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Regulatory evaluation of value-at-risk models

  • Jose A. Lopez

Beginning in 1998, U.S. commercial banks may determine their regulatory capital requirements for financial market risk exposure using value-at-risk (VaR) models i.e., models of the time-varying distributions of portfolio returns. Currently, regulators have available three hypothesis-testing methods for evaluating the accuracy of VaR models: the binomial method, the interval forecast method and the distribution forecast method. These methods use hypothesis tests to examine whether the VaR forecasts in question exhibit properties characteristic of accurate VaR forecasts. However, given the low power often exhibited by these tests, these methods may often misclassify forecasts from inaccurate models as accurate. A new evaluation method that uses loss functions based on probability forecasts, is proposed. Simulation results indicate that this method is capable of differentiating between forecasts from accurate and inaccurate, alternative VaR models.

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Paper provided by Federal Reserve Bank of New York in its series Staff Reports with number 33.

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Date of creation: 1997
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Handle: RePEc:fip:fednsr:33
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  1. Darryll Hendricks, 1996. "Evaluation of value-at-risk models using historical data," Economic Policy Review, Federal Reserve Bank of New York, issue Apr, pages 39-69.
  2. 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.
  3. Granger, C. W. J. & White, Halbert & Kamstra, Mark, 1989. "Interval forecasting : An analysis based upon ARCH-quantile estimators," Journal of Econometrics, Elsevier, vol. 40(1), pages 87-96, January.
  4. Granger, C.W.J. & Pesaran, H., 1996. "A Decision_Theoretic Approach to Forecast Evaluation," Cambridge Working Papers in Economics 9618, Faculty of Economics, University of Cambridge.
  5. Francis X. Diebold & Jose A. Lopez, 1996. "Forecast Evaluation and Combination," NBER Technical Working Papers 0192, National Bureau of Economic Research, Inc.
  6. J. S. Butler & Barry Schachter, 1996. "Improving Value-At-Risk Estimates By Combining Kernel Estimation With Historical Simulation," Finance 9605001, EconWPA.
  7. Darryll Hendricks & Beverly Hirtle, 1997. "Bank capital requirements for market risk: the internal models approach," Economic Policy Review, Federal Reserve Bank of New York, issue Dec, pages 1-12.
  8. Dimson, Elroy & Marsh, Paul, 1995. " Capital Requirements for Securities Firms," Journal of Finance, American Finance Association, vol. 50(3), pages 821-51, July.
  9. Chatfield, Chris, 1993. "Calculating Interval Forecasts," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(2), pages 121-35, April.
  10. 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.).
  11. Paul H. Kupiec & James M. O'Brien, 1995. "A pre-commitment approach to capital requirements for market risk," Finance and Economics Discussion Series 95-36, Board of Governors of the Federal Reserve System (U.S.).
  12. Jose A. Lopez, 1995. "Evaluating the predictive accuracy of volatility models," Research Paper 9524, Federal Reserve Bank of New York.
  13. repec:att:wimass:9520 is not listed on IDEAS
  14. Christoffersen, Peter F, 1998. "Evaluating Interval Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 841-62, November.
  15. Arturo Estrella, 1995. "A prolegomenon to future capital requirements," Economic Policy Review, Federal Reserve Bank of New York, issue Jul, pages 1-12.
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