Rating Models and Validation - Oesterreichische Nationalbank

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Rating Models and Validation V FURTHER READING Further Reading on Ratings/PD Albrecht, Jo‹ rg/Baetge, Jo‹ rg/Jerschensky, Andreas/Roeder, Klaus-Hendrick, Risikomanagement auf der Basis von Insolvenzwahrscheinlichkeiten, in: Die Bank 1999, No. 7, 494—499 Berens, Wolfgang, Beurteilung von Heuristiken: Neuorientierung und Vertiefung am Beispiel logistischer Probleme, Wiesbaden 1991 Corsten, Hans/May, Constantin, Anwendungsfelder Neuronaler Netze und ihre Umsetzung, in: Neuronale Netze in der Betriebswirtschaft: Anwendung in Prognose, Klassifikation und Optimierung — Ein Reader, Corsten, Hans/May, Constantin (eds.), Wiesbaden 1996, 1—11 Crosbie/Bohn, Modelling Default Risk, KMV LLC 2001, http://www.kmv.com/insight/index.html (Modelling Default Risk) Erxleben, K. et al., Klassifikation von Unternehmen, Ein Vergleich von Neuronalen Netzen und Diskriminanzanalyse, in: ZfB 1992, No. 11, 1237—1262 Fahrmeir, L./Frank, M./Hornsteiner, U., Bonita‹tspru‹fung mit alternativen Methoden der Diskriminanzanalyse, in: Die Bank 1994, No. 6, 368—373. Fahrmeier, L./Hamerle, A./Tutz, G. (eds.), Multivariate statistische Verfahren, Berlin/New York 1996 Feulner, Waldemar, Moderne Verfahren bei der Kreditwu‹rdigkeitspru‹fung im Konsumentenkreditgescha‹ft, Frankfurt a. M. 1980 Fischer, Ju‹rgen H., Computergestu‹tzte Analyse der Kreditwu‹rdigkeitspru‹fung auf Basis der Mustererkennung, in: Betriebswirtschaftliche Schriften zur Unternehmensfu‹hrung, Vol. 23: Kreditwesen, Du‹sseldorf 1981 Gabriel, Roland, Wissensbasierte Systeme in der betrieblichen Praxis, Hamburg/New York 1990 Gabriel, Roland/Frick, Detlev, Expertensysteme zur Lo‹sung betriebswirtschaftlicher Problemstellungen, in: ZfbF 1991, No. 6, 544—565 Gaida, S., Kreditrisikokosten-Kalkulation mit Optionspreisansa‹tzen, Die empirische Anwendung eines Modells von Longstaff und Schwartz auf risikobehaftete Finanztitel, Mu‹nster 1997 Gaida, S., Bewertung von Krediten mit Optionspreisansa‹tzen, in: Die Bank 1998, No. 3, 180—184 Hauschildt, Ju‹ rgen/Leker, Jens, Kreditwu‹rdigkeitspru‹fung, inkl. automatisierte, in: Handwo‹rterbuch des Bank- und Finanzwesen, Gerke, Wolfgang/ Steiner, Manfred (eds.), 2nd ed., Stuttgart 1995, 251—262 Hu‹ ls, Dagmar, Fru‹herkennung insolvenzgefa‹hrdeter Unternehmen, in: Schriften des Instituts fu‹r Revisionswesen der Westfa‹lischen Wilhelms-Universita‹t Mu‹nster, Baetge, Jo‹rg (ed.), Du‹sseldorf 1995 Jacobs, Otto H., Bilanzanalyse: EDV-gestu‹tzte Jahresabschlussanalyse als Planungs- und Entscheidungsrechnung, 2nd ed., Munich 1994 Jacobs, Otto H./Oestreicher, Andreas/Piotrowski-Allert, Susanne, Die Einstufung des Fehlerrisikos im handelsrechtlichen Jahresabschluss anhand von Regressionen aus empirisch bedeutsamen Erfolgsfaktoren, in: ZfbF 1999, No. 6, 523—549 Krakl, Johann/Nolte-Hellwig, K. Ulf, Computergestu‹tzte Bonita‹tsbeurteilung mit dem Expertensystem ªCODEXÒ, in: Die Bank 1990, No. 11, 625—634 Krause, Clemens, Kreditwu‹rdigkeitspru‹fung mit Neuronalen Netzen, in: Schriften des Instituts fu‹r Revisionswesen der Westfa‹lischen Wilhelms-Universita‹t Mu‹nster, Baetge, Jo‹rg (ed.), Du‹sseldorf 1993 Kurbel, Karl, Entwicklung und Einsatz von Expertensystemen: Eine anwendungsorientierte Einfu‹hrung in wissensbasierte Systeme, 2nd ed., Berlin 1992 Mechler, Bernhard, Intelligente Informationssysteme, Bonn 1995 Nauck, Detlef/Klawonn, Frank/Kruse, Rudolf, Neuronale Netze und Fuzzy-Systeme: Grundlagen des Konnektionismus, Neuronale Fuzzy-Systeme und der Kopplung mit wissensbasierten Methoden, 2nd ed., Braunschweig 1996 170 Guidelines on Credit Risk Management
Pytlik, Martin, Diskriminanzanalyse und Ku‹nstliche Neuronale Netze zur Klassifizierung von Jahresabschlu‹ssen: Ein empirischer Vergleich, in: Europa‹ische Hochschulschriften, No. 5, Volks- und Betriebswirtschaft, Vol. 1688, Frankfurt a. M. 1995 Sachs, L., Angewandte Statistik, 9th ed., Springer 1999 (Angewandte Statistik) Schnurr, Christoph, Kreditwu‹rdigkeitspru‹fung mit Ku‹nstlichen Neuronalen Netzen: Anwendung im Konsumentenkreditgescha‹ft, Wiesbaden 1997 Schultz, Jens/Mertens, Peter, Expertensystem im Finanzdienstleistungssektor: Zahlen aus der Datensammlung, in: KI 1996, No. 4, 45—48 Weber, Martin/Krahnen, Jan/Weber, Adelheid, Scoring-Verfahren — ha‹ufige Anwendungsfehler und ihre Vermeidung, in: DB 1995; No. 33, 1621—1626 Further Reading on LGD/EAD Altmann, Edward I./Resti, Andrea/Sironi, Andrea, Analyzing and Explaining Default Recovery Rates, The International Swaps & Dervatives Association, 2001 Altmann, Edward I./Resti, Andrea/Sironi, Andrea, The Link between Default and Recovery Rates: Effects on the Procyclicality of Regulatory Capital Ratios, BIS Working Papers, No. 113, 2002 Altmann, Eward I./Brady, Brooks/Resti, Andrea/Sironi, Andrea, The Link between Default and Recovery Rates: Implications for Credit Risk Models and Procyclicality, 2003 Araten, M. und Jacobs M. Jr., Loan Equivalents for Revolving Credits and Advised Lines, The RMA Journal, May 2001, 34—39 Asarnow, E. und J. Marker, Historical Performance of the U.S. Corporate Loan Market: 1988—1993, The Journal of Commercial Lending (1995), Vol. 10 (2), 13—32. Bakshi, G./Dilip Madan, Frank Zhang, Understanding the Role of Recovery in Default Risk Models: Empirical Comparisons and Implied Recovery Rates, in: Finance and Economics Discussion Series, 2001-37, Federal Reserve Board of Governors, Washington D.C. Basel Committee on Banking Supervision, Credit Risk Modeling: Current Practices and Applications, Bank for International Settlements, 1999 Bu‹ rgisser, Peter/Kurth, Alexander/Wagner, Armin, Incorporating Severity Variations into Credit Risk, in: Journal of Risk 2001, Volume 3, Number 4 Frye, Jon, Depressing Recoveries, Federal Reserve Bank of Chicago, Working Papers, 2000 Gordy, Michael B., A Risk-Factor Model Foundation for Ratings-Based Bank Capital Rules, Board of Governors of the Federal Reserve System, 2002 Gupton, Greg M./Gates, Daniel/Carty, Lea V., Bank Loan Loss Given Default, MoodyÕs Investors Service — Global Credit Research, 2000 Gupton, Greg M./Stein, Roger M., Loss CalcTM: MoodyÕs Model for Predicting Loss Given Default (LGD), MoodyÕs Investors Service-Global Credit Research, 2002 Holter, Rebecca/Marburger, Christian, Basel II — Description of LGD-Grading project for the Verein deutscher Hypothekenbanken (Association of German Mortgage Banks), http://www.hypverband.de/hypverband/attachments/aktivl,gd_gdw.pdf (in German) or http://www.pfandbrief.org (menu path: lending/mortgages/LGD-Grading). Jokivuolle, Esa/Peura, Samu, A Model for Estimating Recovery Rates and Collateral Haircuts for Bank Loans, in: Bank of Finland-Discussion Papers 2/2000 Jokivuolle, Esa/Peura, Samu, Incorporating Collateral Value Uncertainty in Loss Given Default: Estimates and Loan-to-value Ratios, 2003 Katzengruber, Bruno, Loss Given Default: Ratingagenturen und Basel 2, in: O‹ sterreichisches Bank-Archiv 2003, No. 10, 747—752 Van de Castle, Karen/Keisman, David, Recovering your Money: Insights into Losses from Defaults, in: Standard & PoorÕs CreditWeek 1999, 28—34 Rating Models and Validation Guidelines on Credit Risk Management 171
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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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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
Page 119 and 120: Chart 70 shows the reliability diag
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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
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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
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Page 151 and 152: Implementing an in-house LGD estima
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Page 157 and 158: during the realization period. The
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: Stuhlinger, Matthias, Rolle von Rat
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