Rating Models and Validation - Oesterreichische Nationalbank

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Rating Models and Validation accounting, it is possible to calculate costs down to the level of individual transactions. In this way, for example, the costs of collateral realization can be assigned to individual transactions by way of the transaction-specific dedication of collateral. The costs of realizing customer-specific collateral can be allocated to the customerÕs transactions arithmetically or using a specific allocation key. Retail Information Carriers Unless the loss components in retail transactions are depicted as they are in the non-retail segment, the collective banking book account for the pool to which the transactions are assigned can serve as the main source of information. The segmentation of the pool can go beyond the minimum segmentation required by the draft EU directive and provide for a more detailed classification according to various criteria, including additional product types, main collateral types, degrees of collateralization or other criteria. For each of the loss parameters listed below, it is advisable to define a separate collective account for each pool. Chart 86: Assignment of Information Carriers to Loss Parameters (Retail) 7.3.2 Customer Types As customer types are required in order to calculate losses due to bankruptcy or composition proceedings, it is advisable to define further subdivisions for these types because the nature and scope of bankruptcy proceedings depend heavily on the customer type. The segmentation shown below is essentially based on the customer segmentation described for PD estimation earlier. On the basis of this segmentation, it is already possible to derive the most essential information for further estimates of bankruptcy proceeds. In the case of corporate customers, the industry in which they operate provides important additional information with which the bank can estimate the value content and liquidity of assets, for example. Beyond the information mentioned above, lenders can only gain additional insight through a more detailed analysis of defaulted customers similar to the analysis performed in order to evaluate a company when determining its liquidation value. 146 Guidelines on Credit Risk Management
Chart 87: Overview of Customer Types Rating Models and Validation Guidelines on Credit Risk Management 147
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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
Page 95 and 96: Chart 46: Aspects of Rating Model V
Page 97 and 98: — Model development procedure Mod
Page 99 and 100: and bad refer to whether a credit d
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
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: — Collateral: Collateral value, c
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
Page 155 and 156: those assets which serve as collate
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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Rating Models and Validation - Oesterreichische Nationalbank

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