Rating Models and Validation - Oesterreichische Nationalbank

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Rating Models and Validation include out-of-sample validation, it is a statistically valid instrument which enables the optimal use of the information contained in the data without neglecting the need to validate the model with unknown data. In this context, however, it is necessary to note that excessively large fluctuations — especially changes in the sign of coefficients and inversions of high and low coefficients in the test scoring functions — indicate that the sample used is too small for statistical rating development. In such cases, highly inhomogeneous data quantities are generated in the repeated random division of the overall sample into analysis and validation samples, which means that a valid model cannot be developed on the basis of the given sample. 5.2 Developing the Scoring Function Chart 36: Scoring Function Development Procedure 74 Guidelines on Credit Risk Management
Once a quality-assured data set has been generated and the analysis and validation samples have been defined, the actual development of the scoring function can begin. In this document, a Òscoring functionÓ refers to the core calculation component in the rating model. No distinction is drawn in this context between rating and scoring models for credit assessment (cf. introduction to chapter 3). The development of the scoring function is generally divided into three stages, which in turn are divided into individual steps. In this context, we explain the fundamental procedure on the basis of an architecture which includes partial scoring functions for quantitative as well as qualitative data (see chart 36). The individual steps leading to the overall scoring function are very similar for quantitative and qualitative data. For this reason, detailed descriptions of the individual steps are based only on quantitative data in order to avoid repetition. The procedure is described for qualitative data only in the case of special characteristics. 5.2.1 Univariate Analyses The purpose of univariate analyses is to identify creditworthiness characteristics which make sense in the business context, can be surveyed with some ease, and show high discriminatory power for the purpose of developing the scoring function. The result of these analyses is a shortlist of fundamentally suitable creditworthiness characteristics. Preselecting creditworthiness characteristics reduces the complexity of the ensuing multivariate analyses, thus facilitating the process substantially. The steps shown in chart 36 are described in detail below for quantitative data (e.g. from balance sheet analysis). Developing a Catalog of Indicators The first step is to develop a comprehensive catalog of indicators on the basis of the quantitative data from the data collection process. This catalog should include indicators from all business-related information categories which can support the assessment of a borrowerÕs situation in terms of assets, finances, and income. These information categories determine the structure of the catalog of indicators. The indicators defined for each information category should ensure that a comprehensive assessment of the area is possible and that different aspects of each area are covered. For the purpose of assessing individual aspects, indicators can be included in different variants which may prove to be more or less suitable in the univariate and multivariate analyses. For this reason, a very large number of indicators — and in some cases very similar indicators — are defined in order to enable the best variants to be selected later in the process. Another condition which indicators have to fulfill is that it must be possible to calculate them for all of the cases included in a segment. This deserves special attention in cases where a rating segment contains companies to which simplified accounting standards apply and for which not all balance sheet items are available. In practice, indicators are usually defined in two steps: Rating Models and Validation Guidelines on Credit Risk Management 75
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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
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
Page 57 and 58: 4.1.5 Consistency Heuristic models
Page 59 and 60: Compared to heuristic models, stati
Page 61 and 62: the data set and statistical testin
Page 63 and 64: data collection procedure. This pro
Page 65 and 66: full surveys is often too high, esp
Page 67 and 68: essential structural characteristic
Page 69 and 70: For each block, interim objectives
Page 71 and 72: Chart 34: Creating the Analysis Dat
Page 73: Chart 35: Creating the Analysis and
Page 77 and 78: an indicator should only be used in
Page 79 and 80: Transformation of Indicators In ord
Page 81 and 82: the scoring functions developed usi
Page 83 and 84: Chart 37: Significance of Quantitat
Page 85 and 86: — For all other statistical and h
Page 87 and 88: of approximately 10 intervals shoul
Page 89 and 90: With regard to the time interval be
Page 91 and 92: around the main diagonal, however,
Page 93 and 94: tial increase in marginal default r
Page 95 and 96: Chart 46: Aspects of Rating Model V
Page 97 and 98: — Model development procedure Mod
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Page 101 and 102: numbers of cases per class observed
Page 103 and 104: Chart 54: Depiction of a and b erro
Page 105 and 106: 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
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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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