# Internal Ratings-Based Systems for Corporate Credit and Operational Risk Advanced Measurement Approaches for Regulatory Capital > Briefs, arguments, decisions, and more. URL: https://www.frixlaw.com/law-library/documents/fr%3A03-18976 ## Record - **Collection:** Federal Register - **Document type:** Notice - **Published:** August 4, 2003 - **Citation:** 68 FR 45949 ## Text 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. 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. 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. 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. 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 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. 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. 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. 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. 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, • 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. 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. 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. 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. 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. 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 • 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. 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. 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. 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. 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 facilities 1 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. 1 Facilities—loans, lines, or other separate extensions of credit to an obligor. 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. 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. 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. 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. 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. 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. 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. 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 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. • 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. 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. 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. 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. 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. 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. 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. 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. 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 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. 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. 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. 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 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. 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 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. 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. 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. 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. 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 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. • 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. 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. 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. 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. 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 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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 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. 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. 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. 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. 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. 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. 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. Grade mapping: Characteristics of the obligors within an internal grade are 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. 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. 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. 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. 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. 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 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. 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. 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. 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. 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. 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. 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 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. 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. 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. 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. 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. 6 The appropriate discount rate for IRB purposes may differ from the contract rate required under FAS 114 for accounting purposes. 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. 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 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. 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. 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 [Text truncated at 120,000 characters. The full text is on the page linked above.] --- Source: Frix Law Library, https://www.frixlaw.com/law-library/documents/fr%3A03-18976. Public record. Not legal advice.