisors may allow
amounts slightly above the limit.
14. Private banking exposures must meet the requirements stated
above, including the requirement that they must be managed as part of a
pool of similar exposures, to be considered under retail IRB.
Otherwise, they would fall under corporate IRB.
15. Each of the three retail risk categories has a separate risk-
weight function. These functions differ from one another only by the
supervisor-specified asset value correlation. The unexpected loss
capital requirement (K) per dollar of EAD for each retail segment of
non-defaulted assets is calculated using the following general formula:
[GRAPHIC] [TIFF OMITTED] TN27OC04.000
where N is the cumulative standard normal distribution,
N-\1\ is the inverse cumulative standard normal
distribution, R is the asset value correlation, and 0.999 is the
``solvency standard'' chosen by the supervisors.\6\ For residential
mortgages, R is specified as 0.15, for qualifying revolving exposures,
R is specified as 0.04, and for other retail exposures, R varies
between 0.03 and 0.16, based on the following formula:
---------------------------------------------------------------------------
\6\ That is, minimum regulatory capital for covering unexpected
losses, K, is set to equal the estimated level of unexpected losses
corresponding to the 99.9th percentile of the loss distribution for
the bank's credit portfolios.
[GRAPHIC] [TIFF OMITTED] TN27OC04.001
16. Minimum capital requirements for defaulted retail exposures are
determined separately. See chapter III for a detailed discussion.
17. Risk-weighted assets (RWA) for each segment are calculated as
12.5 x K x EAD.
18. The expected dollar loss on a segment (EL) is defined as PD x
LGD x EAD. The overall level of expected losses in the retail and
certain other portfolios is used in the calculation of a regulatory
capital adjustment.
D. Quantifying Retail Exposure Credit Risk
19. There are two distinct phases in the process of determining the
minimum regulatory capital requirements for the credit risk of retail
exposures. In the first phase, credit risk segmentation, a bank assigns
every individual retail exposure to a segment or pool with homogeneous
risk characteristics. These characteristics, often referred to as
``primary risk drivers'' (such as loan-to-value ratios and credit
scores), are reliable predictors of loan performance over time that
allow banks to effectively sort exposures into homogeneous segments. To
segment risk in this way, bankers must have a thorough understanding of
how a retail exposure's risk drivers affect the risk parameters (PD,
LGD, and EAD).
20. In the second phase, quantification, a bank statistically
estimates the three risk parameters, PD, LGD, and EAD, for each retail
segment. Historical data are used to create ``reference segments''
whose subsequent credit performance has been observed and included in
the data set. The central assumption of this phase is that the
estimated relationship between the particular set of risk drivers and
the credit performance of the reference segments will hold for the
segments that make up the existing portfolio. Once the risk parameters
are quantified for existing retail exposure segments, the bank then
calculates the minimum regulatory capital requirements based on the
appropriate IRB formulas.
21. Each phase has its own validation challenges. In phase one, the
bank must determine whether the assignment of retail exposures to
segments effectively separates exposures by characteristics that remain
significant drivers of risk over time. In phase two, the bank must
determine whether the risk parameter estimates are accurate and
representative of the risk in the existing portfolio.
22. A robust and detailed data maintenance system should support
implementation of the IRB segmentation and quantification process as
well as their dynamic development. Management oversight and control
mechanisms over the entire IRB retail credit risk system (including
segmentation, quantification, and supporting data maintenance) should
ensure conservative, verifiable, and accurate estimates of the segment-
level credit risk parameters.
23. In summary, IRB banks will be expected to construct and
maintain a retail credit system comprising four interdependent
components corresponding to the four chapters of this guidance. The
four chapters are organized as follows: chapter II, ``Segmentation'';
chapter III, ``Quantification''; chapter IV, ``Data Maintenance''; and
chapter V, ``Control and Oversight Mechanisms.''
E. Supervisory Expectations
24. Taken together, segmentation, quantification, data maintenance,
and control and oversight mechanisms provide a framework for defining
and improving evaluation of retail credit risk and determining minimum
regulatory capital. Supervisors expect that banks will continue to
refine their credit risk systems using regular reviews and updates.
25. All aspects of the risk segmentation system and the
quantification processes must be subject to thorough, independent, and
well-documented validation. Banks should use a variety of validation
approaches; no single approach can conclusively validate the risk
segmentation and quantification methods. Three broad types of useful
tools include evaluating the developmental evidence or logic of the
system; ongoing monitoring of system implementation and reasonableness
(verification and benchmarking); and comparing realized outcomes with
predictions (back-testing).
26. A rigorous framework of control and oversight mechanisms must
govern the entire IRB implementation. The framework must be
characterized by independence, transparency, and accountability; must
ensure that the IRB implementation standards discussed in this guidance
are met; and must ensure that related bank policies are followed. The
control and oversight mechanisms must also include independent
technical validation of all quantitative aspects of the risk
segmentation and quantification systems.
27. For IRB systems to work successfully, they need the active
[[Page 62754]]
support and oversight of the board of directors and senior management
to ensure that the various components fit together seamlessly and that
incentives are in place to extend the system rigorously across business
line, risk management, and other control groups.
28. The proposed regulatory minimum capital requirements are
predicated on a bank's internal systems being sufficiently advanced to
allow a full and accurate assessment of its risk exposure. The IRB
framework demands more rigorous validation work and controls than
supervisors have required in the past. When properly implemented, the
new framework will better align minimum capital requirements with risk.
29. Supervisors will evaluate compliance with the four components
of a retail IRB system and how well the various components of a bank's
retail IRB system complement and reinforce one another to achieve the
overall objective of accurately determining minimum required regulatory
capital for retail exposures. In performing their evaluation,
supervisors will exercise considerable supervisory judgment in
evaluating both the individual components and the overall IRB
framework.
II. Retail Risk Segmentation Systems for IRB
A. Overview
30. This chapter describes the design and operation of a qualifying
retail risk segmentation system. IRB retail risk segments are pools of
exposures within the three retail risk categories that contain
exposures with similar risk characteristics.
31. The retail IRB framework is intended to provide banks with
substantial flexibility to use the retail portfolio segmentation they
believe is most appropriate for their activities, subject to the
following broad standards:
The goal of segmentation is to provide meaningful
differentiation of risk, with each pool composed of exposures with
homogeneous risk characteristics Accordingly, in developing the risk
segmentation system, banks should consider the chosen risk drivers'
ability to separate risk consistently over time and the overall
robustness of the bank's approach to segmentation.
Segmentation must use relevant borrower risk
characteristics (such as credit score, delinquency, or debt-to-income
ratio) and loan-related risk characteristics (such as loan-to-value or
product type) that reliably differentiate a segment's risk from that of
other segments and that perform consistently over time.
Risk drivers for segmentation should be consistent with
the predominant risk characteristics used by the bank for internal
credit risk measurement and management.
The segmentation system should generate pools that
separate exposures by realized performance. It should be designed so
that actual long-run outcomes closely approximate the retail IRB risk
parameters estimated by the bank.
In general, segments should not cross national
jurisdictions.
IRB banks must have ongoing validation processes for risk
segmentation systems that include the evaluation of developmental
evidence or logic of the system, ongoing monitoring, and back-testing.
Validation for the risk segmentation system is ultimately tied to
validation of the bank's quantification of IRB risk parameters. This
aspect of validation is discussed in chapter III.
32. The IRB retail risk parameter estimates that determine minimum
required capital are assigned at the segment level.
B. Criteria for Retail Segmentation
RS-1: Banks must segment exposures into pools with homogeneous risk
characteristics. Banks must separately segment exposures in each
distinct product line within each of the three retail risk categories
(mortgage, QRE, and other).
33. Examples of acceptable approaches to segmentation include:
Banks may segment exposures by common risk drivers that
are deemed relevant and material in determining the loss
characteristics of a particular retail product. For example, a bank may
segment mortgage loans by LTV band, age from origination, geography,
origination channel, and credit score. Statistical modeling, expert
judgment, or some combination of the two may determine the most
relevant risk drivers.
Alternatively, banks could segment by grouping loans with
similar loss characteristics, such as similar average loss rates or
similar PDs. (Those loss parameters would be estimated in accordance
with the techniques outlined in chapter III.)
34. While banks have considerable flexibility in determining IRB
retail risk segments, they should consider factors affecting both
borrower risk characteristics (such as credit score) and loan-related
risk characteristics (such as LTV) when determining segmentation
criteria.
35. Each retail risk segment will typically be associated with a
separate PD, LGD, and EAD. In some cases, it may be reasonable to use
the same LGD estimate for multiple segments. In such cases, the bank
must demonstrate that there are no material differences in LGD among
those segments. Over time, supervisors expect banks to develop more
precise data and methodologies for determining LGDs.
36. There may be situations in which data for certain retail loans
are missing or incomplete, such as data for purchased loans or loans
originated as policy exceptions. The overall segmentation system should
adequately consider the risk associated with these loans based on data
availability. In some cases, missing or incomplete data by itself may
be a significant risk factor for segmentation purposes.
RS-2: Defaulted assets must be segmented on the basis of risk
characteristics predictive of loss and recovery rates.
37. The IRB capital calculation for defaulted assets requires banks
to provide a ``best estimate'' of the losses on these loans. (See
chapter III for details.) Since, by definition, defaulted assets have
PDs equal to 1, these best estimates of losses will depend solely on
banks' estimates of losses given current conditions. To produce these
best estimates, banks must segment defaulted assets separately from
non-defaulted assets, and base the segmentation on those
characteristics that are most predictive of current loss and recovery
rates. This segmentation should provide meaningful differentiation so
that individual loans within each defaulted segment do not have
material differences in their expected loss severity.
RS-3: A retail IRB risk segmentation system must produce segments
within each retail risk category that adequately differentiate risk and
produce reliable estimates of the IRB risk parameters.
38. A bank must support the degree of granularity in its
segmentation system and the distribution of exposures across segments.
Granularity refers to how finely the portfolio is segmented into
differentiated risk pools.
39. Banks have considerable flexibility in determining the
granularity of their risk segmentation. Each bank must perform its own
internal analysis to determine the appropriate degree of granularity in
order to achieve the goal of producing homogeneous risk segments. For
example, a bank using credit score ranges to segment its portfolio must
provide the rationale for the ranges chosen.
[[Page 62755]]
40. A concentration of exposures in a segment (or segments) does
not, by itself, reflect a deficiency in the segmentation system. For
example, a bank may lend within a narrow risk band and, therefore, have
a smaller number of risk segments than a bank that lends across a wider
range of risk bands. However, a bank with a high concentration of
exposures in a particular risk segment will be expected to document
that the bank's segmentation criteria are carefully delineated and well
documented. The bank should be able to demonstrate that there is little
risk differentiation among the exposures within the segment, and that
the segmentation method produces reliable estimates of IRB risk
parameters.
RS-4: Banks must clearly define and document the criteria for
assigning an exposure to a particular retail risk segment. The risk
factors used for IRB risk segmentation purposes must be consistent with
internal methods of assessing credit risk for retail exposures.
41. The method of risk segmentation will help determine the risk
parameters as well as which techniques should be used for validation
and which control mechanisms will best ensure the integrity of the risk
segmentation system. To assist the discussion of segmentation
requirements, described below are some alternative techniques for
determining appropriate segmentation.
Banks may incorporate results of statistical underwriting
models or scoring models directly into their risk segmentation process.
For example, a bank may use a custom or bureau credit score as a
segmenting criterion. In that case, the bank must validate the choice
of the score, as well as demonstrate that its credit scoring system has
adequate controls.
Banks may incorporate the variables from a statistical
model into their risk segmentation processes. For example, a bank that
uses a statistical model to predict losses for its mortgage portfolio
could select some or all of the major inputs to that model, such as
debt-to-income and LTV, as segmentation criteria. As part of its
validation and controls for the IRB segmentation system, the bank must
provide an appropriate rationale and empirical evidence for its choice
of the particular set of risk drivers from the loss prediction model.
Banks may combine expert judgment with statistical
analysis in determining appropriate segmentation criteria. However,
expert judgment of this type must be well documented and supported by
empirical evidence demonstrating that the chosen risk factors are
reliable predictors of risk.
42. A bank must be able to demonstrate a strong relationship
between IRB risk drivers and comparable measures used for credit risk
management. Specifically, a bank should demonstrate that the IRB
segmentation system differentiates credit risk across the portfolio and
captures changes in the level and direction of credit risk that are
similar to measures used in credit risk management. For example, even
if a bank uses custom scores for underwriting or account management,
generic bureau scores may be used for IRB segmentation purposes if the
bank can demonstrate a strong correlation between these measures.
C. Retail Risk Segmentation Architecture
Migration of Exposures Between Retail Segments
RS-5: Banks must develop and document their policies to ensure that
risk driver information is sufficiently accurate and timely to track
changes in underlying credit quality and to migrate exposures between
segments.
43. Under the IRB framework, a bank initially assigns retail
exposures to segments based on the information about their risk drivers
available at the time of origination or acquisition. The bank must then
continue to monitor the risk characteristics of the exposures and
migrate exposures to new segments, as necessary, based on refreshed
information gathered by the bank as part of its monitoring process.
44. Banks must choose risk drivers that accurately reflect the risk
of an exposure. Risk drivers selected should be consistent with risk
measures used for credit risk management.
45. In accordance with industry practices in retail credit risk
management, a bank must have a well-documented policy on monitoring and
updating information on exposure risk characteristics and on migrating
exposures between segments. The policy should specify the risk
characteristics to be updated and the frequency of updates for each
product type or sub-portfolio within its retail portfolio. Updating of
relevant information on these risk drivers must be consistent with
sound risk management.
46. Decisions regarding frequency of obtaining refreshed
information should reflect the specific risk characteristics of
individual segments and/or the materiality of the potential impact on
capital. The frequency of updates and of migration will generally
differ for different risk drivers and for different products. The
underlying principle is that, in every period, retail exposures are
assigned to segments that accurately reflect their risk profile and
produce accurate IRB risk parameters.
47. Banks are expected to assess their approach to updating
information and migrating exposures as part of the validation of the
segmentation process.
Frequency of Changes to the Segmentation System
RS-6: Banks must review their segmentation system at least annually
and have clear policies to define the criteria for modifying the
system.
48. Banks must review their segmentation system to ensure that it
maintains adequate risk separation. Changes in the segmentation system
should be documented and supported to ensure consistency and obtain
historically comparable measurements.
Segmentation and the Recognition of the Risk Mitigation Benefits of
Guarantees and Insurance
RS-7: Banks that design their risk segmentation systems to realize
the benefits of guarantees or other risk mitigants must be able to
support their approach.
49. Retail exposures may have guarantees or insurance, such as
private mortgage insurance (PMI) and government guarantees for
residential mortgages. (See chapter III for a more detailed discussion
of PMI.) A bank's risk segmentation system may reflect such guarantees,
as may its risk parameter estimates. For example, loans with similar
risk characteristics, including the same type of guarantee, could be
pooled together.
D. Validation Process
RS-8: Banks must validate that their retail IRB risk segmentation
process separates exposures into segments with homogeneous risk
characteristics that generate reliable long-run estimates of the IRB
risk parameters.
50. Banks must ensure that the actual performance of their segments
is consistent with the expectations underlying the assignment of
exposures to segments as set forth in their documentation. Over time,
performance data should validate the manner in which the bank
differentiated the portfolio by segment, and the actual loss
characteristics of each segment should be consistent with its estimated
IRB risk parameters.
RS-9: The ongoing validation process must include the review of
[[Page 62756]]
developmental evidence, ongoing monitoring, and back-testing.
51. The ongoing process to confirm and ensure the performance of
the segmentation system consists of:
The evaluation of developmental evidence;
Ongoing monitoring of system implementation and
reasonableness; and
Back-testing (comparing actual to predicted outcomes).
52. IRB banks are expected to employ all of the components of this
process. However, back-testing of segmentation may be difficult if a
bank's process for modeling risks is evolving significantly. Therefore,
banks may at times need to rely more heavily on developmental evidence
and quality control tests to assure themselves and other interested
parties that their segmentation systems are sufficiently accurate.
Segmentation Systems' Developmental Evidence
53. Developmental evidence helps determine whether the segmentation
system can be expected to differentiate effectively between pools of
exposures by the credit risk they pose. To evaluate developmental
evidence, experts make a reasonable assessment of the quality of the
segmentation system by analyzing its design and construction.
For example, developmental evidence in support of
statistical techniques used in the segmentation process, such as
scoring models or underwriting models, must include documentation and
discussion of their logical foundations and an analysis of the
statistical model-building techniques.
The developmental evidence will be more persuasive when it
includes empirical evidence of how well the segmentation system has
differentiated pools of exposures in the past, including evidence that
it worked effectively outside the development sample.
Empirical developmental evidence of a segmentation system
would also include evidence of how the system compares with other
systems. These other systems could include other internal segmentation
systems as well as external systems whose performance can be charted
against industry benchmarks.
Ongoing Monitoring
54. The second source of analytical support for the validity of a
bank's risk segmentation system is the ongoing analysis to confirm that
the system continues to group loans into pools with similar loss
characteristics. The bank must develop a monitoring process to evaluate
its system's ability to segment by risk and to apply this process
consistently over time. The bank must document its approach to
monitoring and the results of this analysis. The bank must also
generate reports to senior management on the functioning of the
segmentation system.
55. Specific verification activities will depend on the
segmentation approach. For retail lending, statistical models will be
an important part of the segmentation process, and the bank must verify
that the data used by these models are accurate and complete.
Back-Testing of the Segmentation System
RS-10: Banks must establish internal tolerance limits for
differences between expected and realized outcomes that require
appropriate managerial review.
56. Back-testing is comparing realized outcomes with each segment's
expected performance. For retail IRB systems, back-testing is a means
of assessing whether the bank's method of segmentation and its
techniques for estimating IRB risk parameters combined to work
effectively. Accordingly, back-testing is a conceptual bridge between
the segmentation system discussed in this chapter and the
quantification of the IRB risk parameters discussed in chapter III.
Because these two processes are so closely linked, a more complete
discussion of back-testing is deferred until chapter III.
III. Quantification of IRB Systems
A. Introduction
57. The IRB framework requires banks to assign to each segment of
the retail portfolio specific numerical values for each of the three
risk parameters: probability of default (PD), loss given default (LGD),
and exposure at default (EAD).\7\ Under the IRB framework, these
numerical values are inserted into the appropriate formula (set forth
in the introduction) to determine the minimum required regulatory
capital for each segment.
---------------------------------------------------------------------------
\7\ A note on units of measurement: PD and LGD are measured as
rates (percentages or decimals). EAD is a dollar amount,
representing estimated exposure at default. Therefore PD x LGD x EAD
will represent the dollar amount of expected losses (EL).
---------------------------------------------------------------------------
58. Quantification is the process by which these numerical values
for each retail segment are determined. The risk parameters must be
estimated in a manner consistent with sound risk management practice,
quantitative techniques, and supervisory standards. In addition, a bank
must ensure that these estimates remain valid over time. Since
quantification occurs at the segment level, it is founded on the retail
risk segmentation system presented in chapter II.
59. Conceptually, the quantification process can be broken into
four stages: obtaining historical reference data; using the historical
reference data to estimate relationships between the risk
characteristics of the borrowers and loans on the one hand and observed
outcomes (such as default rate, loss severity rate, or tendency to make
additional draws on credit card lines prior to default) on the other;
mapping the correspondence between the reference data and the existing
portfolio's data; and applying the relationship between reference data
and parameters to the portfolio's data in order to generate IRB risk
parameters for the bank's existing retail segments.
60. In addition, the estimated values of the risk parameters (PD,
LGD, and EAD) must be independently and thoroughly validated and the
results reported to senior management.
61. The chapter is organized as follows: Section A,
``Introduction,'' establishes the organizing framework for IRB
quantification and develops general standards that apply to the entire
process. Section B, ``Quantification of the IRB Risk Parameters,''
covers specific supervisory standards that apply to the quantification
of the three risk parameters, PD, LGD, and EAD. Section C,
``Quantification: Special Cases and Applications,'' addresses a variety
of special cases and applications of the retail quantification
standards and procedures (for example, small business exposures, loan
purchases, purchased retail receivables, and retail leases). Section D,
``Validation,'' discusses how a bank should validate the segmentation
and quantification processes.
62. A number of general examples are given in the text of this
chapter to aid exposition and interpretation. Some relevant
implementation examples covering the four stages of the full
quantification process are presented in ``Appendix A: Process Analysis
Examples.'' The guidance concludes with a number of examples of
technical issues specific to retail quantification in ``Appendix B:
Technical Examples.''
The Four Stages of the Quantification Process
63. Stage one--obtaining reference data. The bank assembles
historical data that are used to estimate the retail IRB risk
parameters. The reference data must closely resemble the bank's
existing portfolio. Banks must use the best historical data available
for quantifying
[[Page 62757]]
the retail IRB risk parameters. Over time, IRB banks will be expected
to rely primarily on internal data for most of their retail portfolios,
but supplemental external data may also be used when necessary. Banks
may use more than one reference data set to improve the robustness or
precision of the parameters. Reference data sets should include data on
product type, borrower characteristics, and loan payment performance.
Reference data for PD quantification includes some loans that later
defaulted. Reference data sets for LGD and EAD quantification will
consist solely of defaulted loans and the resulting recovery streams
from internal historical data.
64. Important considerations in the choice and construction of a
reference data set include:
Comparability of the reference data to the existing credit
portfolio, including consistency of risk segmentation criteria,
underwriting standards, and definitions of default and loss.
The appropriate inclusion of periods of portfolio stress.
65. The reference data set should also include the following:
External factors relevant to the reference data that might
affect the parameter estimates should be recorded, for example, the
geographic concentration, the economic environment, and industry trends
during the time period of the reference data.
All borrower and loan characteristics that are used to
estimate risk parameters must be included, as well as all variables
necessary to redevelop and validate the estimation approach.
The definition of default and methods of measuring loss
that were in use at the time must be in the reference data set. The
data must include collection costs, gain or loss on sale of collateral,
date of default, etc.
66. When it is not possible to use consistent segmentation criteria
for both the reference and existing portfolio, reasonably close proxy
characteristics must be found.
67. Stage two--estimation. The bank applies analytical or
statistical methods to the reference data to estimate a relationship
between the borrower and loan risk characteristics embodied in the
reference data and the outcomes of interest (defaults, loss severity,
additional draws on unused lines prior to default). In other words, the
bank uses empirical techniques to estimate the segment values of the
risk parameters, PD, LGD, and EAD, as a function of the borrower and
loan risk characteristics of the counterpart segment in the reference
data. The risk parameter estimates may rely on relatively simple
analysis of default rate or loss rate statistics from the reference
data, or they may be a result of regression or other statistical
estimation and classification techniques. This step may include
adjustments for seasoning effects. A bank may use more than one
technique to generate risk parameter estimates from the reference data
if doing so improves robustness and accuracy of the estimates. If
multiple estimates are generated, the bank must have a clear and
consistent policy on reconciling the different estimates.
68. Stage three--mapping. The bank establishes a close
correspondence between the portfolio data and the reference data. The
risk segmentation criteria for the reference data set should match
closely the criteria for the existing portfolio. In addition, if any
other characteristics of the reference data and the existing portfolio
are used to estimate the risk parameters, they should correspond
closely in both data sets. For many retail portfolios, mapping will be
a relatively mechanical process for banks using quantitative criteria
to segment and model risk. If the quantitative characteristics are
equally valid and provide comparable measures, mapping will simply mean
applying these characteristics to the existing portfolio. In some
cases, mapping may be more challenging. For example, if a bank
undertakes a major new effort to expand its offering of products on the
Internet, and the bank has little internal data on exposures offered
this way, the bank may need to augment its reference data with external
data.
69. Stage four--application. The bank applies the relationship
estimated for the reference data to the actual portfolio data. The
ultimate aim of quantification is to generate the risk parameter
estimates for each segment of retail exposures within the existing
portfolio. In the application stage, the bank often simply applies the
risk parameter estimates calculated for each segment of retail
exposures in the reference data to the corresponding segment in the
existing portfolio. If the bank incorporates multiple data sets or
estimation methods for the risk parameters, or if the mapping stage
required adjustment to ensure a close match of the reference data and
the existing portfolio data, the application stage could be more
complex.
Integration of the Four Stages
70. While the four-stage quantification described above is a useful
conceptual approach, banks may satisfy supervisory standards without
explicitly dividing the quantification process into four stages. In
particular, the mapping and application stages may be fairly mechanical
applications of the quantitative risk segmentation criteria to the
existing portfolio. An example of a seamless approach to the four
stages of quantification is provided in example 1 of appendix A.
71. In general, the mapping and application stages will represent
relatively straightforward processes when:
The bank relies on quantitative segmentation criteria (for
example, credit score, LTV, debt-to-income ratio), and these criteria
represent relatively stable risk drivers over time. For example, if a
bank uses a custom credit score, the score values must represent
similar risk over the relevant time period.
There are no major new product offerings, or changes in
underwriting standards or other policies that require alternative risk
segmentation criteria.
72. The complexity of the mapping and application stages will
depend on the availability of data and the consistency over time of
factors such as product offerings and underwriting standards. For some
banks or product types, it will be necessary to work through all four
stages for one or more risk parameters. In those cases, a bank should
use most or all of the detail, complexities, and contingencies
concerning the mapping and application stages spelled out in the
remainder of this chapter.
73. Finally, while the four stages of quantification can sometimes
be streamlined (because a bank's data history is extensive, for
example), validation should not be streamlined. Even when a bank is
able to take a straightforward approach, it must use the full
validation process as prescribed in the last section of this chapter.
General Standards for Sound IRB Quantification
74. Several core standards apply to all elements of the overall IRB
retail quantification process; these general standards are discussed in
this section. Other supervisory standards, specific to particular risk
parameters, are discussed in the subsequent sections. When evaluating
retail IRB quantification, supervisors will consider all of these
standards, both general and specific. Particular practical approaches
to retail quantification may be highly consistent with some standards
and less so with others. In any particular case, an ultimate assessment
relies on the judgment of supervisors to weigh the
[[Page 62758]]
strengths and weaknesses of a bank's chosen approach, using these
supervisory standards as a guide.
RS-11: Banks must have a fully specified process covering all
aspects of retail quantification. The quantification process must be
fully documented and updated periodically.
75. A fully specified quantification process must describe how all
four stages (data, estimation, mapping, and application) are
implemented for each risk parameter. The quantification process should
be periodically reviewed and updated to ensure that it incorporates new
data, analytical techniques, and evolving industry practice.
76. Documentation promotes consistency and allows third parties to
review and replicate the entire process. Examples of third parties that
might make use of the documentation include internal reviewers of the
quantification model and risk segmentation system, internal validation
teams within an independent function, and bank supervisors.
77. Major decisions in the design and implementation of the
quantification process should be justified and fully documented. A bank
should have a well-defined policy for reviewing and updating the
segmentation and quantification design. Particular attention should be
given to new business lines or portfolios in which the distribution of
retail exposures among segments is believed to have changed
substantially. A material merger, the acquisition or sale of loans, and
substantial account attrition or growth clearly raise questions about
the continued applicability of the process and should trigger a review
and possible updating.
78. At a minimum, the risk parameter estimates must be updated at
least quarterly and more frequently if deemed necessary for accurate
credit risk management. New data should be incorporated into the risk
parameter estimates using a well-defined process to correctly merge
data sets over time. The frequency of updates and the process for doing
so must be justified and documented in bank policy.
79. The bank must ensure that the use of judgment in the design of
the quantification system does not produce unduly low risk parameter
estimates.
80. Aspects of the quantification process that are apt to introduce
greater uncertainty and potential error include the following:
Uncertainty when there are substantial changes in the
bank's product offerings, target customer base, or underwriting
standards;
Deficiencies or gaps in available data;
The possibility of model error; and
Mergers or acquisitions where the MIS for the acquired
assets does not match the MIS for existing assets.
81. The more uncertain the bank's estimates are as a result of any
of the causes cited in the previous two paragraphs, the greater should
be the margin of conservatism around those estimates, although these
margins need not be added at each step.
RS-12: Quantification must be based upon the best available data
for the accurate estimation of IRB risk parameters.
82. Given the bank-specific basis of assigning retail exposures to
segments, over time banks are expected to regard internal data as the
primary source of information for estimating IRB risk parameters.
However, banks are permitted to use external data for quantification,
provided a strong similarity can be demonstrated (1) between the bank's
process of assigning exposures to a segment and the process used by the
external data source, and (2) between the bank's internal credit risk
profile for a given set of risk drivers and that of the external data.
83. The bank must have a process for vetting potential reference
data, whether the data are internal or external. The vetting should
assess whether the data are sufficiently accurate, sufficiently
complete, and sufficiently representative of the bank's existing
exposures. Furthermore, the bank must have adequate data to estimate
risk parameters for all loans on the books as if they were held to
maturity, even if some loans are likely to be sold or securitized
before their long-term credit performance can be observed. (See Section
C, RS-27 of this chapter.)
84. One objective of the IRB framework is to encourage further
development of credit risk quantification techniques. Improving the
quality, capture, and retention of internal data is an essential
prerequisite for such advances.
85. For new products it is likely that banks will need to
supplement internal data with external sources. It may also be possible
to accumulate internal data through the testing of new products by
offering loans to a limited number of consumers and observing the
performance.
86. In the case of mergers or purchased portfolios, the data for
the newly acquired segments may not be compatible with the purchasing
bank's MIS. In such cases it may be necessary to gather data on
borrower and loan characteristics from a combination of internal and
external sources. Historical data on the purchased portfolios, if
available from external sources, would allow the incorporation of
borrower and loan risk characteristics data into the purchasing bank's
internal database. The risk parameters can then be estimated by
combining historical data from the purchased portfolio (if available)
with internal reference data.
87. Differences in economic conditions between the reference data's
sample period and the present period should be monitored. In addition,
the bank needs to consider any changes in trend behavior by consumers
or small businesses that might affect the relevance of the historical
data to the present period. For example, the bank may need to monitor
actual or anticipated changes in consumer behavior due to changes in
bankruptcy law or other factors.
88. A well-defined and documented process should be in place to
ensure that the reference data are updated as frequently as needed, as
fresh data become available or as portfolio changes make necessary. All
data sources, characteristics, and the overall processes governing data
collection and maintenance must be fully documented, and that
documentation should be readily available for review by supervisors.
RS-13: The sample period for the reference data must be at least
five years and must include periods of portfolio stress.
89. In general, the bank should use all relevant historical data
available, though the bank may weight some periods more heavily if it
can demonstrate that the weighted data are likely to produce more
accurate risk parameter estimates. Newer reference data, for example,
may receive greater weight because of possible changes in bank
products, underwriting standards, policies, and strategies. On the
other hand, unusual recent circumstances in the bank's internal
portfolio composition or in the historical period may make the recent
data less applicable than the older data. If the reference data include
data from beyond five years (to capture a period of stress or for other
valid reasons), the reference data need not cover all of the
intervening years.
90. Example: During the 2001 to 2003 period of highly elevated
mortgage prepayments owing to record low interest rates, losses may
have been deferred in mortgage portfolios because of readily available
refinancing options. Also, losses on foreclosures during this period
were limited because housing prices generally increased throughout
[[Page 62759]]
the United States despite a recession. A similar (though not as
substantial) drop in interest rates occurred in the early 1990s. That
recession, however, was characterized by a sharp drop in property
values in many parts of the country. In a case like this, where the
recent period has been atypical, a bank may choose to weight the older
data (perhaps from external sources) more heavily than the recent data.
91. When a bank does not have sufficient historical data to
encompass a period of stress for a particular portfolio, other sources
of data covering stressed periods will be required. The bank may be
able to select sub-samples of its internal portfolio that experienced
stressed periods (for example, particular MSAs or geographic regions);
see example 1 of appendix B. The bank may also use external data from
industry sources.
RS-14: Mapping must be based on a robust comparison of available
data elements that are common to the existing portfolio and each
reference data set.
92. Sound mapping practice uses all key common elements available
in the data. A mapping should be plausible and should be consistent
with the risk segmentation system established by the bank. Levels and
ranges of key characteristics for each segment of the existing
portfolio's retail exposures should approximate the values of similar
characteristics for the reference data.
93. A bank that uses multiple reference data sets should conduct a
rigorous mapping process for each data set. (Some common mapping
challenges are discussed in example 2, appendix B.)
94. The use of internal data for reference data purposes does not
eliminate the need for a mapping requirement because changes in bank
strategy (such as marketing, underwriting standards, or account
management practices) or products may alter the risk characteristics or
composition of the portfolio over time, even within the same pools of a
risk segmentation system.
RS-15: Mappings must be reviewed regularly and updated as
necessary.
95. Mappings should be reaffirmed regularly for both internal and
external reference data, regardless of whether the risk segmentation
criteria have undergone explicit changes during the period covered by
the reference data set. Changes in borrower risk characteristics,
products, and bank policies (for example, target population,
underwriting standards, or collection policies) are quite typical in
retail lines of business, so it is imperative that banks review all
mappings regularly. When significant characteristics have been changed,
added, or dropped, a new mapping must be established between the
characteristics of the existing portfolio and characteristics of the
reference data.
RS-16: Banks that combine estimates from internal and external data
or that use multiple estimation methods must have a clear policy
governing the combination process and should examine the sensitivity of
the results to alternative combinations.
96. To improve the accuracy of its estimates, a bank might combine
data from multiple sources and may use multiple estimation methods. The
manner in which the estimates from multiple data sets or estimation
methods are combined is extremely important, since different
combinations will produce different parameter estimates for each
segment. The bank should investigate parameter estimates' sensitivity
to different ways of combining data sets or combining estimation
methods. When results are highly sensitive to how data or estimates are
combined, the bank should choose among the alternatives conservatively.
A bank must document why it selected the combination techniques it did,
and these techniques must be subject to appropriate approval and
oversight by management.
RS-17: A bank must have a clear, well-documented policy for
addressing the absence of significant data elements in either the
reference dataset or the existing portfolio.
97. Some exposures in the reference data set and the existing
portfolio will have missing data elements, some of which are important
factors for measuring risk. Banks may segment these exposures
separately for estimating the risk parameters. Alternatively, they may
use a variety of statistical methods to impute values for the missing
data points--provided these points can be sufficiently correlated to
known information about the exposure. Expertise is required to judge
whether such correlations can be established. Regardless of the
approach and level of sophistication, the bank must have a clear and
well-documented process describing how it treats missing data elements
in the estimation and mapping stages.
B. Quantification of the IRB Risk Parameters
RS-18: For estimating the IRB retail risk parameters, qualifying
banks must use the IRB definition of default.
98. For retail exposures, banks must use the following definition
of default for IRB: A retail exposure will be considered in default for
IRB purposes when any one of the following loss recognition events
occurs:
Loss recognition as embodied in the Federal Financial
Institutions Examination Council (FFIEC) Uniform Retail Credit
Classification and Account Management Policy. All residential mortgages
and revolving credits must be recognized as defaults at 180 days past
due, and all other retail loans must be recognized as defaults at 120
days past due.
A partial or full charge-off is taken against the
exposure.
The exposure is put on non-accrual status.
99. For retail exposures (as opposed to wholesale exposures), the
definition of default is applied to a particular loan rather than to
the obligor. That is, default by an obligor on one obligation would not
require a bank to treat all other obligations of the same obligor as
defaulted.
100. In the early stages of IRB implementation, a bank's historical
reference data might not fully conform to the IRB definition of
default. In addition, a bank may change its policies regarding charge-
offs or placing loans on non-accrual. In such cases, a bank should make
conservative adjustments to reflect such discrepancies.
Quantification of Probability of Default (PD)
101. For a given segment, the PD represents an estimate of the
long-run average of one-year default rates. The one-year default rate
(or default frequency) is the number of accounts that default at any
time within a one-year period divided by the number of accounts open at
the beginning of the year. (To figure in the calculation, an account
must be open at the beginning of the period.) For unseasoned loans
where seasoning effects are material, upward adjustments to the PD
estimates will be necessary (as described in paragraphs 109 through
112).
Data
102. A bank must have a comprehensive reference data set that maps
to the existing portfolio on a segment-by-segment basis. The same
comparability standards apply to both internal and external data
sources. All data sources must meet the minimum five-year requirement
and include a period of economic stress. See example 4, appendix B for
an example of a reference data set.
[[Page 62760]]
Estimation
103. Estimation of PD is the process by which characteristics of
the reference data are related to the default frequencies for each
segment of exposures in the reference portfolio. The relevant
characteristics that help to determine the PDs are referred to as
``drivers of default.'' Drivers of default might include product, loan
and borrower characteristics such as loan-to-value, credit line
utilization, credit score, or delinquency status. Also, a portfolio
separator such as geographic region, while not a direct driver of
default, might indicate separate relationships by geographic region of
the PD to these drivers. These drivers could be criteria for the
assignment of exposures to pools in the risk segmentation system. A
statistical model developed to estimate the PD would incorporate such
drivers directly into the PD estimation.
RS-19: Estimates of PD must be empirically based and must represent
the average over time of segment default frequencies on an account
basis. The effects of seasoning, prepayments, and attrition must be
considered in the PD estimates.
104. PD estimates should capture average expected default rates for
a segment given its risk characteristics. PD estimates should represent
averages of default rates measured over a sufficiently long time period
to provide accurate estimates. The estimation period must include
periods of economic distress.
105. When estimating PDs, a bank may give equal weight to each
sample period or it may weight recent data more heavily if it can
demonstrate that doing so is more predictive of future default
behavior.
106. If the bank calculates an average PD over time by weighting
each year's segment-level PD by the number of loans or volume of
outstanding balances, the estimated PD may be lower or higher than the
estimated PD from an unweighted average. For example, if lending
typically declines during periods of stress, this weighting will tend
to lessen the impact of the stress periods on the weighted average. A
bank using such an approach would be expected to empirically
demonstrate that such an approach produces a more accurate estimated PD
for its existing portfolio. See example 2 of appendix A for an example
of the quantification of a models-based PD consistent with a long-run
average.
107. Different methods of measuring and tracking exposures,
defaults, and losses are common in credit risk management. Banks are
required to produce an estimate equivalent to the one-year account
default rate. See example 3 in appendix B.
108. Some banks may choose to derive a PD based on the average
expected dollar loss rate. A bank may use this method as long as it
produces an accurate PD on an account basis as defined in paragraph
101. See example 3 in appendix A.
Seasoning
109. Seasoning poses a challenge for banks quantifying the default
rate for retail exposures when the default rate follows a
characteristic account age profile, typically rising for the first
several periods following origination and then falling. Seasoning is an
issue for longer-maturity consumer products such as residential
mortgages, but it may also be important for shorter-lived portfolios.
In addition, accounting for seasoning is particularly significant for
portfolios that are growing rapidly through new originations or for
banks that systematically sell or securitize loans before they reach
the peak of the seasoning curve. In both cases, banks should factor
seasoning into their quantification to provide adequate capital to
cover future needs.
110. For segments containing unseasoned loans, a bank should assign
a higher PD estimate that reflects the annualized cumulative default
rate over the segments' expected remaining life.\8\ For seasoned loans,
the bank should use the long-run average of one-year PDs.
---------------------------------------------------------------------------
\8\ If the bank can demonstrate that seasoning does not have a
material effect on PD, the bank can use the long-run average of one-
year PDs.
---------------------------------------------------------------------------
111. The account age profile may be tracked by using account age as
a criterion in the risk segmentation system or as a predictive variable
of the PD parameter. Several methods can be used to account for
seasoning in the PD estimates. See example 4 in appendix A.
112. Periods of unusual prepayments or other types of account
attrition have the potential to materially alter the estimated
historical default rates for some retail exposures. PD estimates must
be developed in such a way that they are not distorted by periods of
unusual prepayment activity or other types of account attrition in the
reference data sets.
Mapping
113. Mapping is establishing a correspondence between the existing
portfolio and the reference data--that is, it is identifying how the
existing portfolio's product, loan, and borrower risk characteristics
relate to the reference data's characteristics. Mapping enables a bank
to determine how risk parameter estimates from the reference data
should apply to the existing portfolio. For banks with a consistent,
long-term process of risk segmentation, PD mapping may consist simply
of adopting the long-run average PD estimates from the historical data.
However, if the bank's internal risk segmentation has varied over time,
the bank must demonstrate a discernable link between its existing
segmentation system and the long-run PD estimates produced from the
reference data.
114. In some business lines, products, or cross-sections of the
portfolio, certain drivers of default may not be available in the risk
segmentation system. Drivers are most likely to be missing as banks
transition to an IRB system or when a bank acquires a portfolio. In
such cases, the bank should modify its mapping process accordingly.
Supervisors expect this practice to be temporary, however, and as the
requisite data become available, banks should incorporate the omitted
effects into the risk segmentation system.
Application
115. In the application stage, the bank applies the PD estimates to
the risk segments of the existing portfolio to calculate minimum
regulatory capital. This should be a relatively mechanical process for
most retail portfolios.
RS-20: PD estimates for all retail segments cannot be less than
0.03 percent (3 basis points)
Quantification of Loss Given Default (LGD)
116. LGD is defined as the segment's credit-related economic losses
net of discounted recoveries divided by the segment's exposure at
default, all measured during a period of high credit losses for the
particular portfolio (e.g., mortgages, credit cards). The LGD
estimation process is similar to the PD estimation process. The bank
identifies a reference data set, which must include periods of
portfolio stress. Once the bank obtains these data sets, it should
select a technique to estimate the credit-related economic loss per
dollar of exposure for all defaulted loans in each reference segment.
The bank's reference data should then be mapped to the bank's existing
retail segments, so that the model can be applied to generate an
estimate of the LGD for each segment in the existing portfolio.
Data
117. Unlike reference data sets used for PD estimation, data sets
for LGD
[[Page 62761]]
estimation contain only defaulted exposures.
118. In order to calculate economic loss, the reference data sets
must include all relevant data for quantifying LGD. This would include
the exposure at the time of default (including principal plus unpaid
but capitalized interest and fees), recoveries, and material collection
and workout expenses. The data should contain the circumstances of
default, for example, roll to charge-off or bankruptcy leading to
charge-off, if they are significant factors for LGD. Recovery data
should include the income and timing of recoveries including direct
payments from the consumer, the sale of the collateral, or realized
income from the sale of defaulted loans. For defaulted loans and
collateral still on the balance sheet, the estimated current market
value can be used to proxy the recovery amount. Cost data comprise the
material direct and indirect costs associated with workouts and
collections, including the dates when the various costs were incurred.
119. The same minimum history of five years for the LGD reference
data set is required, or longer to include a period of portfolio
stress. Although a bank may use internal or external data, most banks
will eventually be expected to collect and maintain sufficient internal
data.
120. In the LGD calculation, all material credit-related losses
must be captured, whether or not those losses are ultimately charged to
the ALLL. Material credit-related losses are broadly defined to include
any material losses associated with a defaulted loan, including write-
off of unpaid interest or fees, write-downs of repossessed collateral,
and any similar losses.
Estimation
121. Banks must determine an accurate LGD parameter for each
segment. As discussed in chapter II, banks may estimate and apply a
common LGD over a range of risk segments within a particular product
type, where appropriate.
RS-21: The estimates of LGD must reflect the concept of ``economic
loss.''
122. For estimating LGD, the definition of loss is based on the
concept of economic loss, which is a broader, more inclusive concept
than accounting measures of loss. Economic loss incorporates the mark-
to-market loss of value of the defaulted loan and collateral plus all
direct and indirect costs of workout and collections, net of recoveries
(including late fees and interest). Losses, recoveries, and costs
should all be discounted to the time of default.
123. The scope of cash flows included in recoveries and costs is
meant to be broad. Workout and collection costs that can be clearly
attributed to certain segments of loans, plus indirect cost items, must
be reflected in the bank's LGD assignments for those exposures.
Recovery costs include the costs of running the bank's collection and
workout departments and the cost of outsourced collection services
directly attributable to recoveries during a particular time or for a
particular segment of loans, at as granular a level as possible.
Recovery costs also include an appropriate percentage of other ongoing
costs, such as corporate overhead.
124. These recovery costs can be allocated using the same
principles and techniques of cost accounting that are usually used to
determine the profit and loss of activities within any large
enterprise. Collection and workout departments, however, may cover
services not 100 percent attributable to defaulted loans. For example,
the same call center may manage reminder calls to delinquent accounts,
many of which will never default, as well as collection calls. The
expenses for these functions should be differentiated to allocate only
collection expenses attributable to defaulted loans.
125. When costs can't be allocated because of data limitations, the
bank may assign those costs using broad averages. (For example, the
bank could allocate costs by outstanding dollar amounts of loans,
including unpaid interest and fees at the time of default, within each
segment.)
126. All losses, costs, and recoveries should be discounted to the
time of default if realization of those material costs and recoveries
is significantly delayed. The discount rate should be applied to the
time interval between the date of default and the date of the realized
loss, incurred cost, or recovery, on a pooled basis. A bank must
establish a discount rate that reflects the time value of money and the
opportunity cost of funds to apply to recoveries and costs. The
discount rate, which should reflect the distressed nature of the asset,
should usually exceed the contract interest rate for newly originated
products as of the date of default. Within the retail portfolio, the
discounting process will be particularly important in the case of
residential mortgages because foreclosure laws in many states allow
considerable time to pass between default and recovery.
RS-22: The estimated LGD must reflect loss severities during
periods of high credit losses.
127. A bank must estimate an LGD for each segment that reflects
economic downturn conditions where necessary to capture the relevant
risks. The LGD cannot be less than the long-run default-weighted
average LGD calculated on the basis of the average economic loss of all
observed defaults within the data source for that retail segment. In
addition, a bank must take into account the potential for the LGD to be
higher than the default-weighted average during a period when credit
losses for a particular portfolio (e.g., mortgages) are substantially
higher than average. For certain types of exposures, loss severities
may not exhibit such cyclical variability, and LGD estimates may not
differ materially (or possibly at all) from the long-run default-
weighted average. However, for other exposures, this cyclical
variability in loss severities may be significant, and banks will need
to incorporate it into their LGD estimates. For this purpose, banks may
use averages of loss severities observed during periods of high credit
losses for that product, forecasts based on appropriately conservative
assumptions, or other similar methods.
128. The LGD of an asset does not change with its actual default.
The assigned LGD should already reflect a default loss experience
predicated on a period of high credit losses. However, once an asset
actually defaults, the bank must construct its best estimate of
expected losses for it based on current economic circumstances and risk
characteristics. For this purpose, banks can group defaulted loans into
segments. (See chapter II.) The amount, if any, by which the LGD on the
defaulted asset segment exceeds the bank's best estimate of the current
expected loss rate on the segment represents the capital requirement
(K) for that segment. The agencies are considering the possible
establishment of an appropriate capital requirement floor for defaulted
assets. When the best estimate of expected loss on a defaulted asset is
less than the sum of specific provisions and partial charge-offs, that
asset will attract supervisory scrutiny and must be justified by the
bank.
129. Examples 5, 6, and 7 in appendix B present some issues related
to LGD estimation.
Mapping
130. LGD mapping follows the same general standards as PD mapping.
The default and loss definitions and loss severity risk drivers in the
reference data and the existing portfolio of retail exposures must be
comparable. Some common challenges in mapping are presented in example
2, appendix B.
[[Page 62762]]
The mapping process must be updated regularly, well documented, and
independently reviewed.
Application
131. At the application stage, banks apply the LGD estimation
framework to their existing portfolio of exposures. Doing so might
require banks to aggregate individual segment-level LGD estimates into
broader averages or to combine estimates.
132. LGD may be particularly sensitive to changes in the way banks
manage retail credits. For example, a change in policy regarding
collection practices or loan sales may have a significant impact on the
quantification of LGD. When such changes take place, the bank should
consider them in all steps of the quantification process. If a bank's
policy changes seem likely to reduce LGD, estimates should be reduced
only after the bank accumulates a significant amount of actual
experience under the new policy to support the reductions; on the other
hand, policy changes that are likely to increase LGD should be
reflected in the estimates in a timely fashion.
RS-23: IRB banks have a minimum LGD of 10 percent for residential
mortgages.
133. This floor is based on the view that LGDs, if appropriately
estimated, are unlikely to fall below this level during periods of high
credit losses. During the initial two-year implementation period of the
IRB framework, the LGDs for retail residential mortgages cannot be set
below 10 percent. During this transition period, the agencies will
review the potential need for continuation of this floor. Mortgages
guaranteed by a sovereign government are exempt from this floor.\9\
---------------------------------------------------------------------------
\9\ This exemption applies to VA-guaranteed and FHA-insured
mortgages.
---------------------------------------------------------------------------
RS-24: If banks choose to reflect the risk-mitigating effect of
private mortgage insurance (PMI) for residential mortgages in their
risk estimates, they must do so by incorporating these insurance
benefits into the quantification of segment-level LGD.
134. In calculating losses for LGD estimation, the amount of
expected PMI benefits would be deducted from the losses otherwise
incurred by the bank on defaulted mortgages.
135. Banks may choose to incorporate loan-level PMI coverage into
their risk segmentation. For example, loans with similar risk
characteristics, including the same type of PMI coverage, could be
placed in a single segment. In any case, banks will need accurate PMI
coverage data in both the reference and existing-portfolio data sets.
This would generally require loan-by-loan tracking of PMI over the life
of the loan, since loans on which the lender requires PMI coverage at
origination (generally because of LTVs greater than 80 percent) often
drop coverage when current LTV falls below 80 percent. Pool-level
mortgage insurance is treated under the IRB securitization framework or
under the general IRB credit risk mitigation rules.
136. Banks with substantial PMI-covered residential mortgages
should monitor the senior unsecured debt ratings of the PMI companies.
If the rating of any PMI company falls below AA, banks should
accordingly adjust the LGD to take into account the elevated
counterparty risk for all mortgages insured by that company.
Quantification of Exposure at Default (EAD)
Introduction
RS-25: The bank must provide an estimate of EAD for each segment in
its retail portfolio.
137. For an individual retail exposure, EAD is the gross amount due
at default, which is the amount by which regulatory capital would be
reduced if the exposure were to be fully written off. This includes all
accrued, but unpaid, interest and fees. EAD for defaulted assets
includes any partial write-offs that have already been incurred. EAD
for a segment is the sum of the EADs of all the loans in the segment.
For fixed exposures such as term loans and installment loans, each
loan's EAD is no less than the principal balance outstanding.\10\ For
revolving exposures and other lines of credit such as credit cards,
overdrafts on checking accounts, and home equity lines of credit, each
loan's EAD includes the outstanding balance plus estimated net
additions to balances for loans defaulting over the following year.
These additions consist of future principal increases including
capitalized future interest and fees.
---------------------------------------------------------------------------
\10\ For all loans, the LGD calculation includes all unpaid
interest and fees in the measure of economic loss.
---------------------------------------------------------------------------
138. For purchased loans, the EAD is set equal to the purchase
price. For example, if a bank buys a retail portfolio consisting of
exposures with $100 million face value at a 5 percent discount, the
initial EAD for the purchasing bank is $95 million. (Example 8 in
appendix B illustrates the effect of the purchase discount on EAD and
LGD.)
139. To estimate the net additional draws, many banks estimate a
loan equivalent exposure (LEQ) as the percentage of the total
authorized but undrawn lines expected to be drawn down by borrowers
that default. Thus, the estimated dollar value of the additional
drawdown before default can be represented as:
Net additional draws =
LEQ * (total authorized line - present outstanding balance)
EAD for the segment can then be represented as:
EAD = Present outstandings + Net additional draws
It is the LEQ that must be estimated, since the total authorized
line and the amount presently outstanding are known. The estimation of
the LEQ is the focus of this section of the guidance.
140. A bank quantifies its EAD by working through the four stages
of quantification: the bank must develop a reference data set; it must
estimate an EAD for segments in the reference data set with a given
array of characteristics; it must map its existing portfolio to the
reference data; and by applying the mapping, it must generate an EAD
estimate for each segment in the existing portfolio.
Data
141. In order to estimate LEQ for an entire segment, EAD reference
data sets contain only defaulted loans. In many cases, the same
reference data may be used for both LGD and EAD. In addition to
relevant descriptive characteristics that can be used in estimation,
the reference data must include historical information on drawn and
undrawn exposures prior to default, as well as the drawn exposure at
the date of default.
142. As discussed below under ``Estimation,'' LEQ estimates of
potential draws may be developed using either a cohort method or a
fixed-horizon method. The bank's reference data set should be
structured so that it is consistent with the estimation method that the
bank applies.
Estimation
143. To derive LEQ estimates for each segment, characteristics of
the reference data are related to additional drawings preceding a
default event. The estimation process should be capable of producing an
average estimate of draws on unused lines to support the EAD
calculation for each segment. Two broad types of estimation methods are
used in practice: the cohort method and the fixed-horizon method.
Regardless of the method used, the LEQ estimates must accurately
capture the potential exposure to losses from loans defaulting over the
coming year.
[[Page 62763]]
144. Under the cohort method, a bank groups defaults into discrete
calendar periods, typically one year. A bank may use a longer period if
it provides a more accurate estimate of total future losses arising
from undrawn exposures. The bank then estimates the relationship
between the balances for defaulted loans at the start of the calendar
period and the balances at the time of default.
145. Under the fixed-horizon method, the bank bases its estimates
on a reference data set that supplies the actual exposure at default
for each defaulted loan along with the drawn and undrawn amounts at a
fixed interval prior to default. Estimates of LEQ are computed from the
increase in balances that occur over the fixed-horizon interval for the
defaults in the segment. The time interval used for the fixed-horizon
method must be sufficiently long to capture the additional exposures
generated by loans that default during the year for which the risk
parameters are being estimated. In particular, the appropriate fixed
interval will be influenced by charge-off policies. For example, using
a six-month time interval for credit card loans would underestimate
EAD.
RS-26: The estimated LEQ must reflect estimated net additional
draws during periods of high credit losses.
146. The LEQ cannot be less than the long-run default-weighted
average for that retail segment. The LEQ must reflect net additional
draws observed during periods of high credit losses if these are
systematically higher than the default-weighted average. For this
purpose, banks may use averages of LEQs observed during periods of high
credit losses for that product, forecasts based on appropriately
conservative assumptions, or other similar methods.
Mapping
147. If the characteristics that drive EAD in the reference data
are the same as those used for the risk segmentation system of the
bank's existing retail portfolio, mapping may be relatively
straightforward. However, if the relevant characteristics are not
available in a bank's existing portfolio risk segmentation system, the
bank will encounter the same mapping complexities that it does when
mapping PD and LGD in similar circumstances.
Application
148. In the application stage, the estimated relationship between
risk drivers and LEQ is applied to the bank's existing portfolio. With
the exception of portfolios purchased at a discount, the estimated EAD
must be at least as large as the currently drawn amount in each
segment; therefore, LEQs cannot be negative. Multiple reference data
sets may be used for LEQ estimation and combined at the application
stage, subject to the general standards for using multiple data sets.
149. EAD may be particularly sensitive to changes in the way banks
manage retail credits. For example, a change in policy regarding line
increases or decreases for particular segments may have a significant
impact on LEQ. When such changes take place, the bank should consider
them when making its estimates--and it should do so from a conservative
point of view. Policy changes likely to significantly increase LEQ
should prompt immediate increases in LEQ estimates. If a bank's policy
changes seem likely to reduce LEQ, estimates should be reduced only
after the bank accumulates a significant amount of actual experience
under the new policy to support the reductions.
C. Quantification: Special Cases and Applications
Small Business Exposures
150. Certain exposures to a company or to an individual for
business purposes can be included in the ``other retail'' category for
IRB purposes provided they meet the following conditions:
A small business loan must be managed by the bank on a
segmented basis, where credit scoring is often a key component of the
underwriting decision process, and the bank must estimate risk
parameters for segments of such loans with similar risk
characteristics. (If the small business exposures are rated and managed
as individual exposures, they will fall under the corporate standards
and requirements.)
The total of all of the bank's exposures to a single
business (whether in the name of the business or in the name(s) of the
proprietor(s) for business purposes) cannot exceed $1 million.
Revolving exposures to an individual can be treated as
QREs, even if used for business purposes. However, revolving exposures
to businesses will be treated as ``other retail'' if they meet the
criteria above.
151. Small business exposures qualifying for retail treatment are
subject to all the standards applicable to other retail exposures.
Retail Leases
152. The minimum capital requirement for retail leases is the sum
of (1) the credit risk capital requirement on the discounted lease
payment stream plus (2) 8% of the residual value of the leased asset:
The lease payment credit risk is determined by estimating
PD and LGD in the same manner as retail loan exposures; EAD equals the
discounted remaining lease payment stream.
The risk of the residual value is the bank's exposure to
loss arising from potential decline in the fair value of the leased
asset below the estimate at the time of lease inception.
Purchased Retail Receivables
153. Purchased retail receivables are treated the same as other
categories of retail exposures, except for the effects of dilution.
Dilution effects refer to the potential reduction in receivable
balances caused by cash or non-cash credits granted to the receivables'
obligor(s). Examples include offsets for the return of goods sold and
discounts given for prompt payment. If dilution poses a material risk,
banks should estimate an expected (long-run average) one-year dilution
rate (as a percentage of the receivables amount.) The minimum
regulatory capital requirement for dilution risk is determined
according to the corporate risk weight formula.
154. When refundable purchase price discounts, collateral, or
partial guarantees provide first dollar loss protection for purchased
retail receivables, banks may treat these as first dollar loss
protection under the IRB securitization framework and use that
framework for the calculation of minimum capital requirements for the
purchased retail receivables. Alternatively, the bank may choose to
treat EAD as the purchase price.
Loan Sales
RS-27: Quantification of the IRB risk parameters must be adjusted
appropriately to recognize the risk characteristics of exposures that
were removed from reference data sets through loan sales or
securitizations.
155. Banks must estimate the risk parameters for all loans on the
books as if they were held to maturity, even if some loans are likely
to be sold or securitized before their long-term credit performance can
be observed. Loan sales and securitizations, however, can pose
substantial difficulties for quantification. For example, PDs might
appear disproportionately low if loans are sold before their historical
performance patterns become manifest. Adjusting the risk parameter
estimation to correct for sales or securitization would be particularly
important for a bank that sells off primarily credits that are
performing poorly (for example, delinquent loans).
156. If the potential bias in the parameter estimates created by
loan
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sales and securitizations is material, the bank must identify, by
detailed risk characteristics, the loans sold out of the pool or
portfolio when using internal historical data as reference data sets
for quantification purposes.
157. For banks with a history of regularly selling or securitizing
loans of particular types, long-run performance data should be
available from the servicers or trustees. Alternatively, banks may be
able to construct appropriate reference data sets with risk
characteristics comparable to the loans sold or securitized by using
internal historical data from retained pools or external data.
Securitization and Undrawn Balances
158. For QREs, home equity lines of credit (HELOCs), and other
retail products where the drawn balances of certain accounts in the
portfolio have been securitized, the IRB risk parameters and minimum
capital requirements shall be determined as follows:
For the seller's interest in securitized receivables, the
risk parameters and minimum capital requirements must be determined as
stipulated in this chapter.
The potential additions to the balances prior to default
for all of the accounts with securitized balances must be determined in
accordance with the section of this chapter on EAD. These additions
must be allocated between the seller's (originating bank's) and
investors' shares on a pro rata basis, in the same proportions as the
drawn balances of the accounts.
For the seller's interest in the undrawn balances, the
risk parameters and capital requirements must be determined as
stipulated in this chapter.
For the investors' interest in the undrawn balances,
minimum regulatory capital will be determined according to the IRB
rules for securitizations.
Multiple Legal Entities
159. In those cases where quantification is conducted across
portfolios that are held by two or more legal entities, segmentation
must meet all the standards set forth in Chapter II. Exposures assigned
to a single segment must share homogeneous risk characteristics,
regardless of whether the exposures are held on the books of a single
or multiple legal entities, to ensure that the risk parameters
accurately reflect the risk of the exposures held by that entity. For
example, if a particular institution within the banking group holds
loans with unique or predictive characteristics (such as credit card
loans originated through a specific marketing channel or mortgage loans
in a certain location), the segmentation system must be designed to
incorporate these characteristics to ensure that PDs, LGDs and EADs for
each entity are accurately stated. The following standards also apply:
The risk parameters for each segment are determined on a
segment-wide basis in the same manner described in the preceding
sections of this chapter.
Capital requirements for each legal entity should be based
on the pro-rata share of the EAD exposure for each segment that is
owned by that entity.
Periodic validation should be conducted to confirm that
minimum capital requirements determined through this approach are not
materially different from those that would be determined on a separate
entity basis.
QRE Treatment Qualification
160. To qualify for QRE treatment, in addition to the other
requirements listed in chapter I, banks must demonstrate that their
revolving portfolios are characterized by low volatility of loss rates
relative to average loss rates, particularly for low PD bands.
161. Specifically, [sigma]LR/LR must be ``relatively
low,'' where LR is the average loss rate, and [sigma]LR is
the volatility, or the standard deviation of the average loss rate over
time.
162. The average loss rate and the standard deviation should be
calculated over a sufficiently long time period to be representative of
the performance of the portfolio over both good and stressful economic
environments.
163. There is no fixed threshold for what constitutes a ``low
ratio'' of [sigma]LR to LR. Banks will be expected to
develop and document policies for their thresholds, and to compare
ratios across portfolios that meet all the remaining qualifications for
QRE treatment. In addition, they should compare the ratios to those of
their other retail portfolios and their corporate and bank portfolios.
Banks must retain data on their loss rates.
164. If the ratio of loss rate volatility to average loss rates is
not sufficiently low, the portfolio will be subject to treatment as
``other retail'' rather than as QRE. Supervisors will review the
relative volatility of loss rates across the QRE sub-portfolios, as
well as the aggregate QRE portfolio, and intend to share information on
the typical characteristics of QRE loss rates across jurisdictions.
Stress Testing
165. Stress-testing analysis indicates the effect of economic
downturns on credit quality and the resulting effect on capital
requirements. Under the new framework, changes in borrower credit
quality will lead to changes in the required IRB regulatory capital
charge. Since credit quality changes typically reflect changing
economic conditions, required regulatory capital may also vary with the
economic cycle. During an economic downturn, regulatory capital
requirements could increase if exposures migrate toward lower credit
quality segments as a result of higher unemployment and lower incomes.
166. Supervisors expect that banks will manage their regulatory
capital position so that they remain adequately capitalized during all
phases of the economic cycle. A bank that is able to credibly estimate
regulatory capital levels during a downturn can be more confident of
appropriately managing regulatory capital. Stress testing is one tool
for that estimation, by means of projecting the levels of key
performance measures in an economic downturn.
167. Stress testing is a general term that can be applied to
different types of analysis, depending on the purpose of the exercise.
To cite an example that differs from the type of stress testing
considered here, a bank might want to shed light on how it would fare
during an extreme scenario that threatens its continued existence.
Still another type of stress testing evaluates the effect of an adverse
scenario (such as a significant increase in unemployment) on the credit
quality of a group of borrowers.
168. Banks are encouraged to use a range of scenarios when stress
testing to manage regulatory capital. Scenarios may be historical,
judgmental, or model-based. Key variables specified in a scenario could
include interest rates, score-band segment transition matrices, asset
values, growth rates, and unemployment rates. A bank may choose to have
a single scenario that applies to the entire portfolio, or it may
identify scenarios specific to the various portfolio segments. The
severity of the stress scenario should be consistent with the periodic
economic downturns experienced in the United States. Such scenarios may
be less severe than those used for other purposes, such as testing a
bank's solvency.
169. Given a scenario, a bank then estimates the effect of the
scenario on risk-weighted assets and its future capital ratios relative
to the regulatory minimums. Estimating capital ratios includes
estimating levels of capital (the numerator of the ratio) as well as
measures of risk-weighted assets (the denominator). Suppose the
scenario for a large retail portfolio segment is a specific historical
recession (for
[[Page 62765]]
example, the national unemployment rates of 1980 to 1985). Score-band
transition matrices observed during the recession could be used to
quantify migration between segments and thus supply the new
distribution of segments expected for the current portfolio, given the
scenario. This would allow the calculation of risk-weighted assets that
would be expected in the recession scenario. Default rates would allow
the estimation of the effects on bank income and the consequent capital
effects of credit losses.
170. The scope of this estimation exercise should be broad and
include all material portfolios under IRB. The time horizon of the
stress-testing analysis should be consistent with the specifics of the
scenario and should be long enough to measure the material effects of
the scenario on key performance measures. For example, if a scenario
such as a historical recession has material income and segment
migration effects over two years, the appropriate time horizon is at
least two years.
171. The stress-testing exercise should also take into account a
bank's discretionary actions that affect regulatory capital levels. For
example, a bank's plan to reduce dividends in the face of lowered
income would, if implemented, affect retained earnings and the capital
accounts. Holding more than the minimum regulatory capital requirements
during normal economic conditions is a key discretionary action. Such
discretionary actions must be consistent with the bank's documented
regulatory capital management policy. Because discretionary plans may
or may not be implemented, a bank should estimate the relevant capital
ratios both with and without these actions.
D. Validation
Introduction
172. Validation consists of a wide range of activities intended to
assure that the risk segmentation method and the risk quantification
process are logical and sound and that the segment-level forecasts of
PD, LGD and EAD are accurate.
173. It is often rather difficult to disentangle the effects of the
risk segmentation system from those of the quantification process, in
particular with respect to validation. Some aspects of the validation
of the risk segmentation system can be assessed independently; those
have been discussed in chapter II. However, to a very significant
degree, the accuracy, logic, and statistical powers of the segmentation
system are inextricably intertwined with the accuracy and validity of
the risk parameters estimated on the basis of that segmentation.
Therefore, most of the discussion that follows applies to both the risk
segmentation system and the risk parameter quantification process.
174. The units that develop and test the segmentation and
quantification processes should conduct the types of ongoing validation
discussed below. In addition, there must be independent review
conducted by a separate unit. See chapter V for details.
RS-28: A validation process must cover all aspects of IRB retail
quantification.
175. Validation of the risk quantification process should focus on
the three estimated segment-level retail IRB parameters, PD, LGD, and
EAD. Although the established validation process should result in an
overall assessment of IRB quantification for each parameter, it also
must cover each of the four stages of the quantification process as
described in preceding sections of this chapter (data, estimation,
mapping, and application). Validation of the risk segmentation system
should focus on the design and the ongoing ability of the system to
divide exposures into stable and homogeneous segments that separate
exposures effectively by risk. The process must be updated periodically
to incorporate new developments in validation practices and to ensure
that validation methods remain appropriate. Documentation must be
updated whenever validation methods change.
RS-29: A bank must establish policies for all aspects of
validation. A bank must comprehensively validate risk segmentation and
quantification at least annually, document the results, and report its
findings to senior management.
176. A full and comprehensive annual validation is a minimum for
effective risk management under IRB. More frequent validation may be
appropriate for certain parts of the IRB system and in certain
circumstances; for example, during high-default periods, banks should
compute realized default and loss severity rates more frequently. They
must document the results of validation and report them to appropriate
levels of senior risk management.
RS-30: Banks must use a variety of validation approaches or tools;
no single validation tool can completely and conclusively assess IRB
quantification. A bank's validation processes must include the
evaluation of logic, ongoing monitoring, and the comparison of
estimated parameter values with actual outcomes.
177. Banks must have processes designed to give reasonable
assurances of their quantification systems' accuracy. The ongoing
process to confirm and ensure accuracy consists of:
The evaluation of developmental evidence (evaluation of
logic) or the evaluation of the conceptual soundness of the approach to
quantification;
Ongoing monitoring of system implementation and
reasonableness (verification and benchmarking); and
Back-testing (comparing actual with predicted outcomes).
178. IRB banks are expected to employ all of the components of this
process. However, the data to perform comprehensive back-testing may
not be available in the early stages of implementing an IRB
segmentation and quantification process. In addition, back-testing may
be difficult if a bank's process for modeling risks is evolving
significantly. Therefore, banks may at times need to rely more heavily
on developmental evidence, quality control tests, and benchmarking to
assure themselves and other interested parties that their
quantification processes are likely to be accurate.
Developmental Evidence
RS-31: Banks must evaluate the developmental evidence, or logic,
involved with the development of the risk segmentation system and the
quantification process.
179. Evaluating logic is essential in validating the risk
segmentation system and all four stages of the quantification process.
Developing a risk segmentation system and quantification process
requires banks to adopt methods, choose characteristics, and make
adjustments; each of these actions requires judgment. Validation should
ensure that these judgments are plausible and informed and that they
reflect as much as possible evolving industry practice and the latest
theoretical developments and empirical techniques in the risk
management field.
180. Evaluating developmental evidence involves making a reasonable
assessment of the quality of the quantification process by analyzing
the design and construction of the four stages of quantification.
Developmental evidence is intended to answer these questions: Could the
risk segmentation system be expected to accurately measure the risk
within each segment and to separate the risk between segments? Could
the quantification process be expected to accurately estimate PDs,
LGDs, and EADs? That evidence will have to be revisited whenever the
bank changes its quantification process or its risk
[[Page 62766]]
segmentation system. Since risk analysis at advanced banks is
constantly evolving, the evaluation of developmental evidence is likely
to be an important ongoing part of the process.
181. Generally, the evaluation of developmental evidence will
include a body of expert opinion. Developmental evidence in support of
the risk segmentation system includes the statistical design of the
segmentation in separating exposures into stable and homogeneous
segments and the selection and combination of default risk drivers.
Developmental evidence in support of techniques used in the
quantification process must include information on the logic that
supports the methods chosen for the four stages of quantification. The
developmental evidence will be more persuasive when it includes
empirical evidence on the power of the segmentation system to separate
exposures by risk and the accuracy of the quantification process. The
sufficiency of the developmental evidence will itself be a matter of
informed expert opinion, and experts should be able to draw conclusions
about whether an IRB system would be likely to perform satisfactorily.
Ongoing Process Verification and Benchmarking
RS-32: Banks must conduct ongoing process verification on the
developed risk segmentation system and quantification process to ensure
proper implementation.
182. The second source of analytical support for the validity of a
bank's IRB systems is the ongoing analysis to confirm that the process
continues to perform as intended. Such analysis involves process
verification and benchmarking.
183. Verification activities address the question: Are methods of
separating exposures into segments and quantifying risk parameters
being used, monitored, and updated as designed?
184. Risk segmentation and quantification process verification also
includes monitoring of model overrides. If individuals have the ability
to override models, the bank should have both a policy stating the
tolerance for overrides and a monitoring system for identifying the
occurrence of and reasons for overrides. The performance of overrides
should be tracked separately.
RS-33: Banks must benchmark their risk quantification estimates
against other sources.
185. A bank must also assess whether it has quantified the risk
parameters on the reference data accurately by comparing those
estimates with alternative PD, LGD, and EAD estimates from internal and
industry sources, a process broadly described as benchmarking.
Benchmarking should also include the comparison of the quantification
results derived from different risk segmentation criteria.
186. Benchmarking allows a bank to compare the robustness of its
estimates with those of other estimation techniques and data sources.
Results of benchmarking exercises can be a valuable diagnostic tool in
checking for potential weaknesses in a bank's risk quantification
system. A bank should investigate the sources of substantial
discrepancies between its IRB risk parameters and those observed in the
benchmarking exercise.
Back-Testing
RS-34: Banks must develop statistical tests to back-test their IRB
risk quantification processes. Banks must establish tolerance limits
for differences between expected and actual outcomes, and banks must
have a validation policy that requires and outlines remedial actions to
be taken when policy tolerances are exceeded.
187. A bank must back-test its risk parameter estimates by
regularly comparing actual segment-level default rates, loss
severities, and exposure-at-default experience from its portfolio with
its PD, LGD, EL, and EAD estimates. However, back-testing is only one
element of the broader validation process, and often it will not permit
identification of the specific reasons for discrepancies between
expectations and outcomes. Rather, it will indicate only that further
investigation is necessary.
188. Random chance and many other factors will make discrepancies
between realized outcomes and those predicted by the estimated risk
parameters inevitable. Even for segments with a large number of
exposures, unexpected changes in aggregate economic conditions can lead
to differences between realized and predicted outcomes. However, if
these discrepancies are unduly large, the bank should analyze the
discrepancies to determine the cause. If the discrepancies demonstrate
a systematic tendency to decrease regulatory capital, the nature and
source of the bias requires even more detailed scrutiny.
189. Banks have wide flexibility in developing statistical tests to
back-test their retail risk parameter quantification and retail risk
segmentation systems. Regardless of the back-testing method used, the
bank should establish thresholds or accuracy tolerance levels for
validation results. Results that breach thresholds should bring an
appropriate response; that response should depend on the results and
should not necessarily be to change the design of the segmentation
system or the quantification of the risk parameter estimates. The
bank's validation policy should describe (at least in broad terms) the
types of required responses when relevant action thresholds are
crossed.
IV. Data Maintenance
A. Overview
190. Banks adopting the IRB approach for retail exposures must use
advanced data maintenance practices to support their risk segmentation
systems, quantification processes, validation, and control and
oversight mechanisms described in this guidance. Timely, accurate, and
reliable data are the foundation for retail credit risk management, and
IRB status reinforces the importance of both data and the means to
store, retrieve, and use them.
191. IRB banks will implement different risk segmentation systems
and quantification processes, and therefore their supporting data
structure and elements will differ. Within a bank, moreover, risk
segmentation and quantification processes may differ across business
lines and countries. Therefore, the data structures and practices
adopted will be unique to each bank.
192. While banks will have substantial flexibility in the specific
design of their data maintenance systems, the underlying principle in
this guidance is that the data systems must be of sufficient depth,
scope, and reliability to implement and evaluate the IRB retail credit
risk system. The system must be able to do the following:
Develop a risk segmentation system and assign retail
exposures to segments;
Develop a quantification process and assign risk parameter
estimates to segments;
Validate the IRB risk segmentation system criteria and
architecture;
Validate the IRB risk parameter estimates;
Produce internal and public reports; and
Support the overall retail credit risk management process.
193. Data maintenance systems must enable banks to undertake
necessary changes in their IRB systems and to improve methods in credit
risk management over time. This will require that systems be capable of
providing detailed historical data and new data elements for enhanced
model development and new product testing.
194. This chapter covers retail IRB data requirements and systems
[[Page 62767]]
comprising the loan characteristics specific to the bank's exposures,
the credit characteristics of the bank's borrowers, and the performance
history of the bank's exposures. It is expected that over time
historical data sets used for risk segmentation and reference data for
quantification discussed in chapters II and III will be constructed
primarily from these internal data, but they may be supplemented by
external data when necessary.
B. General Data Requirements
RS-35: The bank must collect and maintain sufficient data to
support its IRB retail credit risk system.
195. Banks must develop data systems capable of supporting their
risk segmentation systems and quantification processes. Given the risk
segmentation criteria and quantification components that are necessary
for the IRB retail credit risk system, the bank must establish
historical databases at the individual loan level.
196. At a minimum, the bank must maintain loan and borrower risk
characteristics that significantly affect origination decisions (for
example, credit score, collateral type, loan-to-value ratio), as well
as ongoing characteristics that significantly affect account management
decisions (for example, refreshed credit scores, utilization, payment
history), whether or not those are used directly in the segmentation
system.
197. The bank must maintain data history at the loan level for all
loans in the portfolio on performance components (for example, balance
and payment history) and loan disposition (for example, prepayment,
default, recoveries) necessary for PD, LGD, and EAD quantification.
198. Data necessary to support segmentation systems and
quantification processes may vary by business line and by country or
wherever the key drivers of risk are unique to the portfolio, different
data elements are available, or different measurements of loss are
appropriate.
199. As discussed in chapter III, banks must use the best available
data for the development of risk segmentation systems and for
historical reference data sets used in risk parameter quantification.
200. Given the bank-specific basis of assigning retail exposures to
segments, over time internal data should become the primary source of
information for estimating IRB risk parameters. Banks using external
data for quantification must demonstrate a strong link between (a) the
bank's process of assigning exposures to a segment and the process used
by the external data source and (b) the bank's internal risk profile
and the composition of the external data.
201. Internal data refer to data on the historical loan and risk
characteristics and the performance of loans in a bank's own
portfolio--even if some input components are purchased from outside
sources. Property appraisals purchased from a third-party appraiser for
updating LTVs of the bank's mortgage exposures would be internal data
on loan characteristics. Credit scores purchased from a credit bureau
for borrowers with existing exposures would be internal data on
borrower characteristics. However, if a bank purchases extensive data
on borrower and loan risk characteristics and the performance of other
banks' portfolios (for example, about a new product with which the bank
has no experience), such data would be considered external.
202. External data may provide more accurate estimates of the risk
parameters, particularly during the early years of IRB implementation.
Banks should document the use of external data and retain those data in
accordance with all of the requirements for internal data. It is
expected that banks will improve the quality of their internal data
over time.
RS-36: Banks must retain all significant data elements used in the
IRB retail credit risk system for at least five years and must include
a period of portfolio stress. This data requirement applies to all
loans and lines that were open at any time during this period.
203. Banks must retain a minimum five-year loan-level history of
the entire portfolio. The standard above establishes the minimum
requirement for banks to retain significant data elements (key risk
drivers) used in the risk segmentation system or in the quantification
of the risk parameters (PD, LGD, and EAD). However, it is expected that
banks will retain additional data elements used in their internal
credit risk management systems.
204. If the most recent period of portfolio stress occurred more
than five years ago, banks must retain additional data to cover the
stress period. These data may be in the form of representative
statistical samples of the portfolio, rather than data from all loans.
In addition, these data need not cover the period between the stress
period and the most recent five-year period. The method of any sampling
should be statistically sound and well documented.
205. Banks must gather and retain disposition data, including
recovery data on defaulted loans (for example, date and dollar value of
recoveries and collection expenses) sufficient to develop LGD and EAD
estimates. For many banks, information related to recoveries and
collection expenses currently exists only at an aggregate level. These
banks should develop interim solutions and a plan to improve data
availability.
206. Banks must retain data on losses (including recoveries,
expenses, and dates) incurred in their revolving portfolios for at
least five years or longer to include a period of high credit losses,
in sufficient detail to calculate the average loss rates and the
volatility of those loss rates over time. These parameters are
necessary to determine eligibility for QRE capital treatment (see
chapter III).
207. Banks are encouraged to retain data beyond the minimum
requirements because they will need robust historical databases
containing key risk drivers and performance components over as long a
historical period as possible to facilitate the development and
validation of new, more advanced methods.
208. A data structure designed to create a historical data
warehouse at the loan level may take many forms. For example, the loan-
level data may be collected and stored at the business line, while
segment-level data inputs may be stored in a centralized database.
Ultimately, the objective is for the bank to be able to access loan-
level data, as needed, using a structure that is sufficiently robust to
support validation and improvements in the IRB system.
Standards for Refreshed Data
RS-37: Banks must retain refreshed data elements related to key
credit risk drivers, performance components, and loan disposition
consistent with advanced credit risk management standards and
commensurate with the risk and size of the program.
209. Maintaining up-to-date information is necessary to support a
more risk-sensitive and accurate capital computation. This information
may consist of refreshed information on segmentation criteria such as
credit scores, as well as refreshed performance indicators such as
payment history. In documenting its segmentation approach, a bank must
specify the time frames for updating data elements involved with the
capital calculation.
210. For many retail products, banks update key loan and borrower
risk characteristics and performance metrics monthly for account
management and risk measurement purposes. For other portfolios or other
data elements, data may be refreshed less frequently. Data
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elements should be updated with a frequency necessary for the reliable
measurement of credit risk for the particular portfolio or business
line and consistent with advanced credit risk management practices.
Loan Sales
RS-38: Banks must maintain data to allow for a thorough review of
asset sale transactions.
211. Asset sales may involve exposures from a variety of portfolio
segments, and sale pricing may not be available at a granular level. It
is important that the bank be able to quantify the impact of removing a
portion of loans from risk segments across the portfolio and the effect
of asset sale activity on loss mitigation strategies. Documentation for
these transactions should be sufficient for supervisors to determine
how asset sale activity affects the integrity of the IRB risk
segmentation method, quantification, and the resulting capital
calculations.
Validation and Refinement
RS-39: Retained data must be sufficient to support IRB validation
requirements.
212. Data should be sufficient to facilitate the back-testing,
benchmarking, ongoing monitoring, and developmental evidence aspects of
the validation process described in chapters II and III.
Data Standards for Outsourced Activities
RS-40: Banks must ensure that outsourced activities performed by
third-party vendors are supported by sufficient data to meet IRB
requirements.
213. Certain processes, such as loan servicing, broker or
correspondent origination, collection, and asset management, may be
outsourced to or otherwise involve third parties. The necessary data
capture and oversight of risk management standards for these portfolios
and processes must be carried out as if they were conducted internally.
Calculating Capital Ratios and Reporting to the Public
RS-41: At each reporting period, aggregate exposures across all
risk segments must be reconciled to ensure that all exposures are
accounted for appropriately.
214. Data retained by the bank will be essential for regulatory
risk-based capital calculations and public reporting under the Pillar 3
disclosures. These uses underscore the need for a well-defined data
maintenance framework and strong controls over data integrity. Total
exposures should be tied to systems of record, and documentation should
be maintained for this process for all reporting periods.
C. Managing Data Quality and Integrity
Documentation and Definitions
RS-42: Banks must develop and document the process for ensuring
data integrity and for delivering, retaining, and updating inputs to
the IRB data warehouse. Also, banks must develop comprehensive
definitions for the data elements used for each credit group or
business line (a ``data dictionary'').
215. Banks must formalize how they manage data. The full
documentation of a bank's data management provides a means of
evaluating whether the data maintenance framework is functioning as
intended. Moreover, banks must be able to communicate precise
definitions of the items to be collected. Consequently, every bank
should develop a ``data dictionary'' to ensure consistent inputs from
business units and data vendors and to allow third parties (such as
auditors or bank supervisors) to evaluate data quality and integrity.
RS-43: Banks must maintain detailed documentation on changes over
time to the risk segmentation system and the quantification process,
including data elements, method, and supporting processes.
216. When changes are made to risk segmentation systems or the
quantification processes, the bank must be able to determine how these
changes affect capital calculations. Detailed documentation is
necessary for the bank to identify the sources of any significant
changes in the capital charges under IRB.
Data Access and Scalability
RS-44: Banks must store data in a format that allows timely
retrieval for analysis and validation of risk segmentation methods and
parameter quantification processes. Data systems must be scalable to
accommodate the growing needs of the business lines, the centralized
data functions, and risk analysis over time.
217. Banks may have a variety of storage techniques and systems to
create their data warehouses and data marts. IRB data standards can be
achieved by unifying existing accounting, servicing, processing, and
workout and risk management systems, provided the linkages between
these systems are well documented and include sufficient edit and
integrity checks to ensure that the data can be used reliably. The data
architecture must be designed to be scalable to allow for growth in
portfolios, data elements, history, and product scope.
Data Gaps
RS-45: If data gaps occur, banks must specify interim measures to
quantify IRB risk parameters and must establish a plan to meet the data
maintenance standards.
218. A data gap is the absence of key data elements necessary for
the design and application of the bank's risk segmentation system, for
the quantification of the risk parameters, or for validation of the
segmentation and quantification systems. One common cause of data gaps
is a merger or acquisition. Merging or acquiring banks must develop a
plan for creating an integrated IRB system. Data gaps may also arise as
banks make the transition to full implementation of IRB systems.
219. As an interim measure, banks should seek to obtain data from
external sources to supplement internal data shortfalls. Alternatively,
the reference data sometimes may be drawn from other sections of the
portfolio, but only when the business lines and loan and borrower
characteristics are sufficiently similar. The bank must document any
transitional steps and should take an appropriately conservative
approach to quantification when data gaps exist.
220. The level of effort placed on filling data gaps should be
commensurate with the current and anticipated volume of exposures to be
incorporated into the bank's IRB system.
V. Control and Oversight Mechanisms
A. Overview
221. Risk management processes and controls, which are the
foundation of retail lending activities, are essential to product
development, pricing, underwriting, account management activities,
portfolio performance forecasting, and economic capital modeling and
long-term capital planning. Banks will use similar processes and
controls to ensure the accuracy of their segmentation, quantification,
and regulatory capital levels.
RS-46: IRB banks must implement an effective system of controls and
oversight.
222. This system must include controls over lending activities,
independent review, transparency, accountability, use of risk parameter
estimates for internal risk management purposes, internal and external
audit, and board and senior management oversight. Banks will have
flexibility in
[[Page 62769]]
how these elements are combined, provided they incorporate sufficient
checks and balances to ensure that the credit risk management system is
functioning properly.
223. IRB banks must have controls and oversight to ensure the
integrity of the risk segmentation system and the accuracy of the risk
parameter estimates used for determining regulatory capital under the
IRB framework. Table 5.1 lists the key components of an IRB control and
oversight system. These controls can be combined or structured to
reinforce one another in a variety of different ways.
Table 5.1.--Control and Oversight Mechanisms
------------------------------------------------------------------------
------------------------------------------------------------------------
Controls over retail lending A structure and system of management
activities. and controls must be established to
ensure credit quality and data
integrity.
Accountability.................... Responsibilities and lines of
authority should be documented in
bank policy.
Independent review................ An independent review process must
evaluate the integrity of the IRB
risk segmentation system and
quantification process.
Transparency...................... The IRB retail credit risk system
must be sufficiently transparent to
enable third parties to understand
key aspects of the segmentation
system and quantification process.
Use of risk estimates............. IRB risk parameter estimates must be
consistent with internal risk
measurements that are used to guide
risk management activities and
financial management.
Internal and external audit....... Internal and external audit must
assess the effectiveness of control
and oversight mechanisms and
overall compliance with the IRB
standards.
Board and senior management Ultimate responsibility for the
oversight. performance of the IRB retail
credit risk system rests with
senior management and the board.
------------------------------------------------------------------------
B. Controls Over Lending Activities
RS-47: Banks must have an independent risk management function that
provides oversight of retail lending activities.
224. An independent risk management function is not directly
involved in the credit decision process. The group's staff members
should be compensated principally on how effectively they manage credit
risk. The risk management function should be responsible for setting
credit policies and ensuring that credit standards are followed. Retail
credit review and compliance management are functions that should
augment and support risk management activities.
RS-48: Banks must have an effective loan review function for retail
credit portfolios.
225. An effective loan review for retail credit is an essential
control for all IRB banks. Loan review must be independent of the
lending process. The numbers, experience, and knowledge of personnel in
loan review should be commensurate with the complexity and risk of the
bank's retail loan portfolios.
226. The scope of reviews should provide an assessment of the
quality of risk management and quantity of risk in retail loan
portfolios. The frequency of reviews should be based on the risk and
size of the portfolios. Reports should clearly identify any concerns.
Banks should have a process for timely resolution of issues and
weaknesses identified by loan review.
RS-49: A quality control function must confirm that all retail
lending activities follow established policies.
227. The purpose of quality control is to provide ongoing assurance
that all retail lending activities adhere to the bank's policies and
procedures. The quality control program should monitor and evaluate the
integrity of credit origination, account management, and collection
activities and should provide timely feedback to senior management.
Without strong quality control systems governing all aspects of the
lending process, the IRB retail credit risk system can be significantly
compromised.
228. The quality control function should be formally established
and operate independently of the loan production process, collections,
and servicing functions. The quality control program should have
established operating procedures and stated requirements for sample
size and selection. Coverage of this function should include
statistically valid samples.
229. The quality control function should generate monthly reports
to appropriate levels of management, outlining findings and identifying
policy exceptions. This information should be used to address
weaknesses in lending activities. The function should seek corrective
action as necessary.
RS-50: Management information systems (MIS) must be sufficiently
comprehensive to monitor and measure credit quality and performance and
to allow proactive and effective risk management.
230. Comprehensive MIS is needed to support risk management.
Reports should measure risk for each stage of the life-cycle for retail
loans and provide early warning of changes in risk profiles. Front-end
reporting generally includes score distribution, score overrides,
exception reporting, and other pertinent borrower and collateral
information. Ongoing portfolio MIS should provide information about the
overall risk profile, portfolio performance, and the direction of risk,
including score distributions, changes in score distributions, early
default analysis, and vintage analysis. Collection reporting should
include delinquency roll rates, static pool cash collection analysis,
and data on volumes and performance for workouts and loss mitigation
programs. Banks must have a process to ensure that reports are accurate
and consistent.
RS-51: Adequate controls and monitoring systems must be in place to
effectively supervise all third parties involved in the lending
process.
231. Vendor management should include a process to identify,
monitor, manage, and control the risks posed by third-party providers.
Vendor arrangements should be established based on adequate due
diligence and should include written contracts that outline duties,
obligations, and responsibilities of both parties. Banks are expected
to provide ongoing oversight for third-party arrangements to ensure
that activities are conducted in a safe and sound manner and in
compliance with the law. Underlying controls should be the same as if
the bank were conducting the activity directly.
232. Banks frequently use third parties such as brokers, dealers,
and correspondents in the loan origination process. While these sources
of new loans provide positive benefits, they also warrant strong
oversight. For loans that involve brokers and dealers, banks should
ensure that adequate controls, such as loan verification activities,
credit scoring, and the collateral valuation process, exist over loan
[[Page 62770]]
processing. Strong control processes over brokers and dealers can help
ensure that underwriting decisions are based on reliable information.
For correspondent originations, banks should have adequate monitoring
systems in place to ensure that loans meet the bank's internal
underwriting requirements.
C. Accountability
RS-52: Bank policies must identify individuals responsible for all
aspects of the retail IRB credit risk system.
233. Responsibilities and lines of authority should be documented
in bank policy. Personnel should have the tools and resources necessary
to carry out their responsibilities, and their performance should be
evaluated against clear and specific objectives. Individuals should be
held accountable for complying with applicable policies and ensuring
that those aspects of the IRB system that are within their control are
unbiased and accurate.
D. Independent Review of Retail IRB Processes
RS-53: Banks must have a comprehensive, independent review process
that is responsible for ensuring the integrity of the IRB risk
segmentation system and quantification process.
234. The review process should be independent of the individuals
who develop the underlying segmentation systems and perform
quantification activities. The activities of this review process could
be distributed across multiple areas or housed within one unit.
Organizations will choose a structure that fits their management and
oversight framework. For example, the independent review might be
conducted by loan review or other similar units, subject to the
independence requirement above. Individuals performing the reviews
should possess the requisite technical skills and expertise.
235. The review should be conducted at least annually and should
encompass all aspects of the process, including:
Compliance with policies and procedures;
Design and effectiveness of the segmentation system;
Quantification process and accuracy of parameter
estimates;
Model development, use, and validation;
Adequacy of data systems and controls; and
Adequacy of staff skills and experience.
236. The review process should identify any weaknesses, make
recommendations, and ensure corrective action. Significant findings of
IRB reviews must be reported to senior management and the board.
E. Transparency
RS-54: IRB banks must have a transparent retail IRB process.
237. Transparency is the ability of third parties, such as loan
reviewers, auditors, and supervisors, to understand the design,
operations, and accuracy of the risk segmentation system and
quantification process for retail IRB.
238. Transparency in the risk segmentation system and
quantification process may be achieved through documentation that
covers the following:
The segmentation design, including selection of risk
drivers, use of refreshed information, and granularity of segmentation;
Parameter estimates and the processes used for their
estimation, including significant adjustments and assumptions;
Data requirements;
Documentation for model development, implementation, and
validation; and
Specific responsibilities of and performance standards for
individuals and units involved in the retail IRB process and its
oversight.
F. Use of Risk Estimates
RS-55: Retail IRB risk parameter estimates must be consistent with
risk estimates used to guide day-to-day retail risk management
activities.
239. Banks must demonstrate that IRB segmentation and IRB risk
parameter estimates are consistent with those used by bank management
in its planning, execution, and oversight of retail lending activities.
Risk drivers for IRB segmentation purposes should correspond to risk
drivers used as part of the overall risk management of the lines of
business. IRB risk parameter estimates of PD, LGD, and EAD should be
incorporated in credit risk management, internal capital allocation,
and corporate governance. Banks should compare actual default rates
with PD and actual dollar loss rates with internal forecasts for each
of the retail IRB products.
G. Internal and External Audit
RS-56: Internal and external audit must annually evaluate
compliance with the retail IRB capital regulations and supervisory
guidance.
240. Internal audit should report to the board and management on
the bank's compliance with the retail IRB standards, including ones
applicable to the segmentation system and estimation of the IRB risk
parameters. This report will allow the board and management to affirm
that the risk segmentation system, the quantification process, and the
surrounding controls are in compliance with IRB standards. This will be
critical for public disclosure and ongoing review by supervisors. As
part of its review of control mechanisms, internal audit should
evaluate the depth, scope, and quality of the independent review and
quality control functions.
241. As part of the process of certifying financial statements,
external auditors should, to the extent appropriate under applicable
auditing and professional standards, ascertain whether the IRB system
is measuring credit risk appropriately and confirm that the bank's
regulatory capital position is fairly presented. Auditors should also
evaluate, to the extent appropriate under these standards, the bank's
internal control functions relating to regulatory capital and its
compliance with the risk-based capital regulation and supervisory
guidance.
H. Corporate Oversight
RS-57: The full board or a designated committee of the board must
review and approve key elements of the IRB system.
RS-58: Senior management must ensure that all components of the IRB
system, including controls, are functioning as intended and comply with
the risk-based capital regulation and supervisory guidance.
242. Senior management's oversight is expected to be more active
than that of the board of directors. Senior management must have an
extensive understanding of credit policies, underwriting standards, and
account management activities (including collections) and must
understand how these factors affect the IRB risk segmentation system,
risk-parameter estimates, and data maintenance requirements.
243. The depth and frequency of information provided to the board
and senior management must be commensurate with their oversight
responsibilities and the condition of the bank. The board should be
provided with periodic high-level reports summarizing the performance
of the retail IRB credit risk system. Senior management should receive
more detailed reports covering topics such as:
Risk profile by retail portfolio;
Actual losses by risk segment compared with the IRB risk
parameter estimates (PD, LGD, and EAD), with emphasis on unexpected
results;
Changing portfolio trends and risks;
[[Page 62771]]
Reports measuring changes in regulatory and economic
capital;
Reports generated by the independent review function,
quality control, audit, and other control units; and
Results of capital stress testing.
244. Although all of a bank's controls must function smoothly,
independently, and in concert with the others, the direction and
oversight provided by the board and senior management is critical to
ensuring that the IRB system is functioning properly.
245. For retail portfolios that are managed across legal entities,
the board of directors and senior management of each insured depository
institution must have sufficient information about its exposures to
accurately assess and report on its own risk.
246. Senior management should confirm that activities conducted
across multiple legal entities meet the following conditions:
Products are managed centrally using consistent policies;
Segments that cross multiple legal entities meet the
requirements of chapter II to ensure that they have homogeneous risk
characteristics;
Exposures outside the United States are not grouped with
domestic exposures; and
Validation and back-testing activities include the
additional step of ensuring that minimum capital requirements for each
entity are accurate.
Appendix A: Process Analysis Examples
Example 1: A Seamless Application of the Four Stages of
Quantification (See Paragraph 70)
Consider a bank that has been making indirect installment loans
through furniture stores for a number of years. Seven years of
internal data history are available, over a period including a
significant recession. The bank has segmented this portfolio over
the whole period in a consistent manner: by bureau score, internal
behavioral score, and monthly disposable income. In addition, LGDs
for this portfolio have demonstrated significant cyclical
variability over the period covered by the bank's data history.
The bank can empirically show that the participating furniture
retailers, underwriting criteria, and collection practices have
remained reasonably stable over the seven-year period, and the
definition of default has been consistent with the IRB definition.
However, there are frequent changes in the bank's products and in
the borrowing population that affect the risk characteristics of its
loans, so the bank uses only the most recent seven-year data history
as new data become available (assuming that the data includes a
period of recession).
The PD is calculated as the average of the seven annual PDs. The
LGD is the loss severity observed during periods when credit losses
for this type of product have been high. The EAD for non-defaulted
loans is calculated as the outstanding loan amount at the time of
capital measurement plus any accrued but unpaid interest and fees.
In this example, the four stages have not been explicitly
mentioned or applied. Nonetheless, at the level of detail presented
(which is clearly somewhat simplified), the quantification appears
to satisfy most of the standards in the chapter (subject, of course,
to validation).
If the bank desires, it can put its quantification into the
following four-stage framework:
a. The bank's own historical data serve as the reference data;
b. Estimation consists of calculating the historical average PD,
the recessionary LGD, and the outstanding balance by segment;
c. Mapping consists primarily of ensuring that the segmentation
schemes and the definition of default are consistent between the
reference data portfolios and the bank's existing portfolios; and
d. Application is a matter of using the risk parameter estimates
from the reference portfolios for each segment of the existing
portfolios in the regulatory capital formulas.
Thus, as discussed in the main chapter text, the four stages of
quantification are not intended as a set of rigid requirements that
must be followed in every detail in all circumstances. Rather, they
should be seen as a conceptual framework, and as an analytical and
implementation guide for those institutions whose data histories,
institutional circumstances, or unusual complexities require the
greater detail and specificity.
Example 2: Quantification of the PD for First-Lien Mortgages (See
Paragraph 106)
a. For the past four years a mortgage portfolio has been
concentrated in a less risky geographic region than the historical
portfolio, whose data history goes back several more years. The bank
analyzes external mortgage data by geographic region over the same
time period to identify regional differences in default rates.
Analysis of the reference data indicates similar regional
differences.
b. The recent four-year period does not include a period of
stress, so the bank uses its full internal data history to encompass
a period of stress. To estimate the PD parameter over a long run of
data history that is also comparable to the current portfolio, the
bank develops a statistical model of the PD over combined internal
and external performance history. The variables used as PD
predictors included geographic region, loan and borrower risk
characteristics, loan-to-value ratios, and lagged mortgage
foreclosure rates by region. With this model the bank claims that it
is able to fully utilize its 13-year history of internal data as
well as take into account the effects of the more recent geographic
change in its portfolio.
Process Analysis for Example 2:
Data--The existing portfolio of first-lien mortgages is
segmented by LTV, credit score, tenor, fixed-rate vs. ARM, and debt-
to-income ratio. For a given segment, the bank has good historical
data from its own portfolio. The reference data consist of nine
years of lifetime internal performance history for loans originated
between 1990 and 1999, which are concentrated within the riskier
geographic region, plus four years of recent internal history (2000-
2003). The internal data is supplemented by external regional
mortgage data over the full 13-year history (1999-2003).
Estimation--The bank builds a statistical model that estimates
PD as a function of regional foreclosure rates for the previous two
quarters, the loan-to-value ratio, credit score, debt-to-income
ratio, loan tenor, and geographic region, and it builds separate
models by product type (e.g., fixed-rate vs. ARM). A similar model
of LGD is estimated using a regression model that incorporates
economic factors. An LGD estimate reflective of periods of high
credit losses in the mortgage market is produced by stressing the
economic factors in the model. The model results are robust in terms
of the standard statistical diagnostic tests. The model has
continued to perform satisfactorily in validations outside the
development sample.
Mapping--Since the 1990-1999 period, the bank has shifted much
of its first-lien mortgage business to a different region of the
country, one that historically has experienced lower default rates.
The bank segments its portfolio by region and borrower and loan
characteristics utilized in the model to produce a long-run average
PD estimate by region, so as to take the lower regional default
rates into account. An ``economic downturn'' LGD is also calculated
by the same segmentation. Therefore, in mapping from the reference
data to its existing portfolio data the bank assigns the average PD
and the economic downturn LGD per segment of exposures in the
existing portfolio, as estimated by the models.
Application--The bank will now apply the regression models to
its existing portfolio to estimate the PD and LGD values for each
segment in the first-lien mortgage portfolio. It will measure EAD
for non-defaulted loans as the present outstanding balance per
segment plus any accrued but unpaid interest and fees. Then it will
enter the three risk parameters into the IRB mortgage formula to
assess the minimum required regulatory capital for each segment.
Example 3: PD Estimation From Dollars Defaulted and Present
Portfolio Value (See Paragraph 108)
Paragraph 101 defines PD in terms of accounts, not dollars: the
number of defaulted accounts during the course of a year divided by
the number of accounts open at the beginning of the year. This
example discusses issues involved with methods that attempt to
derive PD from dollar loss rates. If a bank chooses to derive a PD
in this manner, the bank will need to consider a variety of factors
to ensure that the PD estimate is an accurate reflection of the
expected rate of defaults on an account basis.
a. A credit card bank directly measures its average dollars of
economic losses for each segment and uses the percentage of dollars
defaulted, rather than as the percentage of loans defaulted, as the
estimate of PD.
[[Page 62772]]
Specifically, the ratio employed is the gross loss divided by the
exposure at default. The gross loss (before recovery) is directly
measured on a segment of accounts over a one-year time horizon. The
bank estimates exposure at default (EAD) for a segment as the
current outstanding balances plus the expected drawdowns on open
balances if all accounts default (including accrued but unpaid
interest and fees at the time of default).
b. The bank's risk segmentation system separates exposures by
size of credit line and credit line utilization as well as by credit
score. If the segmentation appropriately controls for current
balances and credit lines, then it should produce accurate estimates
of both PD and EAD. The bank regularly validates the accuracy of the
EAD estimates and the consistency of the percentage-of-dollars-
defaulted measure with the account default rate.
Process Analysis for Example 3:
Data--The historical reference data consist of measurements of
the outstanding dollar balances and open credit lines at the
beginning of the year. For accounts that defaulted over the
following year the gross defaulted balances are also measured. The
aggregate dollar amounts are measured for each segment.
Estimation--The bank's dollar PD parameter is estimated as the
long-run average of the one-year PDs. Each one-year PD is measured
as the gross balances of defaulted loans divided by the estimated
EAD. The following example illustrates why granular segmentation by
balance and credit line can be important. In the first row of the
following table, all loans with account PD equal to 1% are grouped
together in a single segment. Using an estimatedLEQ of 0.7 derived
from historical reference data, the Gross Loss / ED measure equal 1%
and is equivalent to the account PD. In the second row of the table
however, although all loans with account PD equal to 1% are still
included in the segment, the Gross Loss/EAD measure has fallen to
0.94% and is therefore no longer an acceptable proxy for the account
PD.
--------------------------------------------------------------------------------------------------------------------------------------------------------
Average Average Estimated
balance credit Number Total Total percent Estimated Gross loss/
Account PD per line per accounts in outstanding undrawn drawdown EAD Gross loss EAD
account account segment balance lines (LEQ)
--------------------------------------------------------------------------------------------------------------------------------------------------------
1.0%............................... $225 $600 2,000 $450,000 $750,000 70% $975,000 $9,750 1.0%
1.0%............................... $285 $760 2,000 $570,000 $950,000 70% $1,235,000 $11,550 0.94%
--------------------------------------------------------------------------------------------------------------------------------------------------------
The reason for this discrepancy can be found in the granularity
of the bank's segmentation process. By grouping together all loans
with account PD equal to 1%, the bank is combining loans with
significantly different average balances per account and average
credit lines. They are also using an estimate for LEQ (0.7) based on
historical data for particular portfolios of loans with PD equal to
1% that is not accurate for portfolios with different distributions
of loans by outstanding balances and credit lines.
This can be seen by looking at a finer segmentation of the
portfolios. In the table below, the segment from the top row in the
previous table is divided more finely, by average balance and credit
line. The historically estimated LEQs differ significantly between
the segments, and the 0.7 LEQ in the previous table represents a
weighted average of the two different segment values. Because the
LEQ estimate is the weighted average of the two segment LEQs, then
as long as the distribution of accounts between the two segments
remains steady the Gross Loss/EAD measure shown in the first table
equals 1% and is equivalent to the account PD.
--------------------------------------------------------------------------------------------------------------------------------------------------------
Average Average Estimated
balance credit Number Total Total percent Estimated Gross loss/
Account PD per line per accounts in outstanding undrawn drawdown EAD Gross loss EAD
account account segment balance lines (LEQ)
--------------------------------------------------------------------------------------------------------------------------------------------------------
1.0%............................... $150 $400 1,000 $150,000 $250,000 90% $375,000 $3,750 1.0%
1.0%............................... $300 $800 1,000 $300,000 $500,000 60% $600,000 $6,000 1.0%
------------------------------------
Weighted
Aggregated 1% PD Segment Average LEQ
-----------------------------------------------------------------------------------------------------
1.0%............................... $225 $600 2,000 $450,000 $750,000 70% $975,000 $9,750 1.0
--------------------------------------------------------------------------------------------------------------------------------------------------------
In the next table, the larger segment (from the second row in
the first table above) is divided into two finer segments in the
same manner as previously. In fact, the average balances, average
lines, and LEQs are all the same as in the previous case. The only
change is in the proportion of accounts in each segment. However, by
using the LEQ of 0.7 derived from the coarser segmentation, the bank
estimated Gross Loss/EAD as 0.94 in the second row of the first
table. The finer, more accurate, weighted LEQ of 0.62 produces a
Gross Loss/EAD measure of 1.0%, equivalent to the account PD.
Segmentation by PD, Balance and Credit Line
--------------------------------------------------------------------------------------------------------------------------------------------------------
Estimated
Average Average Number Total Total percent Estimated Gross loss/
Account PD balance per credit line accounts in outstanding undrawn drawdown EAD Gross loss EAD
account per account segment balance lines (LEQ)
--------------------------------------------------------------------------------------------------------------------------------------------------------
1.0%............................... $150 $400 200 $30,000 $50,000 90% $75,000 $750 1.0%
1.0%............................... $300 $800 1,800 $540,000 $900,000 60% $1,080,000 $10,800 1.0%
------------------------------------
Weighted
Aggregated 1% PD Segment Average LEQ
-----------------------------------------------------------------------------------------------------
1.0%............................... $285 $760 2,000 $570,000 $950,000 62% $1,155,000 $11,500 1.0%
--------------------------------------------------------------------------------------------------------------------------------------------------------
[[Page 62773]]
Thus we see that, with the proper segmentation criteria and
sufficiently granular segmentation, the Gross Dollar Loss/EAD
measure can produce a PD that is equivalent to the correct account
PD. If a bank were to use the coarser segmentation shown in the
first table (i.e., all accounts with account PD=1), the bank would
have to carefully monitor the changes in distribution of accounts
within this broader segment and update the weighted average LEQ on a
timely basis. Given how rapidly portfolio composition can change in
credit card markets, this may be a challenging task.
Note: Another method of calculating the PD from dollar
measurements used at some institutions is to estimate the PD for a
segment as the accumulated gross losses at the end of a one-year
period divided by the outstanding balances at the beginning of the
year. This does not provide an estimate equivalent to an account
default rate if initial balances on accounts that eventually default
are significantly different from those that do not default, which is
generally the case. Consider the examples in the following table.
(For simplicity, these examples assume there is no amortization of
principal over the year.)
----------------------------------------------------------------------------------------------------------------
Average Average
Number Total beginning beginning Gross Losses/
Number total defaulted Account PD beginning balance non- balance Total gross beginning
accounts accounts outstanding defaulted defaulted losses outstanding
balances accounts accounts balances
----------------------------------------------------------------------------------------------------------------
1000 20 2.0% $1,000,000 $1.005 $750 $15,000 1.5%
1000 20 2.0% $1,000,000 $995 $1,250 $25,000 2.5%
----------------------------------------------------------------------------------------------------------------
As shown in the table, if balances on accounts that default are
higher than balances on those that do not (which is the more common
situation), then the Gross Losses/Outstanding Balances measure will
overestimate PD. Conversely, if defaulted accounts have lower
balances, the Gross Loss/Outstanding Balances measure will
underestimate PD.
Mapping--To develop a risk segmentation system that produces
homogeneous and stable segments, the bank identifies the drivers of
both default risk and drawdowns and then segments by these drivers.
The mapping would involve linking segments in the reference data to
segments in the present portfolio using the same risk segmentation
system. However, during recessionary periods, the bank monitors
changes in the market and economic environment that could change the
relationships between default risk and drawdowns and the underlying
drivers of these risks. If there were systematic changes, then the
risk segmentation system would need to be updated.
Application--The application is generally a straightforward,
direct application of estimates from segments in the reference data
to segments in the existing portfolio. Estimates would be adjusted
if the default risk were expected to change systematically from
previous periods, for example, because of a trend toward higher
credit lines.
Example 4: PD Quantification With Adjustments for Seasoning (See
Paragraphs 109-112)
a. PDs for a bank's credit card portfolio exhibit a
characteristic time profile by age--a seasoning curve. As a result
of the bank's analyses, the shape of this seasoning curve has been
established by specific products and borrower credit quality at
origination utilizing data from vintages over the last five years.
The bank regularly analyzes new vintages to capture changes in the
characteristic time profile of PDs over changing economic and market
environments. Systematic changes are incorporated into new seasoning
curves.
b. The risk segmentation system criteria for seasoned and
unseasoned loans include updated account age, or ``time on books.''
c. For unseasoned loans, if seasoning effects are material, the
PD is estimated as an annualized cumulative default rate over the
remaining expected life of the loans. For seasoned loans the PD
should simply be measured as a long-run average of the one-year-
ahead PDs.
Process Analysis for Example 4:
Data--The main reference data consists of five years (or more)
of portfolio history. Segments are defined by updated borrower,
product, and loan characteristics including account age.
Supplemental reference data consist of vintage analyses of similar
products originated within the same time period, providing seasoning
curves specific to borrower credit quality at origination, product,
and loan type. Given the level of the annualized default rate
observed in the early history of a cohort, the historical seasoning
curves should indicate the trend that PDs follow over the remaining
expected life of the loans.
The bank presents analyses indicating that the seasoning curve
can be reasonably specified by borrower credit quality at
origination and carefully monitors new cohorts for any deviation of
the time profile of one-year PDs from the corresponding seasoning
curve.
Estimation--For seasoned loans, a long-run average PD is
calculated for each segment by updated borrower, product, and loan
characteristics, including loan age. For unseasoned loans, the PD is
the estimated annualized cumulative default rate over the remaining
expected life of the loans.
Mapping--The risk segmentation system of the present portfolio
is the same as that employed for the reference data. This makes the
mapping straightforward along the lines of refreshed borrower credit
quality. However, the bank should ensure while mapping that the
product characteristics in the reference data are mapped to
equivalent product characteristics in the present portfolio.
Application--At the application stage, the long-run PD estimated
from the reference data may simply be applied to the matching
segments in the existing portfolio.
Appendix B: Technical Examples
Example 1 From General Standards (See Paragraph 91 and Standard RS-
13)
The following example illustrates one possible solution when
sufficient internal historical data is not available for an entire
portfolio. The bank may be able to identify sub-samples within its
portfolio that experienced increased default rates during the
available length of history, even though the aggregate portfolio may
not have realized such a trend. For example, data may be available
from local or regional recessions in New England (late 1980s and
1990-1995), Texas (1983-1989), or California (1991-1995). The bank
must be able to demonstrate that the drivers of high default rates
in these regional recessions can be extrapolated to the entire
portfolio as well as justify and document any resulting adjustments
that would be necessary in the mapping and application stages.
Example 2 From General Standards (See Paragraphs 93 and 130 and
Standard RS-14)
At least two common types of mapping challenges may arise in
regard to PD, LGD, and/or EAD quantification:
a. First, even if similarly named characteristics are available
in the reference data and portfolio data, they may not be directly
comparable. For example, in a portfolio of auto loans, the
particular types of auto loans (for example, new or used, direct or
indirect) may vary from one application to another. Hence, a bank
should ensure that linked characteristics are truly similar.
Although adjustments to enhance comparability can be appropriate,
they must be rigorously developed and documented.
b. Second, levels of aggregation may vary. For example, the
reference data may only broadly identify collateral types--say,
broad categories of automakers. The bank's information systems for
its portfolio might supply more detail such as auto makes and models
plus the age and condition of vehicles. To apply the estimates
derived from the reference data, the bank may regroup the existing
portfolio in order to match broader aggregations in the reference
data.
Example 3 From the PD Estimation Standards (See Paragraph 107)
The following examples illustrate possible PD estimation methods
that might appear in bank practice and potential problems with some
methods:
[[Page 62774]]
Example 3a: Adjustments When PDs Are Measured Over a Shorter Time
Horizon and Then Annualized
In practice the account default rate may be estimated at a
monthly or quarterly rate and ``annualized'' to produce the
equivalent yearly default rate. However, this annualized rate may
not be accurate over a one-year horizon if the bank does not track
loans that migrate within the year. For example, consider a segment
with very high credit quality--call it the ``superprime'' segment.
Over the year, many accounts that default have first migrated to
lower credit quality segments at stages during the year. So,
annualizing the quarterly default rate for the ``superprime''
portfolio would be an underestimate of the true one-year default
rate. The PD should be measured from actual portfolio performance of
all loans in the bucket over a full one-year horizon.
The following example presents this issue. The quarterly
transition rates between the three non-default rating classes
(``superprime,'' ``prime,'' and ``subprime'') and the transition
rates into default are listed below:
----------------------------------------------------------------------------------------------------------------
Beginning of quarter
---------------------------------------------------------------
Superprime Prime Subprime Default
----------------------------------------------------------------------------------------------------------------
End of Quarter:
Superprime.................................. 94% 2% 1% 0
Prime....................................... 5% 94% 3% 0
Subprime.................................... 1% 3% 95% 0
Default..................................... 0.1% 1% 2% 100%
----------------------------------------------------------------------------------------------------------------
A particular segment is 100% superprime at the beginning of a
one-year time horizon. Over each quarter some accounts migrate into
lower quality states with correspondingly higher default rates. As a
result of this migration, the population distribution among the
rating classes changes over each quarter. The Superprime, Prime, and
Subprime columns of the following table show the changing
distribution for these loans that were all superprime as of January
1. For example, at the end of the second quarter, only 88% of the
surviving loans remain superprime, 9% are now prime, and 2% are
subprime.
The last column represents the cumulative default rate for these
formerly Superprime loans. That is, at the end of the second quarter
0.26% will have defaulted; at the end of the third quarter, 0.49%
will have defaulted, and at the end of the year, a total of 0.77% of
the original all-Superprime segment will have defaulted, which is
substantially higher than four times the quarterly default rate, or
0.4%.\11\
---------------------------------------------------------------------------
\11\ The cumulative default rate is the sum of the defaults at
the end of the previous period plus new defaults during the period
just ended. The new defaults are determined as the sum of the
proportions of loans in each rating category times the respective
default rate for that category. For example, at the end of the
second quarter, the new defaults equal the 94% of the loans that
were still Superprime at the beginning of the period times the
Superprime default rate of 0.1% plus the 5% of loans that had become
Prime times the Prime default rate of 1%; plus the 1% of loans that
had become Subprime times the Subprime default rate of 2%. This
yields a default rate during the second quarter of 0.25%, which is
added to the 0.1% default rate from the end of the first quarter to
produce a cumulative rate of 0.26% at the end of the second quarter.
----------------------------------------------------------------------------------------------------------------
Superprime Prime Subprime Default
Time (percent) (percent) (percent) (percent)
----------------------------------------------------------------------------------------------------------------
January 1....................................... 100 0 0 0
End of Quarter 1................................ 94 5 1 0.10
End of Quarter 2................................ 88 9 2 0.26
End of Quarter 3................................ 83 13 3 0.49
---------------
End of Quarter 4................................ 78 17 4 0.77
----------------------------------------------------------------------------------------------------------------
Note that this illustration assumes that the transitions from
one quarter to the next are the same for each quarter throughout the
year. In practice, they may vary from quarter to quarter for many
reasons.
Example 3b: Portfolio Growth and the Timing of Default Measurements
The method and timing of the measurement of portfolio growth and
defaulted accounts for a pool can also bias the PD estimates.
Defaulted accounts would be measured at year-end and should not
include accounts opened within the year. The total number of
accounts should be measured at the beginning of the year. When the
total number of accounts is measured concurrently with the number of
defaulted accounts, if the total pool size increases (decreases)
substantially over the one-year observation period, the PD could be
underestimated (overestimated) substantially.
In the following example, the portfolio shows four alternative
growth rates over one year: (1) The portfolio shrinks by 5 percent,
(2) the portfolio shrinks by 10 percent, (3) the portfolio grows by
5 percent, or (4) the portfolio grows by 10 percent:
The portfolio starts at the beginning of the year with 1 million
accounts and $100 million in outstanding balances, or an average of
$100 per account. For simplicity it is assumed that the PD and
average account balance remain constant over the year while the
number of accounts changes.
----------------------------------------------------------------------------------------------------------------
Total portfolio accounts Accounts PD front PD from end of
Annual portfolio growth rate -------------------------------- defaulted by start of year year portfolio
Start of year End of year end of year portfolio (percent)
----------------------------------------------------------------------------------------------------------------
-5%............................. 1,000,000 950,000 20,000 2.0 2.1
-10%............................ 1,000,000 900,000 20,000 2.0 2.2
5%.............................. 1,000,000 1,050,000 20,000 2.0 1.9
10%............................. 1,000,000 1,100,000 20,000 2.0 1.8
----------------------------------------------------------------------------------------------------------------
Note: It is assumed that all 20,000 defaults that occurred during the year were accounts that were part of the
portfolio on January 1. The Other Retail risk weight curve was used for this example, and LGD is assumed to be
90% in all four cases.
[[Page 62775]]
This example shows clearly how the use of the end-of-year
portfolio size, rather than the number of accounts that were open at
the beginning of the year, produces significant misestimation of PD,
which should equal 2.0% in all four cases.
Example 4 From the PD Estimation Standards (See Paragraph 102)
A bank uses the last five years of internal default history to
estimate a long-run average PD for each pool of retail exposures.
However, it recognizes that the internal experience does not include
any years of portfolio stress. To remedy this and still take
advantage of its experience, the bank uses external loss data to
adjust the PD estimates upward in the years of economic downturn or
systematic economic stress. (An example of an external data source
would be historical mortgage default data purchased from a vendor.).
Using the external data, the bank creates an index by calculating
the ratio between each year's mortgage default rate per pool and the
long-run average rate per pool of exposures over the last five
years, both from the external data. The bank assumes that the
relationship observed in the external data applies to its own
mortgage portfolio, and it uses the index to adjust the estimates
for the internal data accordingly. If the bank rigorously validates,
justifies, and documents these adjustments, it would satisfy the
standard.
Example 5 From the LGD Estimation Standards (See Paragraphs 127-
129)
A bank determines that a business unit forms a homogeneous pool
for the purposes of estimating loss severity. That is, although the
loans in this pool may differ in some respects, the bank determines
that they share a similar loss experience in default. The bank must
provide reasonable support for its claim through an analysis of
lending practices and available internal data. If it does so
convincingly, a common pool across a business unit is consistent
with the standard.
Example 6 From the LGD Estimation Section (See Paragraphs 127-129)
A bank divides observed defaults in the reference pool according
to geographic region and loan-to-value in a mortgage portfolio. One
of the pools has too few observations to produce a reliable
estimate. By augmenting the loss data in this pool with data from
other pools (for example, neighboring geographic regions with the
same LTV), the bank calculates an estimate of the severity. The bank
must validate, justify, and document the accuracy of this proxy
value.
In another example, a bank segments its default data in a credit
card business unit by a number of borrower, loan, and product
characteristics. Although the available internal historical evidence
indicates a higher LGD, the bank judgmentally assigns a loss
severity of 70 percent to a particular prime pool. The bank
justifies this reduction in the LGD by claiming that it will do a
better job of following policies for monitoring credit card
performance in the future, for example, repricing accounts to
generate more income and monitoring lines for problem accounts. Such
an LGD adjustment is not appropriate because it is based on
anticipated future performance rather than realized performance.
Example 7 From the LGD Estimation Standards (See Paragraphs 127-
129)
Timing of Defaults and Recoveries.
A bank measures recovery rates over time for a business line by
loan characteristics. The recoveries are measured as an aggregate
stream of cash inflows monthly or quarterly from all defaulted loans
on book and not based on recoveries from a fixed group of defaulted
loans. Collection costs are assessed as a proportion of the
defaulted balances. Therefore loss severity rates are measured in
the aggregate as:
[GRAPHIC] [TIFF OMITTED] TN27OC04.002
where all dollar values are measured concurrently.
If defaulted balances are approximately constant over time, this
method does not create any problems. However, when defaulted
balances change over time, the bank should adjust for changes in the
volume of defaulted accounts, since the use of recoveries from a
prior group of defaulted accounts could underestimate the loss
severity when aggregate defaulted balances were higher in a previous
period, and overestimate them when defaulted balances were lower in
a previous period.
The following example demonstrates how the loss severity can be
underestimated during periods of decreased defaulted balances when
the loss severity is measured as the present defaulted balances
minus recoveries from the previous period's defaulted balances
(using a fixed 30 percent recovery rate) divided by the current
period's defaulted balances.\*\
----------------------------------------------------------------------------------------------------------------
One-year $Recoveries 30% Measured loss
Portfolio balances (EAD) default Defaulted net discounted severity (True
rate balances recovery rate LGD = 70%)
----------------------------------------------------------------------------------------------------------------
$1,000,000............................................ 2.00% $20,000 $6,000 70%
1,000,000............................................. 1.80 18,000 6,000 67
1,000,000............................................. 1.60 16,000 5,400 66
1,000,000............................................. 1.20 12,000 4,800 60
----------------------------------------------------------------------------------------------------------------
[[Page 62776]]
Thus, while an accurate measure of LGD would remain constant at
70% over the entire four-year period, this example shows how the use
of the current year's defaulted balances, during a period when these
balances are trending downward, leads to underestimates of LGD that
grow more significant each year.
Example 8: The Effect of the Purchase Discount on EAD and LGD (see
paragraph 138)
Suppose a bank buys a QRE portfolio at a 5 percent discount.
Assuming that PD and recoveries remain unchanged, EAD and LGD both
change because of the discount. The discount does not act as a
reserve against EL or as a capital offset against UL. For the
purchasing bank, the newly purchased portfolio is initially put on
the books (EAD) at the discounted price the bank paid. The EL and UL
numbers would change from those of a portfolio bought or originated
at par as follows:
------------------------------------------------------------------------
------------------------------------------------------------------------
Recoveries..................................................... $50
Asset face value............................................... 100
Asset correlation.............................................. 4
PD............................................................. 5
------------------------------------------------------------------------
------------------------------------------------------------------------
No discount 5% discount
------------------------------------------------------------------------
EAD......................................... $100 $95
Loss = EAD -recovery........................ 50 45
LGD = Loss/EAD.............................. 50.0 47.4
EL = PD x LGD x EAD......................... 2.50 2.25
UL (capital) per $ of EAD................... 4.87 4.61
IRB capital = UL per $ x EAD................ 4.87 4.38
------------------------------------------------------------------------
List of Acronyms
ALLL Allowance for loan and lease loss
EAD Exposure at default
EL Expected loss
FFIEC Federal Financial Institutions Examination Council
GAAP Generally Accepted Accounting Principles
HELOC Home Equity Line of Credit
IRB Advanced internal ratings-based approach (Basel II)
K Unexpected loss capital requirement
LEQ Loan equivalent exposure
LGD Loss given default
LTV Loan-to-value ratio
MIS Management Information Systems
PD Probability of default
PMI Private Mortgage Insurance
QIS Quantitative Impact Study
QRE Qualifying revolving retail exposures
R Asset value correlation (AVC)
RS Retail Standard
RWA Risk-weighted assets
UL Unexpected loss
Dated: October 15, 2004.
Julie L. Williams,
Acting Comptroller of the Currency.
By order of the Board of Governors of the Federal Reserve
System.
Dated: October 15, 2004.
Jennifer J. Johnson,
Secretary of the Board.
By order of the Board of Directors.
Dated at Washington, DC, this day of October 18, 2004.
Robert E. Feldman,
Executive Secretary.
By the Office of Thrift Supervision.
Dated: October 14, 2004.
James T. Gilleran,
Director.
[FR Doc. 04-23771 Filed 10-26-04; 8:45 am]
BILLING CODE 4810-33-P; 6210-01-P; 6714-01-P; 6720-01-P