PROPOSED SUPERVISORY GUIDANCE ON INTERNAL RATINGS-BASED SYSTEMS FOR CORPORATE CREDIT AND OPERATIONAL RISK

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Federal Register / Vol. 68, No. 149 / Monday, August 4, 2003 / Notices

DEPARTMENT OF THE TREASURY

Office of the Comptroller of the

Currency

[Docket No. 03–15]

FEDERAL RESERVE SYSTEM

[Docket No. OP–1153]

FEDERAL DEPOSIT INSURANCE

CORPORATION

DEPARTMENT OF THE TREASURY

Office of Thrift Supervision

[No. 2003–28]

Internal Ratings-Based Systems for

Corporate Credit and Operational Risk

Advanced Measurement Approaches

for Regulatory Capital

AGENCIES: Office of the Comptroller of

the Currency (OCC), Treasury; Board of

Governors of the Federal Reserve

System (Board); Federal Deposit

Insurance Corporation (FDIC); and

Office of Thrift Supervision (OTS),

Treasury.

ACTION: Draft supervisory guidance with

request for comment.

SUMMARY: The OCC, Board, FDIC, and

OTS (the Agencies) are publishing for

industry comment two documents that

set forth draft supervisory guidance for

implementing proposed revisions to the

risk-based capital standards in the

United States. These proposed

revisions, which would implement the

New Basel Capital Accord in the United

States, are published as an advance

notice of proposed rulemaking (ANPR)

elsewhere in today’s Federal Register.

Under the advanced approaches for

credit and operational risk described in

the ANPR, banking organizations would

use internal estimates of certain risk

components as key inputs in the

determination of their regulatory capital

requirements. The Agencies believe that

supervisory guidance is necessary to

balance the flexibility inherent in the

advanced approaches with high

standards that promote safety and

soundness and encourage comparability

across institutions.

The first document sets forth Draft

Supervisory Guidance on Internal

Ratings-Based Systems for Corporate

Credit (corporate IRB guidance). This

document describes supervisory

expectations for institutions that intend

to adopt the advanced internal ratings-

based approach (A–IRB) for credit risk

as set forth in today’s ANPR

ety and

soundness and encourage comparability

across institutions.

The first document sets forth Draft

Supervisory Guidance on Internal

Ratings-Based Systems for Corporate

Credit (corporate IRB guidance). This

document describes supervisory

expectations for institutions that intend

to adopt the advanced internal ratings-

based approach (A–IRB) for credit risk

as set forth in today’s ANPR. The

corporate IRB guidance is intended to

provide supervisors and institutions

with a clear description of the essential

components and characteristics of an

acceptable A–IRB framework. The

guidance focuses specifically on

corporate credit portfolios; further

guidance is expected at a later date on

other credit portfolios (including, for

example, retail and commercial real

estate portfolios).

The second document sets forth Draft

Supervisory Guidance on Operational

Risk Advanced Measurement

Approaches for Operational Risk (AMA

guidance). This document outlines

supervisory expectations for institutions

that intend to adopt an advanced

measurement approach (AMA) for

operational risk as set forth in today’s

ANPR.

The Agencies are seeking comments

on the supervisory standards set forth in

both documents. In addition to seeking

comment on specific aspects of the

supervisory guidance set forth in the

documents, the Agencies are seeking

comment on the extent to which the

supervisory guidance strikes the

appropriate balance between flexibility

and specificity. Likewise, the Agencies

are seeking comment on whether an

appropriate balance has been struck

between the regulatory requirements set

forth in the ANPR and the supervisory

standards set forth in these documents.

DATES: Comments must be received no

later than November 3, 2003.

ADDRESSES: Comments should be

directed to:

OCC: Please direct your comments to:

Office of the Comptroller of the

Currency, 250 E Street, SW., Public

Information Room, Mailstop 1–5,

Washington, DC 20219, Attention:

Docket No

latory requirements set

forth in the ANPR and the supervisory

standards set forth in these documents.

DATES: Comments must be received no

later than November 3, 2003.

ADDRESSES: Comments should be

directed to:

OCC: Please direct your comments to:

Office of the Comptroller of the

Currency, 250 E Street, SW., Public

Information Room, Mailstop 1–5,

Washington, DC 20219, Attention:

Docket No. 03–15; fax number (202)

874–4448; or Internet address:

regs.comments@occ.treas.gov. Due to

delays in paper mail delivery in the

Washington area, we encourage the

submission of comments by fax or e-

mail whenever possible. Comments may

be inspected and photocopied at the

OCC’s Public Information Room, 250 E

Street, SW., Washington, DC. You may

make an appointment to inspect

comments by calling (202) 874–5043.

Board: Comments should refer to

Docket No. OP–1153 and may be mailed

to Ms. Jennifer J. Johnson, Secretary,

Board of Governors of the Federal

Reserve System, 20th Street and

Constitution Avenue, NW., Washington,

DC, 20551. However, because paper

mail in the Washington area and at the

Board of Governors is subject to delay,

please consider submitting your

comments by e-mail to

regs.comments@federalreserve.gov, or

faxing them to the Office of the

Secretary at 202/452–3819 or 202/452–

3102. Members of the public may

inspect comments in Room MP–500 of

the Martin Building between 9 a.m. and

5 p.m. on weekdays pursuant to

§ 261.12, except as provided in § 261.14,

of the Board’s Rules Regarding

Availability of Information, 12 CFR

261.12 and 261.14.

FDIC: Written comments should be

addressed to Robert E. Feldman,

Executive Secretary, Attention:

Comments, Federal Deposit Insurance

Corporation, 550 17th Street, NW.,

Washington, DC, 20429. Commenters

are encouraged to submit comments by

facsimile transmission to (202) 898–

3838 or by electronic mail to Comments

@FDIC.gov

Regarding

Availability of Information, 12 CFR

261.12 and 261.14.

FDIC: Written comments should be

addressed to Robert E. Feldman,

Executive Secretary, Attention:

Comments, Federal Deposit Insurance

Corporation, 550 17th Street, NW.,

Washington, DC, 20429. Commenters

are encouraged to submit comments by

facsimile transmission to (202) 898–

3838 or by electronic mail to Comments

@FDIC.gov. Comments also may be

hand-delivered to the guard station at

the rear of the 550 17th Street Building

(located on F Street) on business days

between 8:30 a.m. and 5 p.m. Comments

may be inspected and photocopied at

the FDIC’s Public Information Center,

Room 100, 801 17th Street, NW.,

Washington, DC between 9 a.m. and

4:30 p.m. on business days.

OTS: Send comments to Regulation

Comments, Chief Counsel’s Office,

Office of Thrift Supervision, 1700 G

Street, NW., Washington, DC 20552,

Attention: No. 2003–28. Delivery: Hand

deliver comments to the 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. 2003–

28. Facsimiles: Send facsimile

transmissions to FAX Number (202)

906–6518, Attention: No 2003–28. e-

mail: Send e-mails to

regs.comments@ots.treas.gov, Attention:

No. 2003–28, and include your name

and telephone number. Due to

temporary disruptions in mail service in

the Washington, DC area, commenters

are encouraged to send comments by fax

or e-mail, if possible.

FOR FURTHER INFORMATION CONTACT:

OCC: Corporate IRB guidance: Jim

Vesely, National Bank Examiner, Large

Bank Supervision (202/874–5170 or

james.vesely@occ.treas.gov); AMA

guidance: Tanya Smith, Senior

International Advisor, International

Banking & Finance (202/874–4735 or

tanya.smith@occ.treas.gov).

Board: Corporate IRB guidance: David

Palmer, Supervisory Financial Analyst,

Division of Banking Supervision and

Regulation (202/452–2904 or

david.e.palmer@frb.gov); AMA

guidance: T

ner, Large

Bank Supervision (202/874–5170 or

james.vesely@occ.treas.gov); AMA

guidance: Tanya Smith, Senior

International Advisor, International

Banking & Finance (202/874–4735 or

tanya.smith@occ.treas.gov).

Board: Corporate IRB guidance: David

Palmer, Supervisory Financial Analyst,

Division of Banking Supervision and

Regulation (202/452–2904 or

david.e.palmer@frb.gov); AMA

guidance: T. Kirk Odegard, Supervisory

Financial Analyst, Division of Banking

Supervision and Regulation (202/530–

6225 or thomas.k.odegard@frb.gov). For

users of Telecommunications Device for

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Federal Register / Vol. 68, No. 149 / Monday, August 4, 2003 / Notices

the Deaf (‘‘TDD’’) only, contact 202/

263–4869.

FDIC: Corporate IRB guidance and

AMA guidance: Pete D. Hirsch, Basel

Project Manager, Division of

Supervision and Consumer Protection

(202/898–6751 or phirsch@fdic.gov).

OTS: Corporate IRB guidance and

AMA guidance: Michael D. Solomon,

Senior Program Manager for Capital

Policy (202/906–5654); David W. Riley,

Project Manager (202/906–6669),

Supervision Policy; Teresa A. Scott,

Counsel (Banking and Finance) (202/

906–6478); or Eric Hirschhorn, Principal

Financial Economist (202/906–7350),

Regulations and Legislation Division,

Office of the Chief Counsel, Office of

Thrift Supervision, 1700 G Street, NW.,

Washington, DC 20552.

Document 1: Draft Supervisory

Guidance on Internal Ratings-Based

Systems for Corporate Credit

Table of Contents

I. Introduction

A. Purpose

B. Overview of Supervisory Expectations

1. Ratings Assignment

2. Quantification

3. Data Maintenance

4. Control and Oversight Mechanisms

C. Scope of Guidance

D. Timing

II. Ratings for IRB Systems

A. Overview

B. Credit Ratings

1. Rating Assignment Techniques

a. Expert Judgment

b. Models

c. Constrained Judgment

C. IRB Ratings System Architecture

1. Two-Dimensional Rating System

a. Definition of Default

b

isory Expectations

1. Ratings Assignment

2. Quantification

3. Data Maintenance

4. Control and Oversight Mechanisms

C. Scope of Guidance

D. Timing

II. Ratings for IRB Systems

A. Overview

B. Credit Ratings

1. Rating Assignment Techniques

a. Expert Judgment

b. Models

c. Constrained Judgment

C. IRB Ratings System Architecture

1. Two-Dimensional Rating System

a. Definition of Default

b. Obligor Ratings

c. Loss Severity Ratings

2. Other Considerations of IRB Rating

System Architecture

a. Timeliness of Ratings

b. Multiple Ratings Systems

c. Recognition of the Risk Mitigation

Benefits of Guarantees

3. Validation Process

a. Ratings System Developmental Evidence

b. Ratings System Ongoing Validation

c. Back Testing

III. Quantification of IRB Systems

A. Introduction

1. Stages of the Quantification Process

2. General Principles for Sound IRB

Quantification

B. Probability of Default (PD)

1. Data

2. Estimation

3. Mapping

4. Application

C. Loss Given Default (LGD)

1. Data

2. Estimation

3. Mapping

4. Application

D. Exposure at Default (EAD)

1. Data

2. Estimation

3. Mapping

4. Application

E. Maturity (M)

F. Validation

Appendix to Part III: Illustrations of the

Quantification Process

IV. Data Maintenance

A. Overview

B. Data Maintenance Framework

1. Life Cycle Tracking

2. Rating Assignment Data

3. Example Data Elements

C. Data Element Functions

1. Validation and Refinement

2. Developing Parameter Estimates

3. Applying Rating System Improvements

Historically

4. Calculating Capital Ratios and Reporting

to the Public

5. Supporting Risk Management

D. Managing data quality and integrity

1. Documentation and Definitions

2. Electronic Storage

3. Data Gaps

V. Control and Oversight Mechanisms

A. Overview

B. Independence in the Rating Approval

Process

C. Transparency

D. Accountability

1. Responsibility for Assigning Ratings

2. Responsibility for Rating System

Performance

E. Use of Ratings

F. Rating System Review (RSR)

G. Internal Audit

1. External Audit

H

nd integrity

1. Documentation and Definitions

2. Electronic Storage

3. Data Gaps

V. Control and Oversight Mechanisms

A. Overview

B. Independence in the Rating Approval

Process

C. Transparency

D. Accountability

1. Responsibility for Assigning Ratings

2. Responsibility for Rating System

Performance

E. Use of Ratings

F. Rating System Review (RSR)

G. Internal Audit

1. External Audit

H. Corporate Oversight

I. Introduction

A. Purpose

This document describes supervisory

expectations for banking organizations

(institutions) adopting the advanced

internal ratings-based approach (IRB) for

the determination of minimum

regulatory risk-based capital

requirements. The focus of this

guidance is corporate credit portfolios.

Retail, commercial real estate,

securitizations, and other portfolios will

be the focus of later guidance. This draft

guidance should be considered with the

advance notice of proposed rulemaking

(ANPR) on revisions to the risk-based

capital standard published elsewhere in

today’s Federal Register.

The primary objective of IRB is to

enhance the sensitivity of regulatory

capital requirements to credit risk. To

accomplish that objective, IRB harnesses

a bank’s own risk rating and

quantification capabilities. In general,

the IRB approach reflects and extends

recent developments in risk

management and banking supervision.

However, the degree to which any

individual bank will need to modify its

own credit risk management practices to

deliver accurate and consistent IRB risk

parameters will vary from institution to

institution.

This guidance is intended to provide

supervisors and institutions with a clear

description of the essential components

and characteristics of an acceptable IRB

framework

upervision.

However, the degree to which any

individual bank will need to modify its

own credit risk management practices to

deliver accurate and consistent IRB risk

parameters will vary from institution to

institution.

This guidance is intended to provide

supervisors and institutions with a clear

description of the essential components

and characteristics of an acceptable IRB

framework. Toward that end, this

document sets forth IRB system

supervisory standards that are

highlighted in bold and designated by

the prefix ‘‘S.’’ Whenever possible, these

supervisory standards are principle-

based to enable institutions to

implement the framework flexibly.

However, when prudential concerns or

the need for standardization override

the desire for flexibility, the supervisory

standards are more detailed. Ultimately,

institutions must have credit risk

management practices that are

consistent with the substance and spirit

of the standards in this guidance.

The IRB conceptual framework

outlined in this document is intended

neither to dictate the precise manner by

which institutions should seek to meet

supervisory expectations, nor to provide

technical guidance on how to develop

such a framework. As institutions

develop their IRB systems in

anticipation of adopting them for

regulatory capital purposes, supervisors

will be evaluating, on an individual

bank basis, the extent to which

institutions meet the standards outlined

in this document. In evaluating

institutions, supervisors will rely on

this supervisory guidance as well as

examination procedures, which will be

developed separately. This document

assumes that readers are familiar with

the proposed IRB approach to

calculating minimum regulatory capital

articulated in the ANPR.

B. Overview of Supervisory Expectations

Rigorous credit risk measurement is a

necessary element of advanced risk

management

pervisors will rely on

this supervisory guidance as well as

examination procedures, which will be

developed separately. This document

assumes that readers are familiar with

the proposed IRB approach to

calculating minimum regulatory capital

articulated in the ANPR.

B. Overview of Supervisory Expectations

Rigorous credit risk measurement is a

necessary element of advanced risk

management. Qualifying institutions

will use their internal rating systems to

associate a probability of default (PD)

with each obligor grade, as well as a loss

given default (LGD) with each credit

facility. In addition, institutions will

estimate exposure at default (EAD) and

will calculate the effective remaining

maturity (M) of credit facilities.

Qualifying institutions will be

expected to have an IRB system

consisting of four interdependent

components:

• A system that assigns ratings and

validates their accuracy (Chapter 1),

• A quantification process that

translates risk ratings into IRB

parameters (Chapter 2),

• A data maintenance system that

supports the IRB system (Chapter 3),

and,

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• Oversight and control mechanisms

that ensure the system is functioning as

intended and producing accurate ratings

(Chapter 4).

Together these rating, quantification,

data, and oversight mechanisms present

a framework for defining and improving

the evaluation of credit risk.

It is expected that rating systems will

operate dynamically. As ratings are

assigned, quantified and used, estimates

will be compared with actual results

and data will be maintained and

updated to support oversight and

validation efforts and to better inform

future estimates

ion,

data, and oversight mechanisms present

a framework for defining and improving

the evaluation of credit risk.

It is expected that rating systems will

operate dynamically. As ratings are

assigned, quantified and used, estimates

will be compared with actual results

and data will be maintained and

updated to support oversight and

validation efforts and to better inform

future estimates. The rating system

review and internal audit functions will

serve as control mechanisms that ensure

that the process of ratings assignment

and quantification function according to

policy and design and that

noncompliance and weaknesses are

identified, communicated to senior

management and the board, and

addressed. Rating systems with

appropriate data and oversight feedback

mechanisms foster a learning

environment that promotes integrity in

the rating system and continuing

refinement.

IRB systems need the support and

oversight of the board and senior

management to ensure that the various

components fit together seamlessly and

that incentives to make the system

rigorous extend across line, risk

management, and other control groups.

Without strong board and senior

management support and involvement,

rating systems are unlikely to provide

accurate and consistent risk estimates

during both good and bad times.

The new regulatory minimum capital

requirement is predicated on an

institution’s internal systems being

sufficiently advanced to allow a full and

accurate assessment of its risk

exposures. Under the new framework,

an institution could experience a

considerable capital shortfall in the

most difficult of times if its risk

estimates are materially understated.

Consequently, the IRB framework

demands a greater level of validation

work and controls than supervisors have

required in the past

ng

sufficiently advanced to allow a full and

accurate assessment of its risk

exposures. Under the new framework,

an institution could experience a

considerable capital shortfall in the

most difficult of times if its risk

estimates are materially understated.

Consequently, the IRB framework

demands a greater level of validation

work and controls than supervisors have

required in the past. When properly

implemented, the new framework holds

the potential for better aligning

minimum capital requirements with the

risk taken, pushing capital requirements

higher for institutions that specialize in

riskier types of lending, and lower for

those that specialize in safer risk

exposures.

Supervisors will evaluate compliance

with the supervisory standards for each

of the four components of an IRB

system. However, evaluating

compliance with each of the standards

individually will not be sufficient to

determine an institution’s overall

compliance. Rather, supervisors and

institutions must also evaluate how well

the various components of an

institution’s IRB system complement

and reinforce one another to achieve the

overall objective of accurate measures of

risk. In performing their evaluation,

supervisors will need to exercise

considerable supervisory judgment,

both in evaluating the individual

components and the overall IRB

framework. A summary of the key

supervisory expectations for each of the

IRB components follows.

Ratings Assignment

The first component of an IRB system

involves the assignment and validation

of ratings (see Chapter 1). Ratings must

be accurately and consistently applied

to all corporate credit exposures and be

subject to initial and ongoing validation

ual

components and the overall IRB

framework. A summary of the key

supervisory expectations for each of the

IRB components follows.

Ratings Assignment

The first component of an IRB system

involves the assignment and validation

of ratings (see Chapter 1). Ratings must

be accurately and consistently applied

to all corporate credit exposures and be

subject to initial and ongoing validation.

Institutions will have latitude in

designing and operating IRB rating

systems subject to five broad standards:

Two-dimensional risk-rating system—

IRB institutions must be able to make

meaningful and consistent

differentiations among credit exposures

along two dimensions—obligor default

risk and loss severity in the event of a

default.

Rank order risks—IRB institutions

must rank obligors by their likelihood of

default, and facilities by the loss

severity expected in default.

Calibration—IRB obligor ratings must

be calibrated to values of the probability

of default (PD) parameter and loss

severity ratings must be calibrated to

values of the loss given default (LGD)

parameter.

Accuracy—Actual long-run actual

default frequencies for obligor rating

grades must closely approximate the

PDs assigned to those grades and

realized loss rates on loss severity

grades must closely approximate the

LGDs assigned to those grades.

Validation process—IRB institutions

must have ongoing validation processes

for rating systems that include the

evaluation of developmental evidence,

process verification, benchmarking, and

the comparison of predicted parameter

values to actual outcomes (back-testing).

Quantification

The second component of an IRB

system is a quantification process (see

Chapter 2). Since obligor and facility

ratings may be assigned separately from

the quantification of the associated PD

and LGD parameters, quantification is

addressed as a separate process

ess verification, benchmarking, and

the comparison of predicted parameter

values to actual outcomes (back-testing).

Quantification

The second component of an IRB

system is a quantification process (see

Chapter 2). Since obligor and facility

ratings may be assigned separately from

the quantification of the associated PD

and LGD parameters, quantification is

addressed as a separate process. The

quantification process must produce

values not only for PD and LGD but also

for EAD and for the effective remaining

maturity (M). The quantification of

those four parameters is expected to be

the result of a disciplined process. The

key considerations for effective

quantification are as follows:

Process—IRB institutions must have a

fully specified process covering all

aspects of quantification (reference data,

estimation, mapping, and application).

Documentation—The quantification

process, including the role and scope of

expert judgment, must be fully

documented and updated periodically.

Updating—Parameter estimates and

related documentation must be updated

regularly.

Review—A bank must subject all

aspects of the quantification process,

including design and implementation,

to an appropriate degree of independent

review and validation.

Constraints on Judgment—Judgmental

adjustments may be an appropriate part

of the quantification process, but must

not be biased toward lower risk

estimates.

Conservatism—Parameter estimates

must incorporate a degree of

conservatism that is appropriate for the

overall robustness of the quantification

process.

Data Maintenance

The third component of an IRB

system is an advanced data management

system that produces credible and

reliable risk estimates (see Chapter 3).

The broad standard governing an IRB

data maintenance system is that it

supports the requirements for the other

IRB system components, as well as the

institution’s broader risk management

and reporting needs

antification

process.

Data Maintenance

The third component of an IRB

system is an advanced data management

system that produces credible and

reliable risk estimates (see Chapter 3).

The broad standard governing an IRB

data maintenance system is that it

supports the requirements for the other

IRB system components, as well as the

institution’s broader risk management

and reporting needs. Institutions will

have latitude in managing their data,

subject to the following key data

maintenance standards:

Life Cycle Tracking—Institutions

must collect, maintain, and analyze

essential data for obligors and facilities

throughout the life and disposition of

the credit exposure.

Rating Assignment Data—Institutions

must capture all significant quantitative

and qualitative factors used to assign the

obligor and loss severity rating.

Support of IRB System—Data

collected by institutions must be of

sufficient depth, scope, and reliability

to:

• Validate IRB system processes,

• Validate parameters,

• Refine the IRB system,

• Develop internal parameter

estimates,

• Apply improvements historically,

• Calculate capital ratios,

• Produce internal and public reports,

and

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1 Facilities—loans, lines, or other separate

extensions of credit to an obligor.

• Support risk management.

Control and Oversight Mechanisms

The fourth component of an IRB

system is comprised of control and

oversight mechanisms that ensure that

the various components of the IRB

system are functioning as intended (see

Chapter 4). Given the various uses of

internal risk ratings, including their

direct link to regulatory capital

requirements, there is enormous,

sometimes conflicting, pressure on

banks’ internal rating systems

s

The fourth component of an IRB

system is comprised of control and

oversight mechanisms that ensure that

the various components of the IRB

system are functioning as intended (see

Chapter 4). Given the various uses of

internal risk ratings, including their

direct link to regulatory capital

requirements, there is enormous,

sometimes conflicting, pressure on

banks’ internal rating systems. Control

structures are subject to the following

broad standards:

Interdependent System of Controls—

IRB institutions must implement a

system of interdependent controls that

include the following elements:

• Independence,

• Transparency,

• Accountability,

• Use of ratings,

• Rating system review,

• Internal audit, and

• Board and senior management

oversight.

Checks and Balances—Institutions

must combine the various control

mechanisms in a way that provides

checks and balances for ensuring IRB

system integrity.

The system of oversight and controls

required for an effective IRB system may

operate in various ways within

individual institutions. This guidance

does not prescribe any particular

organizational structure for IRB

oversight and control mechanisms.

Banks have broad latitude to implement

structures that are most effective for

their individual circumstances, as long

as those structures support and enhance

the institution’s ability to satisfy the

supervisory standards expressed in this

document.

C. Scope of Guidance

This draft guidance reflects work

performed by supervisors to evaluate

and compare current practices at

institutions with the concepts and

requirements for an IRB framework. For

instances in which a range of practice

was observable, examples are provided

on how certain practices may or may

not qualify. However, in many other

instances, practices were at such an

early stage of development that it was

not feasible to describe specific

examples. In those cases, requirements

tend to be principle-based and without

examples

and

requirements for an IRB framework. For

instances in which a range of practice

was observable, examples are provided

on how certain practices may or may

not qualify. However, in many other

instances, practices were at such an

early stage of development that it was

not feasible to describe specific

examples. In those cases, requirements

tend to be principle-based and without

examples. Given that institutions are

still in the early stages of developing

qualifying IRB systems, it is expected

that this guidance will evolve over time

to more explicitly take into account new

and improving practices.

D. Timing

S. An IRB system must be operating

fully at least one year prior to the

institution’s intended start date for the

advanced approach.

As noted in the ANPR, the significant

challenge of implementing a fully

complying IRB system requires that

institutions and supervisors have

sufficient time to observe whether the

IRB system is delivering risk-based

capital figures with a high level of

integrity. The ability to observe the

institution’s ratings architecture,

validation, data maintenance and

control functions in a fully operating

environment prior to implementation

will help identify how well the IRB

system design functions in practice.

This will be particularly important

given that in the first year of

implementation institutions will not

only be subject to the new minimum

capital requirements, but will also be

disclosing risk-based capital ratios for

the public to rely upon in the

assessment of the institution’s financial

health.

II. Ratings for IRB Systems

A. Overview

This chapter describes the design and

operation of risk-rating systems that will

be acceptable in an internal ratings-

based (IRB) framework

ons will not

only be subject to the new minimum

capital requirements, but will also be

disclosing risk-based capital ratios for

the public to rely upon in the

assessment of the institution’s financial

health.

II. Ratings for IRB Systems

A. Overview

This chapter describes the design and

operation of risk-rating systems that will

be acceptable in an internal ratings-

based (IRB) framework. Banks will have

latitude in designing and operating IRB

rating systems, subject to five broad

standards:

Two-dimensional risk-rating system—

IRB institutions must be able to make

meaningful and consistent

differentiations among credit exposures

along two dimensions—obligor default

risk and loss severity in the event of a

default.

Rank order risks—IRB institutions

must rank obligors by their likelihood of

default, and facilities by the loss

severity expected in default.

Calibration—IRB obligor ratings must

be calibrated to values of the probability

of default (PD) parameter and loss

severity ratings must be calibrated to

values of the loss given default (LGD)

parameter.

Accuracy—Actual long-run actual

default frequencies for obligor rating

grades must closely approximate the

PDs assigned to those grades and actual

loss rates on loss severity grades must

closely approximate the LGDs assigned

to those grades.

Validation process—IRB institutions

must have ongoing validation processes

for rating systems that include the

evaluation of developmental evidence,

process verification, benchmarking, and

the comparison of predicted parameter

values to actual outcomes (back-testing).

B. Credit Ratings

In general, a credit rating is a

summary indicator of the relative risk

on a credit exposure. Credit ratings can

take many forms. The most widely

known credit ratings are the public

agency ratings, which are expressed as

letters; bank internal ratings tend to be

expressed as whole numbers—for

example, 1 through 10

cted parameter

values to actual outcomes (back-testing).

B. Credit Ratings

In general, a credit rating is a

summary indicator of the relative risk

on a credit exposure. Credit ratings can

take many forms. The most widely

known credit ratings are the public

agency ratings, which are expressed as

letters; bank internal ratings tend to be

expressed as whole numbers—for

example, 1 through 10. Some rating

model outputs are expressed in terms of

probability of default or expected

default frequency, in which case they

may be more than relative measures of

risk. Regardless of the form, meaningful

credit ratings share two characteristics:

• They group credits to discriminate

among possible outcomes.

• They rank the perceived levels of

credit risk.

Banks have used credit ratings of

various types for a variety of purposes.

Some ratings are intended to rank

obligors by risk of default and some are

intended to rank facilities1 by expected

loss, which incorporates risk of default

and loss severity. Bank rating systems

that are geared solely to expected loss

will need to be amended to meet the

two-dimensional requirements of the

IRB approach.

Rating Assignment Techniques

Banks use different techniques, such

as expert judgment and models, to

assign credit risk ratings. For banks

using the IRB approach, how ratings are

assigned is important because different

techniques will require different

validation processes and control

mechanisms to ensure the integrity of

the rating system. To assist the

discussion of rating architecture

requirements, described below are some

of the current rating assignment

techniques. Any of these techniques—

expert judgment, models, constrained

judgment, or a combination thereof—

could be acceptable within an IRB

system, provided the bank meets the

standards outlined in this document.

Expert Judgment

Historically, banks have used expert

judgment to assign ratings to

commercial credits

requirements, described below are some

of the current rating assignment

techniques. Any of these techniques—

expert judgment, models, constrained

judgment, or a combination thereof—

could be acceptable within an IRB

system, provided the bank meets the

standards outlined in this document.

Expert Judgment

Historically, banks have used expert

judgment to assign ratings to

commercial credits. With this

technique, an individual weighs

relevant information and reaches a

conclusion about the appropriate risk

rating. Presumably, the rater makes

informed judgments based on

knowledge gained through experience

and training.

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2 Some banks have developed credit rating

models that they refer to as ‘‘scorecards,’’ but they

have used expert judgment to derive the weights.

While they are models, they are not scoring models

in the now conventional use of the term. In its

conventional use, the term scoring model is

reserved for a rating model derived using statistical

techniques.

The key feature of expert-judgment

systems is flexibility. The prevalence of

judgmental rating systems reflects the

view that the determinants of default are

too complicated to be captured by a

single quantitative model. The quality of

management is often cited as an

example of a risk determinant that is

difficult to assess through a quantitative

model. In order to foster internal

consistency, banks employing expert

judgment rating systems typically

provide narrative guidelines that set out

ratings criteria. However, the expert

must decide how narrative guidelines

apply to a given set of circumstances.

The flexibility possible in the

assignment of judgmental ratings has

implications for the types of ratings

review that are feasible

del. In order to foster internal

consistency, banks employing expert

judgment rating systems typically

provide narrative guidelines that set out

ratings criteria. However, the expert

must decide how narrative guidelines

apply to a given set of circumstances.

The flexibility possible in the

assignment of judgmental ratings has

implications for the types of ratings

review that are feasible. As part of the

ratings validation process, banks will

attempt to confirm that raters follow

bank policy. However, two individuals

exercising judgment can use the same

information to support different ratings.

Thus, the review of an expert judgment

rating system will require an expert who

can identify the impact of policy and

the impact of judgment on a rating.

Models

In recent years, models have been

developed for use in rating commercial

credits. In a model-based approach,

inputs are numeric and provide

quantitative and qualitative information

about an obligor. The inputs are

combined using mathematical equations

to produce a number that is translated

into a categorical rating. An important

feature of models is that the rating is

perfectly replicable by another party,

given the same inputs.

The models used in credit rating can

be distinguished by the techniques used

to develop them. Some models may rely

on statistical techniques while others

rely on expert-judgment techniques.

Statistical models. Statistically

developed models are the result of

statistical optimization, in which well-

defined mathematical criteria are used

to choose the model that has the closest

fit to the observed data. Numerous

techniques can be used to build

statistical models; regression is one

widely recognized example

tatistical techniques while others

rely on expert-judgment techniques.

Statistical models. Statistically

developed models are the result of

statistical optimization, in which well-

defined mathematical criteria are used

to choose the model that has the closest

fit to the observed data. Numerous

techniques can be used to build

statistical models; regression is one

widely recognized example. Regardless

of the specific statistical technique, a

knowledgeable independent reviewer

will have to exercise judgment in

evaluating the reasonableness of a

model’s development, including its

underlying logic, the techniques used to

handle the data, and the statistical

model building techniques.

Expert-derived models.2 Several

banks have built rating models by

asking their experts to decide what

weights to assign to critical variables in

the models. Drawing on their

experience, the experts first identify the

observable variables that affect the

likelihood of default. They then reach

agreement on the weights to be assigned

to each of the variables. Unlike

statistical optimization, the experts are

not necessarily using clear, consistent

criteria to select the weights attached to

the variables. Indeed, expert-judgment

model building is often a practical

choice when there is not enough data to

support a statistical model building.

Despite its dependence on expert

judgment, this method can be called

model-based as long as the result—the

equation, most likely with linear

weights—is used as the basis to rate the

credits. Once the equation is set, the

model shares the feature of replicability

with statistically derived models.

Generally, independent credit experts

use judgment to evaluate the

reasonableness of the development of

these models.

Constrained Judgment

The alternatives just described

present the extremes, but in practice,

many banks use rating systems that

combine models with judgment. Two

approaches are common

is set, the

model shares the feature of replicability

with statistically derived models.

Generally, independent credit experts

use judgment to evaluate the

reasonableness of the development of

these models.

Constrained Judgment

The alternatives just described

present the extremes, but in practice,

many banks use rating systems that

combine models with judgment. Two

approaches are common.

Judgmental systems with quantitative

guidelines or model results as inputs.

Historically, the most common

approach to rating has involved

individuals exercising judgment about

risks, subject to policy guidelines

containing quantitative criteria such as

minimum values for particular financial

ratios. Banks develop quantitative

criteria to guide individuals in assigning

ratings, but often believe that those

criteria do not adequately reflect the

information needed to assign a rating.

One version of this constrained

judgment approach features a model

output as one among several criteria that

an individual may consider in assigning

ratings. The individual assigning the

rating is responsible for prioritizing the

criteria, reconciling conflicts between

criteria, and if warranted, overriding

some criteria. Even if individuals

incorporate model results as one of the

factors in their ratings, they will

exercise judgment in deciding what

weight to attach to the model result. The

appeal of this approach is that the

model combines many pieces of

information into a single output, which

simplifies analysis, while the rater

retains flexibility regarding the use of

the model output.

Model-based ratings with judgmental

overrides. When banks use rating

models, individuals are generally

permitted to override the results under

certain conditions and within tolerance

levels for frequency. Credit-rating

systems in which individuals can

override models raise many of the same

issues presented separately by pure

judgment and model-based systems

the use of

the model output.

Model-based ratings with judgmental

overrides. When banks use rating

models, individuals are generally

permitted to override the results under

certain conditions and within tolerance

levels for frequency. Credit-rating

systems in which individuals can

override models raise many of the same

issues presented separately by pure

judgment and model-based systems. If

overrides are rare, the system can be

evaluated largely as if it is a model-

based system. If, however, overrides are

prevalent, the system will be evaluated

more like a judgmental system.

Since constrained judgment systems

combine features of both expert

judgment and model-based systems,

their evaluation will require the skills

required to evaluate both of these other

systems.

C. IRB Ratings System Architecture

Two-Dimensional Rating System

S. IRB risk rating systems must have

two rating dimensions—obligor and loss

severity ratings.

S. IRB obligor and loss severity ratings

must be calibrated to values of the

probability of default (PD) and the loss

given default (LGD), respectively.

Regardless of the type of rating

system(s) used by an institution, the IRB

approach imposes some specific

requirements. The first requirement is

that an IRB rating system must be two-

dimensional. Banks will assign obligor

ratings, which will be associated with a

PD. They will also either assign a loss

severity rating, which will be associated

with LGD values, or directly assign LGD

values to each facility. The process of

assigning the obligor and loss severity

ratings—hereafter referred to as the

rating system—is discussed below, and

the process of calibrating obligor and

loss severity ratings to PD and LGD

parameters is discussed in Chapter 2.

S. Banks must record obligor defaults

in accordance with the IRB definition of

default.

Definition of Default

The consistent identification of

defaults is fundamental to any IRB

rating system

severity

ratings—hereafter referred to as the

rating system—is discussed below, and

the process of calibrating obligor and

loss severity ratings to PD and LGD

parameters is discussed in Chapter 2.

S. Banks must record obligor defaults

in accordance with the IRB definition of

default.

Definition of Default

The consistent identification of

defaults is fundamental to any IRB

rating system. For IRB purposes, a

default is considered to have occurred

with regard to a particular obligor when

either or both of the two following

events have taken place:

• The obligor is past due more than

90 days on any material credit

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obligation to the banking group.

Overdrafts will be considered as being

past due once the customer has

breached an advised limit or been

advised of a limit smaller than current

outstandings.

• The bank considers that the obligor

is unlikely to pay its credit obligations

to the banking group in full, without

recourse by the bank to actions such as

liquidating collateral (if held).

Any obligor (or its underlying credit

facilities) that meets one or more of the

following conditions is considered

unlikely to pay and therefore in default:

• The bank puts the credit obligation

on non-accrual status.

• The bank makes a charge-off or

account-specific provision resulting

from a significant perceived decline in

credit quality subsequent to the bank

taking on the exposure.

• The bank sells the credit obligation

at a material credit-related economic

loss.

• The bank consents to a distressed

restructuring of the credit obligation

where this is likely to result in a

diminished financial obligation caused

by the material forgiveness, or

postponement, of principal, interest or

(where relevant) fees

credit quality subsequent to the bank

taking on the exposure.

• The bank sells the credit obligation

at a material credit-related economic

loss.

• The bank consents to a distressed

restructuring of the credit obligation

where this is likely to result in a

diminished financial obligation caused

by the material forgiveness, or

postponement, of principal, interest or

(where relevant) fees.

• The bank has filed for the obligor’s

bankruptcy or a similar order in respect

of the obligor’s credit obligation to the

banking group.

• The obligor has sought or has been

placed in bankruptcy or similar

protection where this would avoid or

delay repayment of the credit obligation

to the banking group.

While most conditions of default

currently are identified by bank

reporting systems, institutions will need

to augment data capture systems to

collect those default circumstances that

may not have been traditionally

identified. These include facilities that

are current and still accruing but where

the obligor declared or was placed in

bankruptcy. They must also capture so

called ‘‘silent defaults’’—defaults when

the loss on a facility was avoided by

liquidating collateral.

Loan sales on which a bank

experiences a material loss due to credit

deterioration are considered a default.

Material credit related losses are defined

as XX. (The agencies seek comment on

how to define ‘‘material’’ loss in the

case of loans sold at a discount). Banks

should ensure that they have adequate

systems to identify such transactions

and to maintain adequate records so that

reviewers can assess the adequacy of the

institution’s decision-making process in

this area.

Obligor Ratings

S. Banks must assign discrete obligor

grades.

While banks may use models to

estimate probabilities of default for

individual obligors, the IRB approach

requires banks to group the obligors into

discrete grades. Each obligor grade, in

turn, must be associated with a single

PD.

S

reviewers can assess the adequacy of the

institution’s decision-making process in

this area.

Obligor Ratings

S. Banks must assign discrete obligor

grades.

While banks may use models to

estimate probabilities of default for

individual obligors, the IRB approach

requires banks to group the obligors into

discrete grades. Each obligor grade, in

turn, must be associated with a single

PD.

S. The obligor-rating system must

result in a ranking of obligors by

likelihood of default.

The proper operation of the obligor-

rating system will feature a ranking of

obligors by likelihood of default. For

example, if a bank uses a rating system

based on a 10-point scale, with 1

representing obligors of highest

financial strength and 10 representing

defaulted obligors, grades 2 through 9

should represent groups of ever-

increasing risk. In a rating system in

which risk increases with the grade, an

obligor with a grade 4 is riskier than an

obligor with a grade 2, but need not be

twice as risky.

S. Separate exposures to the same

obligor must be assigned to the same

obligor rating grade.

As noted above, the IRB framework

requires that the obligor rating be

distinct from the loss severity rating,

which is assigned to the facility.

Collateral and other facility

characteristics should not influence the

obligor rating. For example, in a 1-to-10

rating system, where risk increases with

the number grade, a defaulted borrower

with a fully cash-secured transaction

should be rated a 10—defaulted—

regardless of the remote expectation of

loss. Likewise, a borrower whose

financial condition warrants the highest

investment grade rating should be rated

a 1 even if the bank’s transactions are

subordinate to other creditors and

unsecured. Since the rating is assigned

to the obligor and not the facility,

separate exposures to the same obligor

must be assigned to the same obligor

rating grade.

At the bottom of any IRB system

rating scale is a default grade

se

financial condition warrants the highest

investment grade rating should be rated

a 1 even if the bank’s transactions are

subordinate to other creditors and

unsecured. Since the rating is assigned

to the obligor and not the facility,

separate exposures to the same obligor

must be assigned to the same obligor

rating grade.

At the bottom of any IRB system

rating scale is a default grade. Once an

obligor is considered to be in default for

IRB purposes, that obligor must be

assigned a default grade until such time

as its financial condition and

performance improve sufficiently to

clearly meet the bank’s internal rating

definition for one of its non-default

grades. Once an obligor is in default on

any material credit obligation to the

subject bank, all of its facilities at that

institution are considered to be in

default.

S. In assigning an obligor to a rating

category, the bank must assess the risk

of obligor default over a period of at

least one year.

S. Obligor ratings must reflect the

impact of financial distress.

In assigning an obligor to a rating

category, the bank must assess the risk

of obligor default over a period of at

least one year. This use of a one-year

assessment horizon does not mean that

a bank should limit its consideration to

outcomes for that obligor that are most

likely over that year; the rating must

take into account possible adverse

events that might increase an obligor’s

likelihood of default.

Rating Philosophy—Decisions

Underlying Ratings Architecture

S. Banks must adopt a ratings

philosophy. Policy guidelines should

describe the ratings philosophy,

particularly how quickly ratings are

expected to migrate in response to

economic cycles.

S. A bank’s capital management

policy must be consistent with its

ratings philosophy in order to avoid

capital shortfalls in times of systematic

economic stress.

In the IRB framework, banks assign

obligors to groups that are expected to

share common default frequencies

scribe the ratings philosophy,

particularly how quickly ratings are

expected to migrate in response to

economic cycles.

S. A bank’s capital management

policy must be consistent with its

ratings philosophy in order to avoid

capital shortfalls in times of systematic

economic stress.

In the IRB framework, banks assign

obligors to groups that are expected to

share common default frequencies. That

general description, however, still

leaves open different possible

implementations, depending on how the

bank defines the set of possible adverse

events that the obligor might face. A

bank must decide whether obligors are

grouped by expected common default

frequency over the next year (a so-called

point-in-time rating system) or by an

expected common default frequency

over a wider range of possible stress

outcomes (a so-called through-the-cycle

rating system). Choosing between a

point-in-time system and a through-the-

cycle system yields a rating philosophy.

In point in time rating systems,

obligors are assigned to groups that are

expected to share a common default

frequency in a particular year. Point-in-

time ratings change from year to year as

borrowers’ circumstances change,

including changes due to the economic

possibilities faced by the borrowers.

Since the economic circumstances of

many borrowers reflect the common

impact of the general economic

environment, the transitions in point-in-

time ratings will reflect that systematic

influence. A Merton-style probability of

default prediction model is commonly

believed to be an example of a point-in-

time approach to rating (although that

may depend on the specific

implementation of the model).

Through-the-cycle rating systems do

not ask the question, what is the

probability of default over the next year.

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rediction model is commonly

believed to be an example of a point-in-

time approach to rating (although that

may depend on the specific

implementation of the model).

Through-the-cycle rating systems do

not ask the question, what is the

probability of default over the next year.

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Instead, they assign obligors to groups

that would be expected to share a

common default frequency if the

borrowers in them were to experience

distress, regardless of whether that

distress is in the next year. Thus, as the

descriptive title suggests, this rating

philosophy abstracts from the near-term

economic possibilities and considers a

richer assessment of the possibilities.

Like point-in-time ratings, through the

cycle ratings will change from year to

year due to changes in borrower

circumstance. However, since this rating

philosophy abstracts from the

immediate economic circumstance and

considers the implications of

hypothetical stress circumstances, year

to year transitions in ratings will be less

influenced by changes in the actual

economic environment. The ratings

agencies are commonly believed to use

through-the-cycle rating approaches.

Current practice in many banks in the

U.S. is to rate obligors using an

approach that combines aspects of both

point-in-time and through the cycle

approaches. The explanation provided

by banks that combine those approaches

is that they want rating transitions to

reflect the directional impact of changes

in the economic environment, but that

they do not want all of the volatility in

ratings associated with a point-in-time

approach.

Regardless of which ratings

philosophy a bank chooses, an IRB bank

must articulate clearly its approach and

the implications of that choice

by banks that combine those approaches

is that they want rating transitions to

reflect the directional impact of changes

in the economic environment, but that

they do not want all of the volatility in

ratings associated with a point-in-time

approach.

Regardless of which ratings

philosophy a bank chooses, an IRB bank

must articulate clearly its approach and

the implications of that choice. As part

of the choice of rating philosophy, the

bank must decide whether the same

ratings philosophy will be employed for

all of the bank’s portfolios. And

management must articulate the

implications that the bank’s ratings

philosophy has on the bank’s capital

planning process. If a bank chooses a

ratings philosophy that is likely to result

in ratings transitions that reflect the

impact of the economic cycle, its capital

management policy must be designed to

avoid capital shortfalls in times of

systematic economic stress.

Obligor-Rating Granularity

S. An institution must have at least

seven obligor grades that contain only

non-defaulted borrowers and at least

one grade to which only defaulted

borrowers are assigned.

The number of grades used in a rating

system should be sufficient to

reasonably ensure that management can

meaningfully differentiate risk in the

portfolio, without being so large that it

limits the practical use of the rating

system. To determine the appropriate

number of grades beyond the minimum

seven non-default grades, each

institution must perform its own

internal analysis.

S. An institution must justify the

number of obligor grades used in its

rating system and the distribution of

obligors across those grades.

The mere existence of an exposure

concentration in a grade (or grades) does

not, by itself, reflect weakness in a

rating system. For example, banks may

focus on a particular type of lending,

such as asset-based lending, in which

the borrowers may have similar default

risk

justify the

number of obligor grades used in its

rating system and the distribution of

obligors across those grades.

The mere existence of an exposure

concentration in a grade (or grades) does

not, by itself, reflect weakness in a

rating system. For example, banks may

focus on a particular type of lending,

such as asset-based lending, in which

the borrowers may have similar default

risk. Banks with such focused lending

activities may use close to the minimum

number of obligor grades, while banks

with a broad range of lending activities

should have more grades. However,

banks with a high concentration of

obligors in a particular grade are

expected to perform a thorough analysis

that supports such a concentration.

A significant concentration within an

obligor grade may be suspected if the

financial strength of the borrowers

within that grade varies considerably. If

obligors seem unduly concentrated,

then management should ask

themselves the following questions:

• Are the criteria for each grade clear?

Those rating criteria may be too vague

to allow raters to make clear

distinctions. Ambiguity may be an issue

throughout the rating scale or it may be

limited to the most commonly used

ratings.

• How diverse are the obligors? That

is how many market segments (for

example, large commercial, middle

market, private banking, small business,

geography, etc.) are significantly

represented in the bank’s borrower

population? If a bank’s commercial loan

portfolio is not concentrated in one

market segment, its risk rating

distribution is not likely to be

concentrated.

• How broad are the bank’s internal

rating categories compared to those of

other lenders? The bank may be able to

learn enough from publicly available

information to adjust its rating criteria.

Some banks use ‘‘modifiers’’ to

provide more risk differentiation to a

given rating system

s not concentrated in one

market segment, its risk rating

distribution is not likely to be

concentrated.

• How broad are the bank’s internal

rating categories compared to those of

other lenders? The bank may be able to

learn enough from publicly available

information to adjust its rating criteria.

Some banks use ‘‘modifiers’’ to

provide more risk differentiation to a

given rating system. A risk rating

modified with a plus, minus or other

indicator does not constitute a separate

grade unless the bank has developed a

distinct rating definition and criteria for

the modified grade. In the absence of

such distinctions, grades such as 5, 5+,

and 5¥ are viewed as a single grade for

regulatory capital purposes regardless of

the existence of the modifiers.

Loss Severity Ratings

S. Banks must rank facilities by the

expected severity of the loss upon

default.

The second dimension of an IRB

system is the loss severity rating, which

is calibrated to LGD. A facility’s LGD

estimate is the loss the bank is likely to

incur in the event that the obligor

defaults, and is expressed as a

percentage of exposure at the time of

default. LGD estimates can be assigned

either through the use of a loss severity

rating system or they can be directly

assigned to each facility.

LGD analysis is still in very early

stages of development relative to default

risk modeling. Academic research in

this area is relatively sparse, data are not

abundant, and industry practice is still

widely varying and evolving. Given the

lack of data and the lack of research into

LGD modeling, some banks are likely, as

a first step, to segment their portfolios

by a handful of available characteristics

and determine the appropriate LGDs for

those segments. Over time, banks’ LGD

methodologies are expected to evolve.

Long-standing banking experience and

existing research on LGD, while

preliminary, suggests that collateral

values, seniority, industry, etc. are

predictive of loss severity.

S

are likely, as

a first step, to segment their portfolios

by a handful of available characteristics

and determine the appropriate LGDs for

those segments. Over time, banks’ LGD

methodologies are expected to evolve.

Long-standing banking experience and

existing research on LGD, while

preliminary, suggests that collateral

values, seniority, industry, etc. are

predictive of loss severity.

S. Banks must have empirical support

for LGD rating systems regardless of

whether they use an LGD grading

system or directly assign LGD estimates.

Whether a bank chooses to assign

LGD values directly or, alternatively, to

rate facilities and then quantify the LGD

for the rating grades, the key

requirement is that it will need to

identify facility characteristics that

influence LGD. Each of the loss severity

rating categories must be associated

with an empirically supported LGD

estimate. In much the same way an

obligor-rating system ranks exposures

by the probability of default, a facility

rating system must rank facilities by the

likely loss severity.

Regardless of the method used to

assign LGDs (loss severity grades or

direct LGD estimation), data used to

support the methodology must be

gathered systematically. For many

banks, the quality and quantity of data

available to support the LGD estimation

process will have an influence on the

method they choose.

Stress Condition LGDs

S. Loss severity ratings must reflect

losses expected during periods with a

relatively high number of defaults.

Like obligor ratings, which group

obligors by expected default frequency,

loss severity ratings assign facilities to

groups that are expected to experience

a common loss severity. However, the

different treatment accorded to PD and

LGD in the model used to calculate IRB

capital requirements mandates an

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group

obligors by expected default frequency,

loss severity ratings assign facilities to

groups that are expected to experience

a common loss severity. However, the

different treatment accorded to PD and

LGD in the model used to calculate IRB

capital requirements mandates an

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3 The probability that an obligor and a guarantor

(who supports the obligor’s debt) will both default

on a debt is lower than the probability that either

the obligor or the guarantor will default. This

favorable risk-mitigation effect is known as the

reduced likelihood of ‘‘double default.’’ In

determining their rating criteria and procedures,

banks are not permitted to consider possible

favorable effects of imperfect expected correlation

between default events for the borrower and

guarantor for purposes of regulatory capital

requirements. Thus, the adjusted risk weight cannot

reflect the risk mitigation of double default. The

ANPR solicits public comment on the double-

default issues.

asymmetric treatment of obligor and

loss severity ratings. Obligor ratings

assign obligors to groups that are

expected to experience common default

frequencies across a number of years,

some of which are years of general

economic stress and some of which are

not. In contrast, loss severity ratings (or

estimates) must pertain to losses

expected during periods with a high

number of defaults—particular years

that can be called stress conditions. For

cases in which loss severities do not

have a material degree of cyclical

variability, use of a long-run default

weighted average is appropriate,

although stress condition LGD generally

exceeds these averages.

Loss Severity Rating/LGD Granularity

S

tes) must pertain to losses

expected during periods with a high

number of defaults—particular years

that can be called stress conditions. For

cases in which loss severities do not

have a material degree of cyclical

variability, use of a long-run default

weighted average is appropriate,

although stress condition LGD generally

exceeds these averages.

Loss Severity Rating/LGD Granularity

S. Banks must have a sufficiently fine

loss severity grading system or

prediction model to avoid grouping

facilities with widely varying LGDs

together.

While there is no stated minimum

number of loss severity grades, the

systems that provide LGD estimates

must be flexible enough to adequately

segment facilities with significantly

varying LGDs. Banks should have a

sufficiently fine LGD grading system or

LGD prediction model to avoid grouping

facilities with widely varying LGDs

together. For example, a bank using a

loss severity rating-scale approach that

has credit products with a variety of

collateral packages or financing

structures would be expected to have

more LGD grades than those institutions

with fewer options in their credit

products.

Other Considerations of IRB Rating

System Architecture

Timeliness of Ratings

S. All risk ratings must be updated

whenever new relevant information is

received, but must be updated at least

annually.

A bank must have a policy that

requires a dynamic ratings approach

ensuring that obligor and loss severity

ratings reflect current information. That

policy must also specify minimum

financial reporting and collateral

valuation requirements. For example, at

the time of servicing events, banks

typically receive updated financial

information on obligors. For cases in

which loss severity grades or estimates

are dependent on collateral values or

other factors that change periodically,

that policy must take into account the

need to update these factors

lso specify minimum

financial reporting and collateral

valuation requirements. For example, at

the time of servicing events, banks

typically receive updated financial

information on obligors. For cases in

which loss severity grades or estimates

are dependent on collateral values or

other factors that change periodically,

that policy must take into account the

need to update these factors.

Banks’ policies may include an

alternative rating update timetable for

exposures below a de minimus amount

that is justified by the lack of materiality

of the potential impact on capital. For

example, some banks use triggering

events to prompt an update of their

ratings on de minimus exposures rather

than adhering to a specific timetable.

Multiple Ratings Systems

Some banks may develop one risk-

rating system that can be used across the

entire commercial loan portfolio.

However, a bank can choose to deploy

any number of rating systems as long as

all exposures are assigned PD and LGD

values. A different rating system could

be used for each business line and each

rating system could use a different

rating scale. A bank could also use a

different rating system for each business

line with each system using a common

rating scale. Rating models could be

used for some portfolios and expert

judgment systems for others. An

institution’s complexity and

sophistication, as well as the size and

range of products offered, will affect the

types and numbers of rating systems

employed.

While using a number of rating

systems is feasible, such a practice

might make it more difficult to meet

supervisory standards. Each rating

system must conform to the standards in

this guidance and must be validated for

accuracy and consistency. The

requirement that each rating systems be

calibrated to parameter values imposes

the ultimate constraint, which is that

ratings be applied consistently.

Recognition of the Risk Mitigation

Benefits of Guarantees

S

ht make it more difficult to meet

supervisory standards. Each rating

system must conform to the standards in

this guidance and must be validated for

accuracy and consistency. The

requirement that each rating systems be

calibrated to parameter values imposes

the ultimate constraint, which is that

ratings be applied consistently.

Recognition of the Risk Mitigation

Benefits of Guarantees

S. Banks reflecting the risk-mitigating

effect of guarantees must do so by either

adjusting PDs or LGDs, but not both.

S. To recognize the risk-mitigating

effects of guarantees, institutions must

ensure that the written guarantee is

evidenced by an unconditional and

legally enforceable commitment to pay

that remains in force until the debt is

satisfied in full.

Adjustments for guarantees must be

made in accordance with specific

criteria contained in the bank’s credit

policy. The criteria should be plausible

and intuitive, and should address the

guarantor’s ability and willingness to

meet its obligations. Banks are expected

to gather evidence that confirms the

risk-mitigating effect of guarantees.

Other forms of written third-party

support (for example, comfort letters or

letters of awareness) that are not legally

binding should not be used to adjust PD

or LGD unless a bank can demonstrate

through analysis of internal data the

risk-mitigating effect of such support.

Banks may not adjust PDs or LGDs to

reflect implied support or verbal

assurances.

Regardless of the method used to

recognize the risk-mitigating effects of

guarantees, a bank must adopt an

approach that is applied consistently

over time and across the portfolio.

Moreover, the onus is on the bank to

demonstrate that its approach is

supported by logic and empirical

results. While guarantees may provide

grounds for adjusting PD or LGD, they

cannot result in a lower risk weight than

that assigned to a similar direct

obligation of the guarantor.3

Validation Process

S

adopt an

approach that is applied consistently

over time and across the portfolio.

Moreover, the onus is on the bank to

demonstrate that its approach is

supported by logic and empirical

results. While guarantees may provide

grounds for adjusting PD or LGD, they

cannot result in a lower risk weight than

that assigned to a similar direct

obligation of the guarantor.3

Validation Process

S. IRB rating system architecture must

be designed to ensure rating system

accuracy.

As part of their IRB rating system

architecture, banks must implement a

process to ensure the accuracy of their

rating systems. Rating system accuracy

is defined as the combination of the

following outcomes:

• The actual long-run average default

frequency for each rating grade is not

significantly greater than the PD

assigned to that grade.

• The actual stress-condition loss

rates experienced on defaulted facilities

are not significantly greater than the

LGD estimates assigned to those

facilities.

Some differences across individual

grades between observed outcomes and

the estimated parameter inputs to the

IRB equations can be expected. But if

systematic differences suggest a bias

toward lowering regulatory capital

requirements, the integrity of the rating

system (of either the PD or LGD

dimensions or of both) becomes suspect.

Validation is the set of activities

designed to give the greatest possible

assurances of ratings system accuracy.

S. Banks must have ongoing

validation processes that include the

review of developmental evidence,

ongoing monitoring, and the

comparison of predicted parameter

values to actual outcomes (back-testing).

Validation is an integral part of the

rating system architecture. Banks must

have processes designed to give

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ion processes that include the

review of developmental evidence,

ongoing monitoring, and the

comparison of predicted parameter

values to actual outcomes (back-testing).

Validation is an integral part of the

rating system architecture. Banks must

have processes designed to give

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reasonable assurances of their rating

systems’ accuracy. The ongoing process

to confirm and ensure rating system

accuracy consists of:

• The evaluation of developmental

evidence,

• Ongoing monitoring of system

implementation and reasonableness

(verification and benchmarking), and

• Back-testing (comparing actual to

predicted outcomes).

IRB institutions are expected to

employ all of the components of this

process. However, the data to perform

comprehensive back-testing will not be

available in the early stages of

implementing an IRB rating system.

Therefore, banks will have to rely more

heavily on developmental evidence,

quality control tests, and benchmarking

to assure themselves and other

interested parties that their rating

systems are likely to be accurate. Since

the time delay before rating systems can

be back-tested is likely to be an

important issue—because of the rarity of

defaults in most years and the bunching

of defaults in a few years—the other

parts of the validation process will

assume greater importance. If rating

processes are developed in a learning

environment in which banks attempt to

change and improve ratings, back

testing may be delayed even further.

Validation in its early stages will

depend on bank management’s

exercising informed judgment about the

likelihood of the rating system

working—not simply on empirical tests.

Ratings System Developmental

Evidence

The first source of support for the

validity of a bank’s rating system is

developmental evidence

ttempt to

change and improve ratings, back

testing may be delayed even further.

Validation in its early stages will

depend on bank management’s

exercising informed judgment about the

likelihood of the rating system

working—not simply on empirical tests.

Ratings System Developmental

Evidence

The first source of support for the

validity of a bank’s rating system is

developmental evidence. Evaluating

developmental evidence involves

making a reasonable assessment of the

quality of the rating system by analyzing

its design and construction.

Developmental evidence is intended to

answer the question, Could the rating

system be expected to work reasonably

if it is implemented as designed? That

evidence will have to be revisited

whenever the bank makes a change to

its rating system. If a bank adopts a

rating system and does not make

changes, this step will not have to be

revisited. However, since rating systems

are likely to change over time as the

bank learns about the effectiveness of

the system and incorporates the results

of those analyses, the evaluation of

developmental evidence is likely to be

an ongoing part of the process. The

particular steps taken in evaluating

developmental evidence will depend on

the type of rating system.

Generally, the evaluation of

developmental evidence will include a

body of expert opinion. For example,

developmental evidence in support of a

statistical rating model must include

information on the logic that supports

the model and an analysis of the

statistical model-building techniques. In

contrast, developmental evidence in

support of a constrained-judgment

system that features guidance values of

financial ratios might include a

description of the logic and evidence

relating the values of the ratios to past

default and loss outcomes.

Regardless of the type of rating

system, the developmental evidence

will be more persuasive when it

includes empirical evidence on how

well the ratings might have worked in

the past

t of a constrained-judgment

system that features guidance values of

financial ratios might include a

description of the logic and evidence

relating the values of the ratios to past

default and loss outcomes.

Regardless of the type of rating

system, the developmental evidence

will be more persuasive when it

includes empirical evidence on how

well the ratings might have worked in

the past. This evidence should be

available for a statistical model since

such models are chosen to maximize the

fit to outcomes in the development

sample. In addition, statistical models

should be supported by evidence that

they work well outside the development

sample. Use of ‘‘holdout’’ sample

evidence is a good model-building

practice to ensure that the model is not

merely a statistical quirk of the

particular data set used to build the

model.

Empirical developmental evidence of

rating effectiveness will be more

difficult to produce for a judgmental

rating system. Such evidence would

require asking raters how they would

have rated past credits for which they

did not know the outcomes. Those

retrospective ratings could then be

compared to the outcomes to determine

whether the ratings were correct on

average. Conducting such tests,

however, will be difficult because

historical data sets may not include all

of the information that an individual

would have actually used in making a

judgment about a rating.

The sufficiency of the developmental

evidence will itself be a matter of

informed expert opinion. Even if the

rating system is model-based, an

evaluation of developmental evidence

will entail judging the merits of the

model-building technique. Although no

bright line tests are feasible because

expert judgment is essential to the

evaluation of rating system

development, experts will be able to

draw conclusions about whether a well-

implemented system would be likely to

perform satisfactorily

the

rating system is model-based, an

evaluation of developmental evidence

will entail judging the merits of the

model-building technique. Although no

bright line tests are feasible because

expert judgment is essential to the

evaluation of rating system

development, experts will be able to

draw conclusions about whether a well-

implemented system would be likely to

perform satisfactorily.

Ratings System Ongoing Validation

The second source of analytical

support for the validity of a bank rating

system is the ongoing analysis intended

to confirm that the rating system is

being implemented and continues to

perform as intended. Such analysis

involves process verification and

benchmarking.

Process Verification

Verification activities address the

question, Are the ratings being assigned

as intended? Specific verification

activities will depend on the rating

approach. If a model is used for rating,

verification analysis begins by

confirming that the computer code used

to deploy the model is correct. The

computer code can be verified in a

number of established ways. For

example, a qualified expert can

duplicate the code or check the code

line by line. Process verification for a

model will also include confirmation

that the correct data are being used in

the model.

For expert-judgment and constrained-

judgment systems, verification requires

other individual reviewers to evaluate

whether the rater followed rating policy.

The primary requirements for

verification of ratings assigned by

individuals are:

• A transparent rating process,

• A database with information used

by the rater, and

• Documentation of how the

decisions were made.

The specific steps will depend on

how much the process incorporates

specific guidelines and how much the

exercise of judgment is allowed. As the

dependence on specific guidelines

increases, other individuals can more

easily confirm that guidelines were

followed by reference to sufficient

documentation

th information used

by the rater, and

• Documentation of how the

decisions were made.

The specific steps will depend on

how much the process incorporates

specific guidelines and how much the

exercise of judgment is allowed. As the

dependence on specific guidelines

increases, other individuals can more

easily confirm that guidelines were

followed by reference to sufficient

documentation. As the dependence on

judgment rises, the ratings review

function will have to be staffed

increasingly by experts with appropriate

skills and knowledge about the rating

policies of the bank.

Ratings process verification also

includes override monitoring. If

individuals have the ability to override

either models or policies in a

constrained-judgment system, the bank

should have both a policy stating the

tolerance for overrides and a monitoring

system for identifying the occurrence of

overrides. A reporting system capturing

data on reasons for overrides will

facilitate learning about whether

overrides improve accuracy.

Benchmarking

S. Banks must benchmark their

internal ratings against internal, market

and other third-party ratings.

Benchmarking is the set of activities

that uses alternative tools to draw

inferences about the correctness of

ratings before outcomes are actually

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known. The most important type of

benchmarking of a rating system is to

ask whether another rater or rating

method attaches the same rating to a

particular obligor or facility. Regardless

of the rating approach, the benchmark

can be either a judgmental or a model-

based rating. Examples of such

benchmarking include:

• Ratings reviewers who completely

re-rate a sample of credits rated by

individuals in a judgmental system.

• An internally developed model is

used to rate credits rated earlier in a

judgmental system

same rating to a

particular obligor or facility. Regardless

of the rating approach, the benchmark

can be either a judgmental or a model-

based rating. Examples of such

benchmarking include:

• Ratings reviewers who completely

re-rate a sample of credits rated by

individuals in a judgmental system.

• An internally developed model is

used to rate credits rated earlier in a

judgmental system.

• Individuals rate a sample of credits

rated by a model.

• Internal ratings are compared

against results from external agencies or

external models.

Because it will take considerable time

before outcomes will be available, using

alternative ratings as benchmarks will

be a very important validation device.

Such benchmarking must be applied to

all rating approaches, and the

benchmark can be either a model or

judgment. At a minimum, banks must

establish a process in which a

representative sample of its internal

ratings is compared to third-party

ratings (e.g., independent internal raters,

external rating agencies, models, or

other market data sources) of the same

credits.

Benchmarking also includes activities

designed to draw broader inferences

about whether the rating system—as

opposed to individual ratings—is

working as expected. The bank can look

for consistency in ranking or

consistency in the values of rating

characteristics for similarly rated

credits. Examples of such benchmarking

activities include:

• Analyzing the characteristics of

obligors that have received common

ratings.

• Monitoring changes in the

distribution of ratings over time.

• Calculating a transition matrix

calculated from changes in ratings in a

bank’s portfolio and comparing it to

historical transition matrices from

internal bank data or publicly available

ratings.

While benchmarking activities allow

for inferences about the correctness of

the ratings system, they are the not same

thing as back-testing. The benchmark

itself is a prediction and may be in

error

culating a transition matrix

calculated from changes in ratings in a

bank’s portfolio and comparing it to

historical transition matrices from

internal bank data or publicly available

ratings.

While benchmarking activities allow

for inferences about the correctness of

the ratings system, they are the not same

thing as back-testing. The benchmark

itself is a prediction and may be in

error. If benchmarking evidence

suggests a pattern of rating differences,

it should lead the bank to investigate the

source of the differences. Thus, the

benchmarking process illustrates the

possibility of feedback from ongoing

validation to model development,

underscoring the characterization of

validation as a process.

Back Testing

S. Banks must develop statistical tests

to back-test their IRB rating systems.

S. Banks must establish internal

tolerance limits for differences between

expected and actual outcomes.

S. Banks must have a policy that

requires remedial actions be taken when

policy tolerances are exceeded.

The third component of a validation

process is back-testing, which is the

comparison of predictions with actual

outcomes. Back-testing of IRB systems is

the empirical test of the accuracy of the

parameter values, PD and LGD,

associated with obligor and loss severity

ratings, respectively. For IRB rating

systems, back-testing addresses the

combined effectiveness of the

assignment of obligor and loss severity

ratings and the calibration of the

parameters PD and LGD attached to

those ratings.

At this time, there is no generally

agreed-upon statistical test of the

accuracy of IRB systems. Banks must

develop statistical tests to back-test their

IRB rating systems. In addition, banks

must have a policy that specifies

internal tolerance limits for comparing

back-testing results. Importantly, that

policy must outline the actions that

would be taken whenever policy limits

are exceeded

this time, there is no generally

agreed-upon statistical test of the

accuracy of IRB systems. Banks must

develop statistical tests to back-test their

IRB rating systems. In addition, banks

must have a policy that specifies

internal tolerance limits for comparing

back-testing results. Importantly, that

policy must outline the actions that

would be taken whenever policy limits

are exceeded.

As a combined test of ratings

effectiveness, back-testing is a

conceptual bridge between the ratings

system architecture discussed in this

chapter and the quantification of

parameters, discussed in Chapter 2. The

final section of Chapter 2 discusses

back-testing as one type of quantitative

test required to validate the

quantification of parameter values.

III. Quantification of IRB Systems

Ratings quantification is the process

of assigning numerical values to the four

key components for internal ratings-

based assessments of credit-risk capital:

probability of default (PD), the expected

loss given default (LGD), the expected

exposure at default (EAD), and maturity

(M). Section I establishes an organizing

framework for considering IRB

quantification and develops general

principles that apply to the entire

process. Sections II through IV cover

specific principles or supervisory

standards that apply to PD, LGD, and

EAD respectively. The maturity

component, which is much less

dependent on statistical estimates and

the use of data, receives somewhat

different treatment in section V.

Validation of the quantification process

is covered in section VI.

A. Introduction

Stages of the Quantification Process

With the exception of maturity, the

risk components are unobservable and

must be estimated. The estimation must

be consistent with sound practice and

supervisory standards. In addition, a

bank must have processes to ensure that

these estimates remain valid

n section V.

Validation of the quantification process

is covered in section VI.

A. Introduction

Stages of the Quantification Process

With the exception of maturity, the

risk components are unobservable and

must be estimated. The estimation must

be consistent with sound practice and

supervisory standards. In addition, a

bank must have processes to ensure that

these estimates remain valid.

Calculation of risk components for

IRB involves two sets of data: the bank’s

actual portfolio data, consisting of

current credit exposures assigned to

internal grades, and a ‘‘reference data

set,’’ consisting of a set of defaulted

credits (in the case of LGD and EAD

estimation) or both defaulted and non-

defaulted credits (in the case of PD

estimation). The bank estimates a

relationship between the reference data

set and probability of default, loss

severity, or exposure; then this

estimated relationship is applied to the

actual portfolio data for which capital is

being assessed.

Quantification proceeds through four

logical stages: obtaining reference data;

estimating the reference data’s

relationship to the parameters; mapping

the correspondence between the

reference data and the portfolio’s data;

and applying the relationship between

reference data and parameters to the

portfolio’s data. (Readers may find it

helpful to refer to the appendix to this

chapter, which illustrates how this four-

stage framework can be applied to

ratings quantification approaches in

practice.) An evaluation of any bank’s

IRB quantification process focuses on

understanding how the bank

implements each stage for each of the

key parameters, and on assessing the

adequacy of the bank’s approach.

Data—First, the bank constructs a

reference data set, or source of data,

from which parameters can be

estimated.

Reference data sets include internal

data, external data, and pooled internal/

external data

any bank’s

IRB quantification process focuses on

understanding how the bank

implements each stage for each of the

key parameters, and on assessing the

adequacy of the bank’s approach.

Data—First, the bank constructs a

reference data set, or source of data,

from which parameters can be

estimated.

Reference data sets include internal

data, external data, and pooled internal/

external data. Important considerations

include the comparability of the

reference data to the current credit

portfolio, whether the sample period

‘‘appropriately’’ includes periods of

stress, and the definition of default used

in the reference data. The reference data

must be described using a set of

observed characteristics; consequently,

the data set must contain variables that

can be used for this characterization.

Relevant characteristics might include

external debt ratings, financial

measures, geographic regions, or any

other factors that are believed to be

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related in some way to PD, LGD, or

EAD. More than one reference data set

may be used.

Estimation—Second, the bank applies

statistical techniques to the reference

data to determine a relationship

between characteristics of the reference

data and the parameters (PD, LGD, or

EAD).

The result of this step is a model that

ties descriptive characteristics of the

obligor or facility in the reference data

set to PD, LGD, or EAD estimates. In this

context, the term ‘models’ is used in the

most general sense; a model may be

simple, such as the calculation of

averages, or more complicated, such as

an approach based on advanced

regression techniques. This step may

include adjustments for differences

between the IRB definition of default

and the default definition in the

reference data set, or adjustments for

data limitations

In this

context, the term ‘models’ is used in the

most general sense; a model may be

simple, such as the calculation of

averages, or more complicated, such as

an approach based on advanced

regression techniques. This step may

include adjustments for differences

between the IRB definition of default

and the default definition in the

reference data set, or adjustments for

data limitations. More than one

estimation technique may be used to

generate estimates of the risk

components, especially if there are

multiple sets of reference data or

multiple sample periods.

Mapping—Third, the bank creates a

link between its portfolio data and the

reference data based on common

characteristics.

Variables or characteristics that are

available for the current portfolio must

be mapped to the variables used in the

default, loss-severity, or exposure

model. (In some cases, the bank

constructs the link for a representative

exposure in each internal grade, and the

mapping is then applied to all credits

within a grade.) An important element

of mapping is making adjustments for

differences between reference data sets

and the bank’s portfolio. The bank must

create a mapping for each reference data

set and for each combination of

variables used in any estimation model.

Application—Fourth, the bank

applies the relationship estimated for

the reference data to the actual portfolio

data.

The ultimate aim of quantification is

to attribute a PD, LGD, or EAD to each

exposure within the portfolio, or to each

internal grade if the mapping was done

at the grade level. This step may include

adjustments to default frequencies or

loss rates to ‘‘smooth’’ the final

parameter estimates. If the estimates are

applied to individual transactions, the

bank must in some way aggregate the

estimates at the grade level. In addition,

if multiple data sets or estimation

methods are used, the bank must adopt

a means of combining the various

estimates

the grade level. This step may include

adjustments to default frequencies or

loss rates to ‘‘smooth’’ the final

parameter estimates. If the estimates are

applied to individual transactions, the

bank must in some way aggregate the

estimates at the grade level. In addition,

if multiple data sets or estimation

methods are used, the bank must adopt

a means of combining the various

estimates.

A number of examples are given in

this chapter to aid exposition and

interpretation. None of the examples is

sufficiently detailed to incorporate all

the considerations discussed in this

chapter. Moreover, technical progress in

the area of quantification is rapid. Thus,

banks should not interpret an example

that is consistent with the standard

being discussed, and that resembles the

bank’s current practice, as creation of a

‘‘safe harbor’’ or as an indication that

the bank’s practice will be approved as-

is. Banks should consider this guidance

in its entirety when determining

whether systems and practices are

adequate.

General Principles for Sound IRB

Quantification

Several core principles apply to all

elements of the overall ratings

quantification process; those general

principles are discussed in this

introductory section. Each of these

principles is, in effect, a supervisory

standard for IRB systems. Other

supervisory standards, specific to

particular elements or parameters, are

discussed in the relevant sections.

Supervisory evaluation of IRB

quantification requires consideration of

all of these principles and standards,

both general and specific. Particular

practical approaches to ratings

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

strengths and weaknesses of a bank’s

chosen approach, using these

supervisory standards as a guide.

S

f these principles and standards,

both general and specific. Particular

practical approaches to ratings

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

strengths and weaknesses of a bank’s

chosen approach, using these

supervisory standards as a guide.

S. IRB institutions must have a fully

specified process covering all aspects of

quantification (reference data,

estimation, mapping, and application).

The quantification process, including

the role and scope of expert judgment,

must be fully documented and updated

periodically.

A fully specified quantification

process must describe how all four

stages (data, estimation, mapping, and

application) are implemented for each

parameter. Documentation promotes

consistency and allows third parties to

review and replicate the entire process.

Examples of third parties that might use

the documentation include rating-

system reviewers, auditors, and bank

supervisors. Periodic updates to the

process must be conducted to ensure

that new data, analytical techniques,

and evolving industry practice are

incorporated into the quantification

process.

S. Parameter estimates and related

documentation must be updated

regularly.

The parameter estimates must be

updated at least annually, and the

process for doing so must be

documented in bank policy. The update

should also evaluate the judgmental

adjustments embedded in the estimates;

new data or techniques may suggest a

need to modify those adjustments.

Particular attention should be given to

new business lines or portfolios in

which the mix of obligors is believed to

have changed substantially. A material

merger, acquisition, divestiture, or exit

clearly raises questions about the

continued applicability of the process

and should trigger an intensive review

and updating

data or techniques may suggest a

need to modify those adjustments.

Particular attention should be given to

new business lines or portfolios in

which the mix of obligors is believed to

have changed substantially. A material

merger, acquisition, divestiture, or exit

clearly raises questions about the

continued applicability of the process

and should trigger an intensive review

and updating.

The updating process is particularly

relevant for the reference data stage

because new data become available all

the time. New data must be

incorporated, into the PD, LGD, and

EAD estimates, using a well-defined

process.

S. A bank must subject all aspects of

the quantification process, including

design and implementation, to an

appropriate degree of independent

review and validation.

An independent review is an

assessment conducted by persons not

accountable for the work being

reviewed. The reviewers may be either

internal or external parties. The review

serves as a check that the quantification

process is sound and works as intended;

it should be broad-based, and must

include all of the elements of the

quantification process that lead to the

ultimate estimates of PD, LGD, and

EAD. The review must cover the full

scope of validation: evaluation of the

integrity of data inputs, analysis of the

internal logic and consistency of the

process, comparison with relevant

benchmarks, and appropriate back-

testing based on actual outcomes.

S. Judgmental adjustments may be an

appropriate part of the quantification

process, but must not be biased toward

lower estimates of risk.

Judgment will inevitably play a role

in the quantification process and may

materially affect the estimates.

Judgmental adjustments to estimates are

often necessary because of some

limitations on available reference data

or because of inherent differences

between the reference data and the

bank’s portfolio data

uantification

process, but must not be biased toward

lower estimates of risk.

Judgment will inevitably play a role

in the quantification process and may

materially affect the estimates.

Judgmental adjustments to estimates are

often necessary because of some

limitations on available reference data

or because of inherent differences

between the reference data and the

bank’s portfolio data. The bank must

ensure that adjustments are not biased

toward optimistically low parameter

estimates for PD, LGD, and EAD.

Individual assumptions are less

important than broad patterns;

consistent signs of judgmental decisions

that lower parameter estimates

materially may be evidence of bias.

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The reasoning and empirical support

for any adjustments, as well as the

mechanics of the calculation, must be

documented. The bank should conduct

sensitivity analysis to demonstrate that

the adjustment procedure is not biased

toward reducing capital requirements.

The analysis must consider the impact

of any judgmental adjustments on

estimates and risk weights, and must be

fully documented.

S. Parameter estimates must

incorporate a degree of conservatism

that is appropriate for the overall

robustness of the quantification process.

In estimating values of PD, LGD, and

EAD should be as precise and accurate

as possible. However, estimates of PD,

LGD and EAD are statistics, and thus

inherently subject to uncertainty and

potential error. It is often possible to be

reasonably confident that a risk

component or other parameter lies

within a particular range, but greater

precision is difficult to achieve

fication process.

In estimating values of PD, LGD, and

EAD should be as precise and accurate

as possible. However, estimates of PD,

LGD and EAD are statistics, and thus

inherently subject to uncertainty and

potential error. It is often possible to be

reasonably confident that a risk

component or other parameter lies

within a particular range, but greater

precision is difficult to achieve. Aspects

of the ratings quantification process that

are apt to introduce uncertainty and

potential error include the following:

The estimation of coefficients of

particular variables in a regression-

based statistical default or severity

model.

• The calculation of average default

or loss rates for particular categories of

credits in external default databases.

• The mapping between portfolio

obligors or facilities and reference data

when the set of common characteristics

does not align exactly.

A general principle of the IRB

approach is that a bank must adjust

estimates conservatively in the presence

of uncertainty or potential error. In

many cases this corresponds to

assigning a final parameter estimate that

increases required capital relative to the

best estimate produced through sound-

practice estimation techniques. The

extent of this conservative adjustment

should be related to factors such as the

relevance of the reference data, the

quality of the mapping, the precision of

the statistical estimates, and the amount

of judgment used throughout the

process. Margins of conservatism need

not be added at each step; indeed, that

could produce an excessively

conservative result. The overall margin

of conservatism should adequately

account for all uncertainties and

weaknesses; this is the general

interpretation of requirements to

incorporate appropriate degrees of

conservatism. Improvements in the

quantification process (use of better

data, estimation techniques, and so on)

may reduce the appropriate degree of

conservatism over time

essively

conservative result. The overall margin

of conservatism should adequately

account for all uncertainties and

weaknesses; this is the general

interpretation of requirements to

incorporate appropriate degrees of

conservatism. Improvements in the

quantification process (use of better

data, estimation techniques, and so on)

may reduce the appropriate degree of

conservatism over time.

Estimates of PD, LGD, EAD, or other

parameters or coefficients should be

presented with an accompanying sense

of the statistical precision of the

estimates; this facilitates an assessment

of the appropriate degree of

conservatism.

B. Probability of Default (PD)

Data

To estimate PD accurately, a bank

must have a comprehensive reference

data set with observations that are

comparable to the bank’s current

portfolio of obligors. Clearly, the data

set used for estimation should be similar

to the portfolio to which such estimates

will be applied. The same comparability

standard applies to both internal and

external data sets.

To ensure ongoing applicability of the

reference data, a bank must assess the

characteristics of its current obligors

relative to the characteristics of obligors

in the reference data. Such variables

might include qualitative and

quantitative obligor information,

internal and external rating, rating

dates, and line of business or geography.

To this end, a bank must maintain

documentation that fully describes all

explanatory variables in the data set,

including any changes to those variables

over time. A well-defined and

documented process must be in place to

ensure that the reference data are

updated as frequently as is practical, as

fresh data become available or portfolio

changes make necessary.

S. The sample for the reference data

must be at least five years, and must

include periods of economic stress

during which default rates were

relatively high

s to those variables

over time. A well-defined and

documented process must be in place to

ensure that the reference data are

updated as frequently as is practical, as

fresh data become available or portfolio

changes make necessary.

S. The sample for the reference data

must be at least five years, and must

include periods of economic stress

during which default rates were

relatively high.

To foster more robust estimation,

banks should use longer time series

when more than five years of data are

available. However, the benefits of using

a longer time series (longer than five

years) may have to be weighed against

a possible loss of data comparability.

The older the reference data, the less

similar they are likely to be to the bank’s

current portfolio; striking the correct

balance is a matter of judgment.

Reference obligors must not differ from

the current portfolio obligors

systematically in ways that seem likely

to be related to obligor default risk.

Otherwise, the derived PD estimates

may not be applicable to the current

portfolio.

Note that this principle does not

simply restate the requirement for five

years of data: periods of stress during

which default rates are relatively high

must be included in the data sample.

Exclusion of such periods biases PD

estimates downward and unjustifiably

lowers regulatory capital requirements.

Example. A bank’s reference data set

covers the years 1987 through 2001. Each

year includes identical data elements, and

each year is similarly populated. For its grade

PD estimates, the bank relies upon data from

a sub-sample covering 1992 through 2001.

The bank provides no justification for

dropping the years from 1987 through 1991.

The bank contends that it is not necessary to

include those data, as the reference sample

they use for estimation satisfies the five-year

requirement. This practice is not consistent

with the standard because the bank has not

supported its decision to ignore available

data

-sample covering 1992 through 2001.

The bank provides no justification for

dropping the years from 1987 through 1991.

The bank contends that it is not necessary to

include those data, as the reference sample

they use for estimation satisfies the five-year

requirement. This practice is not consistent

with the standard because the bank has not

supported its decision to ignore available

data. The fact that the excluded years include

a recession would raise particular concerns.

S. The definition of default within the

reference data must be reasonably

consistent with the IRB definition of

default.

Regardless of the source of the

reference data, a bank must apply the

same default definition throughout the

quantification processes. This fosters

consistent estimation across parameters

and reduces the potential for undesired

bias. In addition, consistent application

of the same definition across banks will

permit true horizontal analysis by

supervisors and engaged market

participants.

This standard applies to both internal

and external reference data. For internal

data, a bank’s default definition is

expected to be consistent with the IRB

definition going forward. Banks will be

expected to make appropriate

adjustments to their data systems such

that all defaults as defined for IRB are

captured by the time a bank fully

implements its IRB system. For any

historical or external data that do not

fully comply with the IRB definition of

default, a bank must make conservative

adjustments to reflect such

discrepancies. Larger discrepancies

require larger adjustments for

conservatism.

Example. To identify defaults in its

historical data, a bank applies a consistent

definition of ‘‘placed on nonaccrual.’’ This

definition is used in the bank’s quantification

exercises to estimate PD, LGD, and EAD. The

bank recognizes that use of the nonaccrual

definition fails to capture certain defaults as

identified in the IRB rules

epancies

require larger adjustments for

conservatism.

Example. To identify defaults in its

historical data, a bank applies a consistent

definition of ‘‘placed on nonaccrual.’’ This

definition is used in the bank’s quantification

exercises to estimate PD, LGD, and EAD. The

bank recognizes that use of the nonaccrual

definition fails to capture certain defaults as

identified in the IRB rules. Specifically, the

bank indicates that the following kinds of

defaulted facilities would not have been

placed on nonaccrual: (1) Credit obligations

that were sold at a material credit-related

economic loss, and (2) distressed

restructurings. To be consistent with the

standard, the bank must make a well-

supported adjustment to its grade PD

estimates to reflect the difference in the

default definitions.

Estimation

Estimation of PD is the process by

which characteristics of the reference

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4 The New Basel Capital Accord produced by the

Basel Committee on Banking Supervision discusses

three techniques for PD estimation. IRB banks are

not constrained to select from among these three

techniques; they have broad flexibility to

implement appropriate approaches to

quantification. The three Basel techniques are best

regarded not as a complete taxonomy of the

possible approaches to PD estimation, but rather as

illustrations of a few of the many possible

approaches.

data are related to default frequencies.4

The relevant characteristics that help to

determine the likelihood of default are

referred to as ‘‘drivers of default’’.

Drivers might include variables such as

financial ratios, management expertise,

industry, and geography.

S. Estimates of default rates must be

empirically based and must represent a

long-run average

the many possible

approaches.

data are related to default frequencies.4

The relevant characteristics that help to

determine the likelihood of default are

referred to as ‘‘drivers of default’’.

Drivers might include variables such as

financial ratios, management expertise,

industry, and geography.

S. Estimates of default rates must be

empirically based and must represent a

long-run average.

Estimates must capture average

default experience over a reasonable

mix of high-default and low-default

years of the economic cycle. The

average is labeled ‘‘long-run’’ because a

long observation period would span

both peaks and valleys of the economic

cycle. The emphasis should not be on

time-span; the long-run average concept

captures the breadth, not the length, of

experience.

If the reference data are characterized

by internal or external rating grades, one

estimation approach is to calculate the

mean of one-year realized default rates

for each grade, giving equal weight to

each year’s realized default rate. PD

estimates generally should be calculated

in this manner.

Another approach is to pool obligors

in a given grade over a number of years

and then calculate the mean default

rate. In this case, each year’s default rate

is weighted by the number of obligors.

This approach may underestimate

default rates. For example, if lending

declines in recessions so that obligors

are fewer in those years than in others,

weighting by number of obligors would

dilute the effect of the recession year on

the overall mean. The obligor-weighted

calculation, or another approach, will be

allowed only if the bank can

demonstrate that this approach provides

a better estimate of the long-run average

PD. At a minimum, this would involve

comparing the results of both methods.

Statistical default prediction models

may also play a role in PD estimation

uld

dilute the effect of the recession year on

the overall mean. The obligor-weighted

calculation, or another approach, will be

allowed only if the bank can

demonstrate that this approach provides

a better estimate of the long-run average

PD. At a minimum, this would involve

comparing the results of both methods.

Statistical default prediction models

may also play a role in PD estimation.

For example, the characteristics of the

reference data might include financial

ratios or a distance-to-default measure,

as defined by a specific implementation

of a Merton-style structural model.

For a model-based approach to meet

the requirement that ultimate grade PD

estimates be long-run averages, the

reference data used in the default model

must meet the long-run requirement.

For example, a model can be used to

relate financial ratios to likelihood of

default based on the outcome for the

firms—default or non-default. Such a

model must be calibrated to capture the

default experience over a reasonable

mix of good and bad years of the

economic cycle. The same requirement

would hold for a structural model;

distance to default must be calibrated to

default frequency using long-run

experience. This applies to both internal

and vendor models, and a bank must

verify that this requirement is met.

Example 1. A bank uses external data from

a rating agency to estimate PD. The PD

estimate for each agency grade is calculated

as the mean of yearly realized default rates

over a time period (1980 through 2001) that

includes several recessions and high-default

years. The bank provides support that this

time period adequately represents long-run

experience. This illustrates an estimation

method that is consistent with the standard.

Example 2a. Like the institution in

example 1, a bank maps internal ratings to

agency grades. The estimates for the agency

grades are set indirectly, using the default

probabilities from a default prediction model

ault

years. The bank provides support that this

time period adequately represents long-run

experience. This illustrates an estimation

method that is consistent with the standard.

Example 2a. Like the institution in

example 1, a bank maps internal ratings to

agency grades. The estimates for the agency

grades are set indirectly, using the default

probabilities from a default prediction model.

The bank does so because although it links

internal and agency grades, the bank views

the default model’s results as more predictive

than the historical agency default experience.

For each agency grade, the bank calculates a

PD estimate as the mean of the model-based

default probabilities for the agency-rated

obligors. In order to meet the long-run

requirement, the bank calculates the

estimates over the seven years from 1995

through 2001. The bank demonstrates that

this time period includes a reasonable mix of

high-default and low-default experience.

This estimation method is consistent with

the standard.

Example 2b. In a variant of example 2a, a

bank uses the mean default frequency per

agency rating grade for a single year, such as

2001. Empirical evidence shows that the

mean default frequency for agency grades

varies substantially from year to year. A

single year thus does not reflect the full range

of experience, because a long-run average

should be relatively stable year to year. Such

instability makes this estimation method

unacceptable.

Example 2c. Another bank calculates the

agency grade PD estimates as the median

default probability of companies in that

grade. The bank does so without

demonstrating that the median is a better

statistical estimator than the mean. This

estimation method is not consistent with the

standard. A median gives less weight to

obligors with high estimated default

probabilities than a simple mean does

le 2c. Another bank calculates the

agency grade PD estimates as the median

default probability of companies in that

grade. The bank does so without

demonstrating that the median is a better

statistical estimator than the mean. This

estimation method is not consistent with the

standard. A median gives less weight to

obligors with high estimated default

probabilities than a simple mean does. The

difference between mean and median can be

material because distributions of credits

within grades often are substantially skewed

toward higher default probabilities: the

riskier obligors within a grade tend to have

individual default probabilities that are

substantially worse than the median, while

the least risky have default probabilities only

somewhat better than the median.

S. Judgmental adjustments may play

an appropriate role in PD estimation,

but must not be biased toward lower

estimates.

The following examples illustrate

how supervisors will evaluate

adjustments:

Example 1. A bank uses the last five years

of internal default history to estimate grade

PDs. However, they recognize that the

internal experience does not include any

high-default years. In order to remedy this

and still take advantage of its experience, the

bank uses external agency data to adjust the

estimates upward. Using the agency data, the

bank calculates the ratio between the long-

run average and the mean default rate per

grade over the last five years. The bank

assumes that the relationship observed in the

agency data applies to its portfolio, and

adjusts the estimates for the internal data

accordingly. This practice is consistent with

the standard.

Example 2. A bank uses internal default

experience to estimate grade PDs. However,

the bank has historically failed to recognize

defaults when the loss on the default

obligation was avoided by seizing collateral.

The bank makes no adjustment for such

missing defaults

its portfolio, and

adjusts the estimates for the internal data

accordingly. This practice is consistent with

the standard.

Example 2. A bank uses internal default

experience to estimate grade PDs. However,

the bank has historically failed to recognize

defaults when the loss on the default

obligation was avoided by seizing collateral.

The bank makes no adjustment for such

missing defaults. The realized default rate

using the more inclusive definition would be

higher than that observed by the bank (and

loss severity rates would be correspondingly

lower). This practice would not be consistent

with the standard, unless the bank

demonstrates that the necessary adjustment

is immaterial.

Mapping

Mapping is the process of establishing

a correspondence between the bank’s

current obligors and the reference

obligor data used in the default model.

Hence, mapping involves identifying

how default-related characteristics of

the current portfolio correspond to the

characteristics of reference obligors.

Such characteristics might include

financial and nonfinancial variables,

and assigned ratings or grades.

Mapping can be thought of as taking

each obligor in the bank’s portfolio and

characterizing it as if it were part of the

reference data. There are two broad

approaches to the mapping process:

Obligor mapping: Each portfolio

obligor is mapped to the reference data

based on its individual characteristics.

For example, if a bank applies a default

model, a default probability will be

generated for each obligor. That

individual default probability is then

used to assign each obligor to a

particular internal grade, based on the

bank’s established criteria. To obtain a

final estimate of the grade PD in the

subsequent application stage, the bank

averages the default probabilities of

individual obligors within each grade

lies a default

model, a default probability will be

generated for each obligor. That

individual default probability is then

used to assign each obligor to a

particular internal grade, based on the

bank’s established criteria. To obtain a

final estimate of the grade PD in the

subsequent application stage, the bank

averages the default probabilities of

individual obligors within each grade.

Grade mapping: Characteristics of the

obligors within an internal grade are

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averaged or otherwise summarized to

construct a ‘‘typical’’ or representative

obligor for each grade. Then, the bank

maps that representative obligor to the

reference data. For example, if the bank

uses a default model, the default

probability associated with that typical

obligor will serve as the grade PD in the

application stage. Alternatively, the

bank may map the typical obligor to a

particular external rating grade based on

quantitative and qualitative

characteristics, and assign the long-run

default rate for that rating to the internal

grade in the application stage.

Either grade mapping or obligor

mapping can be part of the

quantification process; either method

can produce a single PD estimate for

each grade in the application stage.

However, in the absence of other

compelling considerations, banks

should use obligor mapping for two

reasons:

• First, default probabilities are

nonlinear under many estimation

approaches. As a result, the default

probability of the typical obligor—the

result of a grade mapping approach—is

often lower than the mean of the

individual obligor default probabilities

from the obligor mapping approach. For

example, consider a bank that maps to

the S&P scale and uses historical S&P

bond default rates

• First, default probabilities are

nonlinear under many estimation

approaches. As a result, the default

probability of the typical obligor—the

result of a grade mapping approach—is

often lower than the mean of the

individual obligor default probabilities

from the obligor mapping approach. For

example, consider a bank that maps to

the S&P scale and uses historical S&P

bond default rates. For ease of

illustration, suppose that one internal

grade contains only three obligors that

individually map to BB, BB¥, and B+.

The historical default rates for these

three grades are 1.07, 1.76, and 3.24

percent, respectively (based on 1981–

2001 data). Using obligor mapping,

those rates would be assigned directly to

the three obligors, yielding a mean PD

of 2.02 percent for the grade. Using

grade mapping, the grade PD would be

only 1.76, because the grade’s typical

obligor is rated BB¥.

• Second, a hypothetical obligor with

a grade’s average characteristics may not

represent well the risks presented by the

grade’s typical obligor. For example, a

bank might observe that obligors with

high leverage and low earnings

variability have about the same default

risk as obligors with low leverage and

high earnings variability. These two

types of obligors might both end up in

the same grade, for example, Grade 6. If

so, the typical obligor in Grade 6 would

have moderate leverage and moderate

earnings variability—a combination that

might fail to reflect any of the

individual obligors in Grade 6, and that

could easily result in a PD for the grade

that is too low.

A bank electing to use grade mapping

instead of obligor mapping should be

especially careful in choosing a

‘‘typical’’ obligor for each grade. Doing

so typically requires that the bank

examine the actual distribution of

obligors within each grade, as well as

the characteristics of those obligors

dual obligors in Grade 6, and that

could easily result in a PD for the grade

that is too low.

A bank electing to use grade mapping

instead of obligor mapping should be

especially careful in choosing a

‘‘typical’’ obligor for each grade. Doing

so typically requires that the bank

examine the actual distribution of

obligors within each grade, as well as

the characteristics of those obligors.

Banks should be aware that different

measures of central tendency (such as

mean, median, or mode) will give

different results, and that these different

results may have a material effect on a

grade’s PD; they must be able to justify

their choice of a measure. Banks must

have a clear and consistent policy

toward the calculation.

S. The mapping must be based on a

robust comparison of available data

elements that are common to the

portfolio and the reference data.

Sound mapping practice uses all

common elements that are available in

the data as the basis for mapping. If a

bank chooses to ignore certain common

variables or to weight some variables

more heavily than others, those choices

must be supported. Mapping should

also take into account differences in

rating philosophy (for example, point-

in-time or through-the-cycle) between

any ratings embedded in the reference

data set and the bank’s own rating

regime.

A mapping should be plausible, and

should be consistent with the rating

philosophy established by the bank as

part of its obligor rating policy. For a

bank that uses grade mapping, levels

and ranges of key variables within each

internal grade should be close to values

of similar variables for corresponding

obligors within the reference data.

The standard allows for use of a

limited set of common variables that are

predictive of default risk, in part to

permit flexibility in early years when

data may be far from ideal

ting policy. For a

bank that uses grade mapping, levels

and ranges of key variables within each

internal grade should be close to values

of similar variables for corresponding

obligors within the reference data.

The standard allows for use of a

limited set of common variables that are

predictive of default risk, in part to

permit flexibility in early years when

data may be far from ideal.

Nevertheless, banks will eventually be

expected to use variables that are widely

recognized as the most reliable

predictors of default risk in mapping

exercises. In the meantime, banks

relying on data elements that are weak

predictors must compensate by making

their estimates more conservative. For

example, leverage and cash flow are

widely recognized to be reliable

predictors of corporate default risk.

Borrower size is also predictive, but less

so. A mapping based solely on size is by

nature less reliable than one based on

leverage, cash flow, and size.

Example 1. In estimating PD, a bank relies

on observed default rates on bonds in various

agency grades for PD quantification. To map

its internal grades to the agency grades, the

bank identifies variables that together explain

much of the rating variation in the bond

sample. The bank then conducts a statistical

analysis of those same variables within its

portfolio of obligors, using a multivariate

distance calculation to assign each portfolio

obligor to the external rating whose

characteristics it matches most closely (for

example, assigning obligors to ratings so that

the sum of squared differences between the

external grade averages and the obligor’s

characteristics is minimized). This practice is

broadly consistent with the standard.

Example 2. A bank uses grade mapping to

link portfolio obligors to the reference data

set described by agency ratings

ating whose

characteristics it matches most closely (for

example, assigning obligors to ratings so that

the sum of squared differences between the

external grade averages and the obligor’s

characteristics is minimized). This practice is

broadly consistent with the standard.

Example 2. A bank uses grade mapping to

link portfolio obligors to the reference data

set described by agency ratings. The bank

looks at publicly rated portfolio obligors

within an internal grade to determine the

most common external rating, does the same

for all grades, and creates a correspondence

between internal and external ratings. The

strength of the correspondence is a function

of the number of externally rated obligors

within each grade, the distribution of those

external ratings within each grade and the

similarity of externally rated obligors in the

grade to those not externally rated. This

practice is broadly consistent with this

standard, but would require a comparison of

rating philosophies and may require

adjustments and the addition of margins of

conservatism.

S. A mapping process must be

established for each reference data set

and for each estimation model.

Banks should never assume that a

mapping is self-evident. Even a rating

system that has been explicitly designed

to replicate external agency ratings may

or may not be effective in producing a

replica; formal mapping is still

necessary. Indeed, in such a system the

kind of analysis involved in mapping

may help identify inconsistencies in the

rating process itself.

A mapping process is needed even

where the reference obligors come from

internal historical experience. Banks

must not assume that internal data do

not require mapping, because changes

in bank strategy or external economic

forces may alter the composition of

internal grades or the nature of the

obligors in those grades over time

help identify inconsistencies in the

rating process itself.

A mapping process is needed even

where the reference obligors come from

internal historical experience. Banks

must not assume that internal data do

not require mapping, because changes

in bank strategy or external economic

forces may alter the composition of

internal grades or the nature of the

obligors in those grades over time.

Mappings must be reaffirmed regardless

of whether rating criteria or other

aspects of the ratings system have

undergone explicit changes during the

period covered by the reference data set.

Banks often use multiple reference

data sets, and then combine the

resulting estimates to get a grade PD. A

bank that does that must conduct a

rigorous mapping process for each data

set.

Supervisors expect all meaningful

characteristics of obligors to be factored

directly into the rating process; this

should include characteristics like the

obligor’s industry or physical location.

But in some circumstances, certain

effects related to industry, geography, or

other factors are not reflected in rating

assignments or default estimates. In

such cases, it may be appropriate for

banks to capture the impact of the

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5 For example, suppose a bank asserts that its

Grade 3 corresponds to an S&P rating of A.

Applying reverse mapping, the bank would take a

sample of A-rated obligors from the reference data,

run them through the bank’s rating process (perhaps

a simplified version), and check to see that those

obligors usually receive a grade of 3 on the bank’s

internal scale.

omissions by using different mappings

for different business lines or types of

obligors

orresponds to an S&P rating of A.

Applying reverse mapping, the bank would take a

sample of A-rated obligors from the reference data,

run them through the bank’s rating process (perhaps

a simplified version), and check to see that those

obligors usually receive a grade of 3 on the bank’s

internal scale.

omissions by using different mappings

for different business lines or types of

obligors. Supervisors expect this

practice to be transitional; banks will

eventually be required to incorporate

the omitted effects into the rating

system and the estimation process as

they are uncovered and documented,

rather than adjusting the mapping.

Example 1. The bank maps its internal

grades carefully to one rating agency, and

then assumes a correspondence to another

agency’s scale despite known differences in

the rating methods of the two agencies. The

bank then applies a mean of the grade default

rates from these two public debt-rating

agencies to its internal grades. This practice

is not consistent with the standard, because

the bank should map to each agency’s scale

separately.

Example 2. A bank uses internal historical

data as its reference data. The bank computes

a mean default rate for each grade as the

grade PD for capital purposes, and asserts

that mapping is unnecessary because ‘‘its

strong credit culture ensures that a 4 is

always a 4.’’ This practice is not consistent

with the standard, because no mapping has

been done; there is no assurance that a

representative obligor in a grade today is

comparable to an obligor in that same grade

in the past.

S. The mapping must be updated and

independently validated regularly.

The appropriate mapping between a

bank’s portfolio and the reference data

may change over time. For example,

relationships between internal grades

and external agency grades may change

during the economic cycle because of

differences in rating philosophy.

Similarly, distance-to-default measures

for obligors in a given grade may not be

constant over time

independently validated regularly.

The appropriate mapping between a

bank’s portfolio and the reference data

may change over time. For example,

relationships between internal grades

and external agency grades may change

during the economic cycle because of

differences in rating philosophy.

Similarly, distance-to-default measures

for obligors in a given grade may not be

constant over time. These likely changes

make it imperative that the bank update

all mappings regularly.

Sound validation practices may

include tests for internal consistency

such as ‘‘reverse mapping.’’ Using this

technique, a bank evaluates obligors

from the reference data set as if they

were subject to the bank’s rating system

(that is, part of the bank’s current

portfolio). The bank’s mapping is then

applied to these reverse-mapped

obligors to see whether the mapped

characterization of the reference obligor

is consistent with that of the initial

evaluation.5 Another valuable technique

is to apply different mapping methods

and compare the results. For example,

mappings based on financial ratio

comparisons can be rechecked using

mappings based on available external

ratings.

Example. A bank mapped its internal

grades to the rating scale of one public debt-

rating agency in 1992. Since then, the bank

has completed a major acquisition of another

large bank and significantly changed its

business mix in other ways. The bank

continues to use the same mapping, without

reassessing its validity. This practice is not

consistent with the standard.

Application

In the application stage, the bank

applies the PD estimation method to the

current portfolio of obligors using the

mapping process. It obtains final PD

estimates for each rating grade, which

will be used to calculate minimum

regulatory capital. To arrive at those

estimates, a bank may adjust the raw

results derived from the estimation

stage

not

consistent with the standard.

Application

In the application stage, the bank

applies the PD estimation method to the

current portfolio of obligors using the

mapping process. It obtains final PD

estimates for each rating grade, which

will be used to calculate minimum

regulatory capital. To arrive at those

estimates, a bank may adjust the raw

results derived from the estimation

stage. For example, it might aggregate

individual obligor default probabilities

to the rating grade level, or smooth

results because a rating grade’s PD

estimate was higher than a lower quality

grade. The bank must explain and

support all adjustments when

documenting its quantification process.

Example. A bank uses external data to

estimate long-run average PDs for each grade.

The resulting PD estimate for Grade 2 is

slightly higher than the estimate for Grade 3,

even though Grade 2 is supposedly of higher

credit quality. The bank uses statistics to

demonstrate that this anomaly occurred

because defaults are rare in the highest

quality rating grades. The bank judgmentally

adjusts the PD estimates for grades 2 and 3

to preserve the expected relationship

between obligor grade and PD, but requires

that total risk-weighted assets across both

grades using the adjusted PD estimates be no

less than total risk-weighted assets based on

the unadjusted estimates, using a typical

distribution of obligors across the two grades.

Such an adjustment during the application

stage is consistent with this guidance.

S. IRB institutions that aggregate the

default probabilities of individual

portfolio obligors when calculating PD

estimates for internal grades must have

a clear policy governing the aggregation

process.

As noted above, mapping may be

grade-based or obligor-based. Grade-

based mappings naturally provide a

single PD per grade, because the

estimated default model is applied to

the representative obligor for each

grade

gate the

default probabilities of individual

portfolio obligors when calculating PD

estimates for internal grades must have

a clear policy governing the aggregation

process.

As noted above, mapping may be

grade-based or obligor-based. Grade-

based mappings naturally provide a

single PD per grade, because the

estimated default model is applied to

the representative obligor for each

grade. In contrast, obligor-based

mappings must aggregate in some

manner the individual PD estimates to

the grade level. The expectation is that

the grade PD estimate will be calculated

as the mean. The bank will be allowed

to calculate this estimate differently

only if it can demonstrate that the

alternative method provides a better

estimate of the long-run average PD. To

obtain this evidence, the bank must at

least compare the results of both

methods.

S. IRB institutions that combine

estimates from multiple sets of reference

data must have a clear policy governing

the combination process, and must

examine the sensitivity of the results to

alternative combinations.

Because a bank should make use of as

much information as possible when

mapping, it will usually use multiple

data sets. The manner in which the data

or the estimates from those multiple

data sets are combined is extremely

important. A bank must document its

justification for the particular

combination methods selected. Those

methods must be subject to appropriate

approval and oversight.

The data may come from the same

basic data source but from different time

periods or from different data sources

altogether. For example, banks often

combine internal data with external

data, use external data from different

sample periods, or combine results from

corporate-bond default databases with

results from equity-based models of

obligor default. Different combinations

will produce different PD estimates. The

bank should investigate alternative

combinations and document the impact

on the estimates

ther. For example, banks often

combine internal data with external

data, use external data from different

sample periods, or combine results from

corporate-bond default databases with

results from equity-based models of

obligor default. Different combinations

will produce different PD estimates. The

bank should investigate alternative

combinations and document the impact

on the estimates. When ultimate results

are highly sensitive to how estimates

from different data sources are

combined, the bank must choose among

the alternatives conservatively.

C. Loss Given Default (LGD)

The LGD estimation process is similar

to the PD estimation process. The bank

identifies a reference data set of

defaulted credits and relevant

descriptive characteristics. Once the

bank obtains these data sets (with the

facility characteristics), it must select a

technique to estimate the economic loss

per dollar of exposure at default, for a

defaulted exposure with a given array of

characteristics. The bank’s portfolio

must then be mapped, so that the model

can be applied to generate an estimate

of LGD for each portfolio transaction or

severity grade.

Data

Unlike reference data sets used for PD

estimation, data sets for severity

estimation contain only exposures to

defaulting obligors. At least two broad

categories of data are necessary to

produce LGD estimates.

First, data must be available to

calculate the actual economic loss

experienced for each defaulted facility.

Such data may include the market value

of the facility at default, which can be

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obligors. At least two broad

categories of data are necessary to

produce LGD estimates.

First, data must be available to

calculate the actual economic loss

experienced for each defaulted facility.

Such data may include the market value

of the facility at default, which can be

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6 The appropriate discount rate for IRB purposes

may differ from the contract rate required under

FAS 114 for accounting purposes.

used to proxy a recovery rate.

Alternatively, economic loss may be

calculated using the exposure at the

time of default, loss of principal,

interest, and fees, the present value of

subsequent recoveries and related

expenses (or the costs as calculated

using an approved allocation method),

and the appropriate discount rate.

Second, factors must be available to

group the defaulted facilities in

meaningful ways. Characteristics that

are likely to be important in predicting

loss rates include whether or not the

facility is secured and the type and

coverage of collateral if the facility is

secured, seniority of the claim, general

economic conditions, and obligor’s

industry. Although these factors have

been found to be significant in existing

academic and industry studies, a bank’s

quantification of LGD certainly need not

be limited to these variables. For

example, a bank might expand its loss

severity research by examining many

other potential drivers of severity

(characteristics of an obligor that might

help the bank predict the severity of a

loss), including obligor size, line of

business, geographic location, facility

type, obligor ratings (internal or

external), historical internal severity

grade, or tenor of the relationship.

A bank must ensure that the reference

data remains applicable to its current

portfolio of facilities

vers of severity

(characteristics of an obligor that might

help the bank predict the severity of a

loss), including obligor size, line of

business, geographic location, facility

type, obligor ratings (internal or

external), historical internal severity

grade, or tenor of the relationship.

A bank must ensure that the reference

data remains applicable to its current

portfolio of facilities. It must implement

established processes to ensure that

reference data sets are updated when

new data become available. All data

sources, variables, and the overall

processes concerning data collection

and maintenance must be fully

documented, and that documentation

should be readily available for review.

S. The sample period for the reference

data must be at least seven years, and

must include periods of economic stress

during which defaults were relatively

high.

Seven years is the minimum sample

period for the LGD reference data. A

longer sample period is desirable,

because more default observations will

be available for analysis and may serve

to refine severity estimates. In any case,

a bank must select a sample period that

includes episodes of economic stress,

which are defined as periods with a

relatively high number of defaults.

Inclusion of stress periods increases the

size and potentially the breadth of the

reference data set. According to some

empirical studies, the average loss rate

is higher during periods of stress.

Example. A bank intends to rely primarily

on internal data when quantifying all

parameter estimates, including LGD. Its

internal data cover the period 1994 through

2000. The bank will continue to extend its

data set as time progresses. Its current policy

mandates that credits be resolved within two

years of default, and the data set contains the

most recent data available. Although the

current data set satisfies the seven-year

requirement, the bank is aware that it does

not include stress periods

LGD. Its

internal data cover the period 1994 through

2000. The bank will continue to extend its

data set as time progresses. Its current policy

mandates that credits be resolved within two

years of default, and the data set contains the

most recent data available. Although the

current data set satisfies the seven-year

requirement, the bank is aware that it does

not include stress periods. In comparing its

loss estimates with rates published in

external studies for similarly stratified data,

the bank observes that its estimates are

systematically lower. To be consistent with

the standard, the bank must take steps to

include stress periods in its estimates.

S. The definition of default within the

reference data must be reasonably

consistent with the IRB definition of

default.

This standard parallels a similar

standard in the section on PD. The

following examples illustrate how it

applies in the case of LGD.

Example 1. For LGD estimation, a bank

includes in its default data base only

defaulted facilities that actually experience a

loss, and excludes credits for which no loss

was recorded because liquidated collateral

covered the loss (effectively applying a ‘‘loss

given loss’’ concept). This practice is not

consistent with the standard because the

bank’s default definition for LGD is narrower

than the IRB definition.

Example 2. A bank relies on external data

sources to estimate LGD because it lacks

sufficient internal data. One source uses

‘‘bankruptcy filing’’ to indicate default while

another uses ‘‘missed principal or interest

payment,’’ and the two sources result in

significantly different loss estimates for the

severity grades defined by the bank. The

bank’s practice is not consistent with the

standard, and the bank should determine

whether the definitions used in the reference

data sets differ substantially from the IRB

definition. If so, and the differences are

difficult to quantify, the bank should seek

other sources of reference data

s result in

significantly different loss estimates for the

severity grades defined by the bank. The

bank’s practice is not consistent with the

standard, and the bank should determine

whether the definitions used in the reference

data sets differ substantially from the IRB

definition. If so, and the differences are

difficult to quantify, the bank should seek

other sources of reference data. For more

minor differences, the bank may be able to

make appropriate adjustments during the

estimation stage.

Estimation

Estimation of LGD is the process by

which characteristics of the reference

data are related to loss severity. The

relevant characteristics that help

explain how severe losses tend to be

upon default might include variables

such as seniority, collateral, facility

type, or business line.

S. The estimates of loss severity must

be empirically based and must reflect

the concept of ‘‘economic loss.’’

Loss severity is defined as economic

loss, which is different from accounting

measures of loss. Economic loss

captures the value of recoveries and

direct and indirect costs discounted to

the time of default, and it should be

measured for each defaulted facility.

The scope of the cash flows included in

recoveries and costs is meant to be

broad. Workout costs that can be clearly

attributed to certain facilities or types of

facilities must be reflected in the bank’s

LGD assignments for those exposures.

When such allocation is not practical,

the bank may assign those costs using

factors based on broad averages.

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

an be clearly

attributed to certain facilities or types of

facilities must be reflected in the bank’s

LGD assignments for those exposures.

When such allocation is not practical,

the bank may assign those costs using

factors based on broad averages.

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 must be no less than the

contract interest rate on new

originations of a type similar to the

transaction in question, for the lowest-

quality grade in which a bank originates

such transactions.6 Where possible, the

rate should reflect the fixed rate on

newly originated exposures with term

corresponding to the average resolution

period of defaulting assets.

Ideally, severity should be measured

once all recoveries and costs have been

realized. However, a bank may not

resolve a defaulted obligation for many

years following default. For practical

purposes, banks may choose to close the

period of observation before this final

resolution occurs—that is, at a point in

time when most costs have been

incurred and when recoveries are

substantially complete. Banks that do so

should estimate the additional costs and

recoveries that would likely occur

beyond this period and include them in

the LGD estimates. A bank must

document its choice of the period of

observation, and how it estimated

additional costs and recoveries beyond

this period.

LGD for each type of exposure must

be the loss per default (expressed as a

percentage of exposure at default)

expected during periods when default

rates are relatively high. This expected

loss rate is referred to as ‘‘stress-

condition LGD.’’ For cases in which loss

severities do not have a material degree

of cyclical variability, use of the long-

run default-weighted average is

appropriate, although stress-condition

LGD generally exceeds this average.

The drivers of severity can be linked

to loss estimates in a number of ways

efault

rates are relatively high. This expected

loss rate is referred to as ‘‘stress-

condition LGD.’’ For cases in which loss

severities do not have a material degree

of cyclical variability, use of the long-

run default-weighted average is

appropriate, although stress-condition

LGD generally exceeds this average.

The drivers of severity can be linked

to loss estimates in a number of ways.

One approach is to segment the

reference defaults into groups that do

not overlap. For example, defaults could

be grouped by business line,

predominant collateral type, and loan-

to-value coverage. The LGD estimate for

each category is the mean loss

calculated over the category’s defaulted

facilities. Loss must be calculated as the

default-weighted average (where

individual defaults receive equal

weight) rather than the average of

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annual loss rates, and must be based on

results from periods during which

default rates were relatively numerous if

loss rates are materially cyclical.

Banks can also draw estimates of LGD

from a statistical model. For example,

they can build a regression model of

severity using data on loss severity and

some quantitative measures of the loss

drivers. Any model must meet the

requirements for model validation

discussed in Chapter 1. Other methods

for computing LGD could also be

appropriate.

Example 1. A bank has internal data on

defaulted facilities, including information on

business line, facility type, seniority, and

predominant collateral type (if the facility is

secured). The data allow for a reasonable

calculation of economic loss. The data span

eight years and include three years that can

be termed high-default years

methods

for computing LGD could also be

appropriate.

Example 1. A bank has internal data on

defaulted facilities, including information on

business line, facility type, seniority, and

predominant collateral type (if the facility is

secured). The data allow for a reasonable

calculation of economic loss. The data span

eight years and include three years that can

be termed high-default years. After analyzing

the economic cycle using internal and

external data, the bank concludes that the

data show no evidence of material cyclical

variability in loss severities, and that the

default data span enough experience to allow

estimation of a long-run average. On the basis

of preliminary analysis, the bank determines

that the drivers of loss severity for large

corporate facilities are similar to those for

middle-market loans, and that the two groups

can be estimated as a pool. Again on the basis

of preliminary analysis, the bank segments

this pool by seniority and by six collateral

groupings, including unsecured. These

groupings contain enough defaults to allow

reasonably precise estimates. The loss

severity estimates are then calculated by

averaging loss rates within each segment.

This practice is consistent with the standard.

Example 2. A bank uses internal data in

which information on security and seniority

is lacking. The bank groups corporate and

middle-market defaulted facilities into a

single pool and calculates the LGD estimate

as the mean loss rate. No adjustments for the

lack of data are made in the estimation or

application steps. This practice is

unacceptable because there is ample external

evidence that security and seniority matter in

these segments. A bank with such limited

internal default data must incorporate

external or pooled data into the estimation.

Example 3. A bank determines that a

business unit—for example, a unit dedicated

to a particular type of asset-based lending—

forms a homogeneous pool for the purposes

of estimating loss severity

se there is ample external

evidence that security and seniority matter in

these segments. A bank with such limited

internal default data must incorporate

external or pooled data into the estimation.

Example 3. A bank determines that a

business unit—for example, a unit dedicated

to a particular type of asset-based lending—

forms a homogeneous pool for the purposes

of estimating loss severity. That is, although

the facilities 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 this

pooling through analysis of lending practices

and available internal and external data. In

this example, the mean of a single segment

is consistent with the standard.

S. Judgmental adjustments may play

an appropriate role in LGD estimation,

but must not be biased toward lower

estimates.

It is difficult to make general

statements about good and bad practices

in this area, because adjustments can

take many different forms. The

following examples illustrate how

supervisors would be likely to evaluate

particular adjustments observed in

practice.

Example 1. A bank divides observed

defaults into segments according to collateral

type. One of the segments has too few

observations to produce a reliable estimate.

Relying on external data and judgment, the

bank determines that the segment’s estimated

severity of loss falls somewhere between the

estimates for two other categories. This

segment’s severity is set judgmentally to be

the mean of the estimates for the other

segments. This practice is consistent with the

standard.

Example 2. A bank does not know when

recoveries (and related costs) occurred in a

portfolio segment; therefore, it cannot

properly discount the segment’s cash flows.

However, the bank has sufficient internal

data to calculate economic loss for defaulted

facilities in another portfolio segment

the mean of the estimates for the other

segments. This practice is consistent with the

standard.

Example 2. A bank does not know when

recoveries (and related costs) occurred in a

portfolio segment; therefore, it cannot

properly discount the segment’s cash flows.

However, the bank has sufficient internal

data to calculate economic loss for defaulted

facilit

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