Rating Models and Validation - Oesterreichische Nationalbank

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Rating Models and Validation pretation of results deserves special attention. In the course of development, the bank can use a data pool in order to provide a broader data set (cf. section 5.1.2). In the short term, estimated values can be adjusted conservatively to compensate for a high degree of variance. In the medium and long term, however, it is advisable to generate a comprehensive and quality-assured historical data set. These data provide an important basis for future validation and back-testing activities, as well as enabling future changes in estimation methodology. Moreover, Basel II and the draft EU directive require the creation of loss histories, even for the IRB Foundation Approach. 121 When selecting methods, the bank can take the materiality of each loss component into account with regard to the effort and precision involved in each method. Based on a bankÕs individual requirements, it may be appropriate to implement specific LGD estimation tools for certain customer and transaction segments. For this purpose, individual combinations of loss parameters and information carriers can be aggregated to create a business segment-specific LGD tool using various estimation methods. This tool should reflect the significance of individual loss components. Throughout the development stage, it is also important to bear validation requirements in mind as an ancillary condition. In the sections that follow, we present an example of how to implement estimation methods for each of the loss parameters: book value loss, interest loss, and workout costs. Estimating Book Value Loss (Example: Recovery Rates for Physical Collateral) In the course of initial practical implementations at various institutions, segmentation has emerged as the best-practice approach with regard to implementability, especially for the recovery rates of physical collateral. In this section, we briefly present a segmentation approach based on Chart 91 below. It is first necessary to gather recovery rates for all realized collateral over as long a time series as possible. These percentages are placed on one axis ranging from 0% to the highest observed recovery rate. In order to differentiate recovery rates more precisely, it is then possible to segment them according to various criteria. These criteria can be selected either by statistical means using discriminatory power tests or on the basis of expert estimates and conjectured relationships. 121 Cf. EUROPEAN COMMISSION, draft directive on regulatory capital requirements, Annex D-5, No. 33. 158 Guidelines on Credit Risk Management
Chart 91: Example of Segmentation for Estimating LGD The diagram below shows an example of the distribution of historical recovery rates from the realization of real estate collateral based on the type of real estate: Chart 92: Example of Recovery Rates for Default by Customer Group Even at first glance, the distribution in the example above clearly reveals that the segmentation criterion is suitable due to its discriminatory power. Statistical tests (e.g. Kolmogorov-Smirnov Test, U-Test) can be applied in order to analyze the discriminatory power of possible segmentation criteria even in cases where their suitability is not immediately visible. Rating Models and Validation Guidelines on Credit Risk Management 159
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≈√ Guidelines on Credit Risk Ma
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The ongoing development of contempo
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5 Developing a Rating Model 60 5.1
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Rating Models and Validation I INTR
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This segmentation from the business
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The best-practice segmentation pres
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Chart 2: Data Requirements for Gove
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2.2 Financial Service Providers In
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Insurance Companies Due to their di
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Capital Market-Oriented/Internation
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— Market prospects are not assess
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Chart 5: Data Requirements for Corp
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elationships in the project, these
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Before the Project As the repayment
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Chart 6: Data Requirements for Reta
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During the Credit Term Instead of o
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3.1 Heuristic Models Heuristic mode
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Chart 9: Information Categories for
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Explanatory Component The explanato
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This example defines linguistic ter
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ments as to whether higher or lower
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Chart 16: Indicators in the ÒCrebo
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Logistic regression has a number of
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adapts the network according to any
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The parameters required to calculat
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ture of the borrowerÕs creditworth
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Chart 24: Vertical Linking of Ratin
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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
Page 107 and 108: Interpretation of the Pietra Index
Page 109 and 110: The following relation applies to t
Page 111 and 112: Chart 62: ROC Curve with Simultaneo
Page 113 and 114: Chart 65: Interpretation of the Bay
Page 115 and 116: The table below (chart 67) shows a
Page 117 and 118: The lower the Brier Score is, the b
Page 119 and 120: Chart 70 shows the reliability diag
Page 121 and 122: Chart 71: Identification of Signifi
Page 123 and 124: estimates, which means that it is e
Page 125 and 126: transition matrix, the data require
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Page 129 and 130: to internal back-testing results in
Page 131 and 132: Changes in correlations One of the
Page 133 and 134: 6.4.3 Developing Stress Tests This
Page 135 and 136: Counterparty-based and credit facil
Page 137 and 138: tify decisive factors influencing i
Page 139 and 140: III ESTIMATING AND VALIDATING LGD/E
Page 141 and 142: Chart 81: Loss Components in LGD to
Page 143 and 144: assets, as the danger exists that m
Page 145 and 146: — Collateral: Collateral value, c
Page 147 and 148: Chart 87: Overview of Customer Type
Page 149 and 150: transaction, however, it is also ne
Page 151 and 152: Implementing an in-house LGD estima
Page 153 and 154: nents; this is analogous to the use
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Page 157: during the realization period. The
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Page 163 and 164: The level of utilization for off-ba
Page 165 and 166: 8.3 EAD Estimation Methods As in th
Page 167 and 168: IV REFERENCES Backhaus, Klaus/Erich
Page 169 and 170: Stuhlinger, Matthias, Rolle von Rat
Page 171 and 172: Pytlik, Martin, Diskriminanzanalyse
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Rating Models and Validation - Oesterreichische Nationalbank

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