Rating Models and Validation - Oesterreichische Nationalbank

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Contents 8 Estimating Exposure at Default (EAD) 162 8.1 Transaction Types 162 8.2 Customer Types 163 8.3 EAD Estimation Methods 165 IV REFERENCES 167 V FURTHER READING 170 6 Guidelines on Credit Risk Management
Rating Models and Validation I INTRODUCTION The OeNB Guideline on Rating Models and Validation was created within a series of publications produced jointly by the Austrian Financial Markets Authority and the Oesterreichische Nationalbank on the topic of credit risk identification and analysis. This set of guidelines was created in response to two important developments: First, banks are becoming increasingly interested in the continued development and improvement of their risk measurement methods and procedures. Second, the Basel Committee on Banking Supervision as well as the European Commission have devised regulatory standards under the heading ÒBasel IIÓ for banksÕ in-house estimation of the loss parameters probability of default (PD), loss given default (LGD), and exposure at default (EAD). Once implemented appropriately, these new regulatory standards should enable banks to use IRB approaches to calculate their regulatory capital requirements, presumably from the end of 2006 onward. Therefore, these guidelines are intended not only for credit institutions which plan to use an IRB approach but also for all banks which aim to use their own PD, LGD, and/or EAD estimates in order to improve assessments of their risk situation. The objective of this document is to assist banks in developing their own estimation procedures by providing an overview of current best-practice approaches in the field. In particular, the guidelines provide answers to the following questions: — Which segments (business areas/customers) should be defined? — Which input parameters/data are required to estimate these parameters in a given segment? — Which models/methods are best suited to a given segment? — Which procedures should be applied in order to validate and calibrate models? In part II, we present the special requirements involved in PD estimation procedures. First, we discuss the customer segments relevant to credit assessment in chapter 1. On this basis, chapter 2 covers the resulting data requirements for credit assessment. Chapter 3 then briefly presents credit assessment models which are commonly used in the market. In Chapter 4, we evaluate these models in terms of their suitability for the segments identified in chapter 1. Chapter 5 discusses how rating models are developed, and part II concludes with chapter 6, which presents information relevant to validating estimation procedures. Part III provides a supplement to Part II by presenting the specific requirements for estimating LGD (chapter 7) and EAD (chapter 8). Additional literature and references are provided at the end of the document. Finally, we would like to point out that these guidelines are only intended to be descriptive and informative in nature. They cannot (and are not meant to) make any statements on the regulatory requirements imposed on credit institutions dealing with rating models and their validation, nor are they meant to prejudice the regulatory activities of the competent authorities. References to the draft EU directive on regulatory capital requirements are based on the latest version available when these guidelines were written (i.e. the draft released on July 1, 2003) and are intended for information purposes only. Although this document has been prepared with the utmost care, the publishers cannot assume any responsibility or liability for its content. Guidelines on Credit Risk Management 7
Page 1 and 2: ≈√ Guidelines on Credit Risk Ma
Page 3 and 4: The ongoing development of contempo
Page 5: 5 Developing a Rating Model 60 5.1
Page 9 and 10: This segmentation from the business
Page 11 and 12: The best-practice segmentation pres
Page 13 and 14: Chart 2: Data Requirements for Gove
Page 15 and 16: 2.2 Financial Service Providers In
Page 17 and 18: Insurance Companies Due to their di
Page 19 and 20: Capital Market-Oriented/Internation
Page 21 and 22: — Market prospects are not assess
Page 23 and 24: Chart 5: Data Requirements for Corp
Page 25 and 26: elationships in the project, these
Page 27 and 28: Before the Project As the repayment
Page 29 and 30: Chart 6: Data Requirements for Reta
Page 31 and 32: During the Credit Term Instead of o
Page 33 and 34: 3.1 Heuristic Models Heuristic mode
Page 35 and 36: Chart 9: Information Categories for
Page 37 and 38: Explanatory Component The explanato
Page 39 and 40: This example defines linguistic ter
Page 41 and 42: ments as to whether higher or lower
Page 43 and 44: Chart 16: Indicators in the ÒCrebo
Page 45 and 46: Logistic regression has a number of
Page 47 and 48: adapts the network according to any
Page 49 and 50: The parameters required to calculat
Page 51 and 52: ture of the borrowerÕs creditworth
Page 53 and 54: Chart 24: Vertical Linking of Ratin
Page 55 and 56: e necessary in this case if the def
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4.1.5 Consistency Heuristic models
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Compared to heuristic models, stati
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the data set and statistical testin
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data collection procedure. This pro
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full surveys is often too high, esp
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essential structural characteristic
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For each block, interim objectives
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Chart 34: Creating the Analysis Dat
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Chart 35: Creating the Analysis and
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Once a quality-assured data set has
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an indicator should only be used in
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Transformation of Indicators In ord
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the scoring functions developed usi
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Chart 37: Significance of Quantitat
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— For all other statistical and h
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of approximately 10 intervals shoul
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With regard to the time interval be
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around the main diagonal, however,
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tial increase in marginal default r
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Chart 46: Aspects of Rating Model V
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— Model development procedure Mod
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and bad refer to whether a credit d
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numbers of cases per class observed
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Chart 54: Depiction of a and b erro
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Chart 57: Shape of the a—b Error
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Interpretation of the Pietra Index
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The following relation applies to t
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Chart 62: ROC Curve with Simultaneo
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Chart 65: Interpretation of the Bay
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The table below (chart 67) shows a
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The lower the Brier Score is, the b
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Chart 70 shows the reliability diag
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Chart 71: Identification of Signifi
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estimates, which means that it is e
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transition matrix, the data require
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tency of the cumulative and conditi
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to internal back-testing results in
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Changes in correlations One of the
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6.4.3 Developing Stress Tests This
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Counterparty-based and credit facil
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tify decisive factors influencing i
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III ESTIMATING AND VALIDATING LGD/E
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Chart 81: Loss Components in LGD to
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assets, as the danger exists that m
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— Collateral: Collateral value, c
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Chart 87: Overview of Customer Type
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transaction, however, it is also ne
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Implementing an in-house LGD estima
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nents; this is analogous to the use
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those assets which serve as collate
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during the realization period. The
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Chart 91: Example of Segmentation f
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ity of the realization market and t
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The level of utilization for off-ba
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8.3 EAD Estimation Methods As in th
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IV REFERENCES Backhaus, Klaus/Erich
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Stuhlinger, Matthias, Rolle von Rat
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Pytlik, Martin, Diskriminanzanalyse
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Rating Models and Validation - Oesterreichische Nationalbank

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