Rating Models and Validation - Oesterreichische Nationalbank

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Rating Models and Validation Loss Parameter Information Carrier Book value – Realized recovery rate Book value – Recovery rate for collateral realization Book value – Partial write-off (restructuring) Method of direct/ simplified estimation Customer Estimation using loss data Collateral Estimation using loss data Customer Estimation using loss data Interest loss Transaction Calculation of lost interest Workout costs Customer/ transaction Expert estimation, cost and activity accounting Components of Loss Parameters Liquidation value of the enterprise at default Chart 90: Loss Parameters and Estimation Methods Method of indirect/detailed estimation Calculation of liquidation value by company valuation based on net value of tangible assets Bankruptcy costs Expert estimation Markdown on Expert estimation market price due to forced sale Value of collateral at time of default Estimation using loss data, cash flow model, realization-based expert valuation of collateral Liquidation period Expert estimation, estimation using loss data Realization costs Expert estimation, estimation using loss data Markdown on market price due to forced sale Refinancing costs until realization Expert estimation, estimation using loss data Contribution margin analysis, financial calculations Lost interest Contribution margin analysis, financial calculations Opportunity costs Contribution margin analysis, financial calculation Direct write-off/ easy restructuring Medium restructuring/liquidation Difficult restructuring/liquidation Expert estimation, cost and activity accounting Expert estimation, cost and activity accounting Expert estimation, cost and activity accounting LGD calculation is based on estimates of individual loss parameters. In this process, it is necessary to differentiate at least by individual customer type, collateral type, and transaction type according to the given level of materiality. If the existing loss history does not allow the direct estimation of parameters, it is possible to estimate individual loss parameters on the basis of their individual components (see table above). For each loss parameter, we explain the application of these methods in greater detail below. Book Value Loss Book value losses can arise in the course of restructuring due to a partial writeoff or in the course of liquidation. The liquidation itself can be based on bankruptcy realization or the realization of collateral. In the case of realization in bankruptcy/composition proceedings, historical default data are used for the purpose of direct estimation. In order to reduce the margin of fluctuation around the average recovery rate, it is advisable to perform segmentation by individual customer type. In the case of corporate customers, additional segmentation by industry may be helpful in order to account for their typical assets structure and thus also their probable recovery rates. As 154 Guidelines on Credit Risk Management
those assets which serve as collateral for loans from third parties are isolated from the bankruptcy estate, the recovery rate will be reduced accordingly for the unsecured portion of the loan. For this reason, it is also advisable to further differentiate customers by their degree of collateralization from balance sheet assets. 120 If possible, segments should be defined on the basis of statistical analyses which evaluate the discriminatory power of each segmentation criterion on the basis of value distribution. If a meaningful statistical analysis is not feasible, the segmentation criteria can be selected on the basis of expert decisions. These selections are to be justified accordingly. If historical default data do not allow direct estimates on the basis of segmentation, the recovery rate should be excluded from use at least for those cases in which collateral realization accounts for a major portion of the recovery rate. If the top-down approach is also not suitable for large unsecured exposures, it may be worth considering calculating the recovery rate using an alternative business valuation method based on the net value of tangible assets. Appropriately conservative estimates of asset values as well as the costs of bankruptcy proceedings and discounts for the sale of assets should be based on suitable documentation. When estimating LGD for a partial write-off in connection with loan restructuring, it is advisable to filter out those cases in which such a procedure occurs and is materially relevant. If the available historical data do not allow reliable estimates, the same book value loss as in the bankruptcy proceedings should be applied to the unsecured portion of the exposure. In the case of collateral realization, historical default data are used for the purpose of direct estimation. Again, it is advisable to perform segmentation by collateral type in order to reduce the margin of fluctuation around the average recovery rate (see section 7.3.3). In the case of personal collateral, the payment of the secured amount depends on the creditworthiness of the collateral provider at the time of realization. This information is implicit in historical recovery rates. In order to differentiate more precisely in this context, it is possible to perform segmentation based on the ratings of collateral providers. In the case of guarantees and credit derivatives, the realization period is theoretically short, as most contracts call for payment at first request. In practice, however, realization on the basis of guarantees sometimes takes longer because guarantors do not always meet payment obligations immediately upon request. For this reason, it may be appropriate to differentiate between institutional and other guarantors on the basis of the bankÕs individual experience. In the case of securities and positions in foreign currencies, potential value fluctuations due to market developments or the liquidity of the respective market are implicitly contained in the recovery rates. In order to differentiate more 120 For example, this can be done by classifying customers and products in predominantly secured and predominantly unsecured product/customer combinations. In non-retail segments, unsecured transactions tend to be more common in the case of governments, financial service providers and large capital market-oriented companies. The companyÕs revenues, for example, might also serve as an alternative differentiating criterion. In the retail segment, for example, unsecured transactions are prevalent in standardized business, in particular products such as credit cards and current account overdraft facilities. In such cases, it is possible to estimate book value loss using the retail pooling approach. Rating Models and Validation Guidelines on Credit Risk Management 155
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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
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
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
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: nents; this is analogous to the use
Page 157 and 158: during the realization period. The
Page 159 and 160: Chart 91: Example of Segmentation f
Page 161 and 162: ity of the realization market and t
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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