Rating Models and Validation - Oesterreichische Nationalbank

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Rating Models and Validation This categorization will depend heavily on the organizational structure of each bank. Product catalogs frequently list various combinations of the categories mentioned above. 7.3.5 Linking of Collateral Types and Customer Types In the table below, typical collateral types are mapped to individual customer groups. Chart 89: Typical Collateral for Various Customer Types 150 Guidelines on Credit Risk Management
Implementing an in-house LGD estimation procedure should begin with an analysis of the collateral/customer combinations which are most significant for the individual bank. First of all, it is necessary to ensure that the basic prerequisite of a sufficient quantitative and qualitative data set is fulfilled. Depending on the type and extent of data requirements, collateral can be further subdivided according to its value content, the materiality of individual collateral types, as well as the complexity of collateral agreements. It may also be useful to define categories based on transaction types. On the basis of these preliminary tasks, it is then possible to select the appropriate LGD estimation method specifically for each loss parameter. These methods are presented in the next section. 7.4 Methods of Estimating LGD Parameters In this section, we give a general presentation of the procedures currently being discussed and/or used in LGD estimation. Section 7.5 then gives specific applied examples of how the loss components are estimated in practice. In general, we can differentiate top-down and bottom-up approaches to LGD estimation. 7.4.1 Top-Down Approaches Top-down approaches use freely available market data to derive LGD by cleansing or breaking down the complex external information available, such as recovery rates or expected loss. This can be done in two ways: — Using explicit loss data — Using implicit loss data For the sake of completeness, it is worth noting here that the draft EU directive mentions another method in addition to these two methods of calculating LGD from external data. For purchased corporate receivables and in the retail segment, LGD can also be estimated on the basis of internally available loss data (expected loss) if suitable estimates of default probability are possible. Explicit Loss Data There are two possible ways to use explicit loss data. The first possibility is to use historical loss information (such as recovery rates for bonds) provided by specialized agencies. The recovery rate corresponds to the insolvency payout and indicates the amount reimbursed in bankruptcy proceedings as a percentage of the nominal amount receivable. LGD can then be calculated using the following formula: LGD ¼ 1 Recovery Rate Historical recovery rates are currently available in large quantities, predominantly for US bonds and corporate loans. In addition, loss data are also available on banks and governments. Even if the definitions of loss and default are consistent, these data probably only apply to borrowers from Austrian banks to a limited extent. For example, aspects such as collateralization, bankruptcy procedures, balance sheet structures, etc. are not always comparable. Therefore, historical recovery rates from the capital market are generally only suitable for unsecured transactions with governments, international financial service providers and large international companies. Rating Models and Validation Guidelines on Credit Risk Management 151
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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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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 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: transaction, however, it is also ne
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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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