Rating Models and Validation - Oesterreichische Nationalbank

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Rating Models and Validation This will make it possible to evaluate the start-upÕs business success to date on the basis of quantitative data and to compare this information with the business plan and future planning information, thus providing a more complete picture of the start-upÕs creditworthiness. NPOs (Non-Profit Organizations) Although NPOs do not operate for the purpose of making a profit, it is still necessary to review the economic sustainability of these organizations by analyzing their annual financial statements. In comparison to those of conventional profitoriented companies, the individual balance sheet indicators of NPOs have to be interpreted differently. However, these indicators still enable reliable statements as to the organizationÕs economic efficiency. In order to allow forward-looking assessments of whether the organization will be able to meet its payment obligations, it is also necessary to analyze the organizationÕs debt service capacity. This debt service capacity analysis is to be reviewed in a critical light by assessing the organizationÕs planning and future development. It is also important to analyze bank account activity data in order to detect payment disruptions at an early stage. The viability of an NPO also depends on qualitative factors such as its management and the prospects of the industry. As external information, the general legal and political circumstances in which the NPO operates should be taken into account, as NPOs are often dependent on current legislation and government grants (e.g. in organizations funded by donations). 2.4 Corporate Customers — Specialized Lending Specialized lending operations can be characterized as follows: 3 — The exposure is typically to an entity (often a special purpose entity (SPE)) which was created specifically to finance and/or operate physical assets; — The borrowing entity has little or no other material assets or activities, and therefore little or no independent capacity to repay the obligation, apart from the income that it receives from the asset(s) being financed; — The terms of the obligation give the lender a substantial degree of control over the asset(s) and the income that it generates; and — As a result of the preceding factors, the primary source of repayment of the obligation is the income generated by the asset(s), rather than the independent capacity of a broader commercial enterprise. On the basis of the characteristics mentioned above, specialized lending operations have to be assessed differently from conventional companies and are therefore subject to different data requirements. In contrast to that of conventional companies, credit assessment in this context focuses not on the borrower but on the assets financed and the cash flows expected from those assets. 3 Cf. EUROPEAN COMMISSION, draft directive on regulatory capital requirements, Article 47, No. 8. 22 Guidelines on Credit Risk Management
Chart 5: Data Requirements for Corporate Customers — Specialized Lending Rating Models and Validation Guidelines on Credit Risk Management 23
Page 1 and 2: ≈√ Guidelines on Credit Risk Ma
Page 3 and 4: The ongoing development of contempo
Page 5 and 6: 5 Developing a Rating Model 60 5.1
Page 7 and 8: Rating Models and Validation I INTR
Page 9 and 10: This segmentation from the business
Page 11 and 12: The best-practice segmentation pres
Page 13 and 14: Chart 2: Data Requirements for Gove
Page 15 and 16: 2.2 Financial Service Providers In
Page 17 and 18: Insurance Companies Due to their di
Page 19 and 20: Capital Market-Oriented/Internation
Page 21: — Market prospects are not assess
Page 25 and 26: elationships in the project, these
Page 27 and 28: Before the Project As the repayment
Page 29 and 30: Chart 6: Data Requirements for Reta
Page 31 and 32: During the Credit Term Instead of o
Page 33 and 34: 3.1 Heuristic Models Heuristic mode
Page 35 and 36: Chart 9: Information Categories for
Page 37 and 38: Explanatory Component The explanato
Page 39 and 40: This example defines linguistic ter
Page 41 and 42: ments as to whether higher or lower
Page 43 and 44: Chart 16: Indicators in the ÒCrebo
Page 45 and 46: Logistic regression has a number of
Page 47 and 48: adapts the network according to any
Page 49 and 50: The parameters required to calculat
Page 51 and 52: ture of the borrowerÕs creditworth
Page 53 and 54: Chart 24: Vertical Linking of Ratin
Page 55 and 56: e necessary in this case if the def
Page 57 and 58: 4.1.5 Consistency Heuristic models
Page 59 and 60: Compared to heuristic models, stati
Page 61 and 62: the data set and statistical testin
Page 63 and 64: data collection procedure. This pro
Page 65 and 66: full surveys is often too high, esp
Page 67 and 68: essential structural characteristic
Page 69 and 70: For each block, interim objectives
Page 71 and 72: Chart 34: Creating the Analysis Dat
Page 73 and 74:
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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The following relation applies to t
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Chart 62: ROC Curve with Simultaneo
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Chart 65: Interpretation of the Bay
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The table below (chart 67) shows a
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The lower the Brier Score is, the b
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Chart 70 shows the reliability diag
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Chart 71: Identification of Signifi
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estimates, which means that it is e
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transition matrix, the data require
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tency of the cumulative and conditi
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to internal back-testing results in
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Changes in correlations One of the
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6.4.3 Developing Stress Tests This
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Counterparty-based and credit facil
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tify decisive factors influencing i
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III ESTIMATING AND VALIDATING LGD/E
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Chart 81: Loss Components in LGD to
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assets, as the danger exists that m
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— Collateral: Collateral value, c
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Chart 87: Overview of Customer Type
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transaction, however, it is also ne
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Implementing an in-house LGD estima
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nents; this is analogous to the use
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those assets which serve as collate
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during the realization period. The
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Chart 91: Example of Segmentation f
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ity of the realization market and t
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The level of utilization for off-ba
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8.3 EAD Estimation Methods As in th
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IV REFERENCES Backhaus, Klaus/Erich
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Stuhlinger, Matthias, Rolle von Rat
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Pytlik, Martin, Diskriminanzanalyse
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