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Evaluating credit risk models

  • Jose A. Lopez
  • Marc R. Saidenberg

Over the past decade, commercial banks have devoted many resources to developing internal models to better quantify their financial risks and assign economic capital. These efforts have been recognized and encouraged by bank regulators; for example, the 1997 Market Risk Amendment (MRA) formally incorporates banks' internal, value-at-risk models into regulatory capital calculations. A key component in the implementation of the MRA was the development of standards, such as for model validation, that must be satisfied in order for banks' models to be used for regulatory capital purposes. Recent proposals, such as by the IIF (1998) and ISDA (1998), argue that credit risk models should also be used to determine risk-adjusted capital requirements. ; However, an important question for both users of such models and their regulators is evaluating the accuracy of the model's forecasts of credit losses. A serious impediment to such model validation is the small number of forecasts available due to the long planning horizons typical of credit risk models. Using a panel data approach, we propose evaluation methods for credit risk models based on cross-sectional simulation. Specifically, models are evaluated not only on their forecasts over time, but also on their forecasts at a given point in time for simulated credit portfolios. Once the forecasts corresponding to these portfolios are generated, they can be evaluated using various methods, such as the binomial method commonly used for evaluating value-at-risk models and currently embodied in the MRA.

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Paper provided by Federal Reserve Bank of San Francisco in its series Working Papers in Applied Economic Theory with number 99-06.

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Date of creation: 1999
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Handle: RePEc:fip:fedfap:99-06
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  1. Jose A. Lopez, 1996. "Regulatory Evaluation of Value-at-Risk Models," Center for Financial Institutions Working Papers 96-51, Wharton School Center for Financial Institutions, University of Pennsylvania.
  2. Crouhy, Michel & Galai, Dan & Mark, Robert, 2000. "A comparative analysis of current credit risk models," Journal of Banking & Finance, Elsevier, vol. 24(1-2), pages 59-117, January.
  3. Francis X. Diebold & Jose A. Lopez, 1996. "Forecast Evaluation and Combination," NBER Technical Working Papers 0192, National Bureau of Economic Research, Inc.
  4. Paul H. Kupiec, 1995. "Techniques for verifying the accuracy of risk measurement models," Finance and Economics Discussion Series 95-24, Board of Governors of the Federal Reserve System (U.S.).
  5. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-44, January.
  6. Jose A. Lopez, 1999. "Methods for evaluating value-at-risk estimates," Economic Review, Federal Reserve Bank of San Francisco, pages 3-17.
  7. William F. Treacy & Mark S. Carey, 1998. "Credit risk rating at large U.S. banks," Federal Reserve Bulletin, Board of Governors of the Federal Reserve System (U.S.), issue Nov, pages 897-921.
  8. Gordy, Michael B., 2000. "A comparative anatomy of credit risk models," Journal of Banking & Finance, Elsevier, vol. 24(1-2), pages 119-149, January.
  9. Diebold, Francis X & Gunther, Todd A & Tay, Anthony S, 1998. "Evaluating Density Forecasts with Applications to Financial Risk Management," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 863-83, November.
  10. Pamela Nickell & William Perraudin & Simone Varotto, 2001. "Ratings versus equity-based credit risk modelling: an empirical analysis," Bank of England working papers 132, Bank of England.
  11. Francis X. Diebold & Jinyong Hahn & Anthony S. Tay, 1998. "Real-Time Multivariate Density Forecast Evaluation and Calibration: Monitoring the Risk of High-Frequency Returns on Foreign Exchange," New York University, Leonard N. Stern School Finance Department Working Paper Seires 98-079, New York University, Leonard N. Stern School of Business-.
  12. Francis X. Diebold & Todd A. Gunther & Anthony S. Tay, 1997. "Evaluating Density Forecasts," Center for Financial Institutions Working Papers 97-37, Wharton School Center for Financial Institutions, University of Pennsylvania.
  13. Mark Carey, 1998. "Credit Risk in Private Debt Portfolios," Journal of Finance, American Finance Association, vol. 53(4), pages 1363-1387, 08.
  14. Jeremy Berkowitz, 1999. "Evaluating the forecasts of risk models," Finance and Economics Discussion Series 1999-11, Board of Governors of the Federal Reserve System (U.S.).
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