Rating Models and Validation - Oesterreichische Nationalbank

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Rating Models and Validation Reporting and Countermeasures Once the stress tests have been carried out, it is necessary to report the results to the relevant levels of management. In this context, it is crucial to present only those results which are truly decisive. Such reports should cover the results of routine stress tests as well as those of new tests based specifically on the prevailing economic situation. The targeted selection of decisive results will also facilitate the process of developing countermeasures. In order to derive countermeasures from stress tests, the tests have to be designed in such a way that they enable causal analysis. Adaptation and Ongoing Development of Stress Tests As the portfolio composition as well as economic and political conditions change constantly, it is also necessary to adapt stress tests on an ongoing basis. This point is decisive in ensuring plausible results from which suitable countermeasures can be derived. 138 Guidelines on Credit Risk Management
III ESTIMATING AND VALIDATING LGD/EAD AS RISK COMPONENTS 7 Estimating Loss Given Default (LGD) While it is common practice in many banks to calculate PDs without using the results to calculate Basel II regulatory capital requirements, these institutions are now also focusing on estimating LGD and EAD due to the requirements of Basel II. It is not possible to discuss the estimation of these two parameters without referring to Basel II, as no independent concepts have been developed in this field to date. Therefore, institutions which plan to implement their own LGD estimation procedures in compliance with the draft EU directive face a number of special challenges. First, in contrast to PD estimates, LGD estimation procedures cannot rely on years of practical experience or established industry standards. Second, many institutions do not have comprehensive loss databases at their disposal. This chapter presents potential solutions for banksÕ in-house estimation of LGD as well as the current state of development in this area. We cannot claim that this chapter presents a conclusive discussion of LGD estimation or that the procedure presented is suitable for all conceivable portfolios. Instead, the objective of this chapter is to encourage banks to pursue their own approaches to improving LGD estimates. The LGD estimation procedure is illustrated in chart 80 below. Chart 80: LGD Estimation Procedure In this chapter, we derive the loss parameters on the basis of the definitions of default and loss presented in the draft EU directive and then link them to the main segmentation variables identified: customers, transactions, and collateral. We then discuss procedures which are suitable for LGD estimation. Finally, we Rating Models and Validation Guidelines on Credit Risk Management 139
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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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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
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Page 125 and 126: transition matrix, the data require
Page 127 and 128: tency of the cumulative and conditi
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
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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
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Page 157 and 158: during the realization period. The
Page 159 and 160: Chart 91: Example of Segmentation f
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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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