Rating Models and Validation - Oesterreichische Nationalbank

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Rating Models and Validation It is possible to specify segments even further using additional criteria (e.g. liquidity of realization markets and liquidation period). Such specification does not necessarily make sense for every segment. When selecting criteria, it is important to ensure that a sufficiently large group can be assigned to each segment. At the same time, the criteria should not overlap excessively in terms of information content. For example, the property type in real estate constitutes a complex data element which contains implicit information on the relevant realization market, its liquidity, etc. Additional subdivisions can serve to enhance the information value, although the absolute information gain tends to drop as the fineness of the categorization increases. In the calculation of book value loss, the collateral of an active loan is assigned to a segment according to its specific characteristics. The assigned recovery rate is equal to the arithmetic mean of historical recovery rates for all realized collateral assigned to the segment. The book value loss for the secured portion of the loan is thus equal to the secured book value minus the recovery rate. 122 In the course of quantitative validation (cf. chapter 6), it is particularly necessary to review the standard deviations of realized recovery rates critically. In cases where deviations from the arithmetic mean are very large, the mean should be adjusted conservatively. One highly relevant practical example is the LGD-Grading procedure used by the Verband deutscher Hypothekenbanken (VDH, the Association of German Mortgage Banks), 123 which consists of approximately 20 institutions. The basis for this model was a sample of some 2,500 defaulted loans (including 1,900 residential and 600 commercial construction loans) which the participating institutions had contributed to a pool in anonymous form. For each data record, the experts preselected and surveyed 30 characteristics. Due to the market presence of the participating institutions, the sample can be assumed to contain representative loss data. On the basis of the 30 characteristics selected, the developers carried out suitable statistical analyses in order to identify 22 discriminating segments with regard to recovery rates. Segmentation is based on the property type, which is currently divided into 9 specific types; efforts are underway to subdivide this category further into 19 types. Additional segmentation criteria include the location and characteristics of the realization market, for example. This historical recovery rate is then applied to the market value in the case of liquidation. For this purpose, the current market value (expert valuation) is extrapolated for the time of liquidation using a conservative market value forecast and any applicable markdowns. In another practical implementation for object financing transactions, segmentation is based on a far smaller sample due to the relative infrequency of defaults. In this case, object categories (aircraft, etc.) were subdivided into individual object types (in the case of aircraft: long-haul freight, long-haul passenger, etc.). Due to the relatively small data set, experts were called in to validate the segment assignments. Additional segmentation criteria included the liquid- 122 For the unsecured portion, the bankruptcy recovery rate can be estimated using a specific segmentation approach (based on individual criteria such as the legal form of business organization, industry, total assets, and the like) analogous to the one described for collateral recovery rates. 123 Various documents on the implementation of this model are available at http://www.hypverband.de/hypverband/attachments/ aktivl,gd_gdw.pdf (in German), or at http://www.pfandbrief.org (menu path: lending/mortgages/LGD-Grading). 160 Guidelines on Credit Risk Management
ity of the realization market and the marketability of the object. A finer differentiation would not have been justifiable due to the size of the data set used. Due to the high variance of recovery rates within segments, the results have to be interpreted conservatively. The recovery rates are applied to the value at realization. In this process, the value at default, which is calculated using a cash flow model, is adjusted conservatively according to the expected average liquidation period for the segment. Estimating Interest Loss In practice, interest loss is estimated on the basis of the interest payments lost due to the default. In this process, the agreed interest for the residual term is discounted, for example using the current risk-free term structure of interest rates. Therefore, the resulting present value implicitly contains potential refinancing costs as well as the spread components process and overhead costs, risk premiums, and profit. There are also practical approaches which account for the increased equity portion required to refinance the amount not written off over the liquidation period. The bankÕs individual cost of equity can be used for this purpose. The practical examples implemented to date have not taken the opportunity costs of lost equity into account. Estimating Workout Costs The estimation method selected for calculating workout costs depends on the organizational structure and the level of detail used in cost unit and cost center accounting. The type and scope of the estimation method should reflect the significance of workout costs for the specific customer or transaction type using the available accounting information. If a bank has a separate restructuring and liquidation unit for a specific business segment, for example, it is relatively easy to allocate the costs incurred by that department. In one practical implementation of a model for object financing transactions, experts estimated the time occupied by typical easy and difficult restructuring/ liquidation cases for an employee with the appropriate qualifications. Based on accounting data, costs per employee were allocated to the time occupied, making it possible to determine the cost rates for easy and difficult liquidation cases. Historical rates for easy and difficult liquidation cases were used to weight these cost rates with their respective probabilities of occurrence. Combining Book Value Loss, Interest Loss and Workout Costs to Yield LGD In order to calculate LGD, the individual loss component estimates have to be merged. In this context, it is important to note that the collateral recoveries are expressed as a percentage of the secured portion and bankruptcy proceeds as a percentage of the unsecured portion of the loan, and that workout costs are more specific to cases than volumes. In order to calculate LGD, the individual components have to be merged accordingly for the credit facility in question. In this context, estimated probabilities of occurrence first have to be assigned to the post-default development scenarios preselected for the specific facility type (cf. section 7.4.2). Then it is necessary to add up the three estimated loss components (book value loss, interest loss, and workout costs). It is not neces- Rating Models and Validation Guidelines on Credit Risk Management 161
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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
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Chart 54: Depiction of a and b erro
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Chart 57: Shape of the a—b Error
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Interpretation of the Pietra Index
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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
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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 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
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Page 157 and 158: during the realization period. The
Page 159: Chart 91: Example of Segmentation f
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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