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Regulatory evaluation of value-at-risk models

  • Jose A. Lopez

Beginning in 1998, commercial banks may determine their regulatory capital requirements for market risk exposure using value-at-risk (VaR) models; i.e., time-series models of the distributions of portfolio returns. Currently, regulators have available three statistical methods for evaluating the accuracy of VaR models: the binomial method, the interval forecast method, and the distribution forecast method. These methods test whether the VaR forecasts in question exhibit properties characteristics of accurate VaR forecasts. However, the statistical tests can have low power against alternative models. A new evaluation method, based on proper scoring rules for probability forecasts, is proposed. Simulation results indicate that this method is clearly capable of differentiating among accurate and alternative VaR models.

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Paper provided by Federal Reserve Bank of New York in its series Research Paper with number 9710.

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Date of creation: 1997
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Handle: RePEc:fip:fednrp:9710
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  1. Francis X. Diebold & Todd A. Gunther & Anthony S. Tay, 1997. "Evaluating density forecasts," Working Papers 97-6, Federal Reserve Bank of Philadelphia.
  2. Chatfield, Chris, 1993. "Calculating Interval Forecasts," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(2), pages 121-35, April.
  3. Dimson, Elroy & Marsh, Paul, 1995. " Capital Requirements for Securities Firms," Journal of Finance, American Finance Association, vol. 50(3), pages 821-51, July.
  4. 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.).
  5. Jose A. Lopez, 1995. "Evaluating the predictive accuracy of volatility models," Research Paper 9524, Federal Reserve Bank of New York.
  6. 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.
  7. 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.
  8. Arturo Estrella, 1995. "A prolegomenon to future capital requirements," Economic Policy Review, Federal Reserve Bank of New York, issue Jul, pages 1-12.
  9. 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.
  10. J. S. Butler & Barry Schachter, 1996. "Improving Value-At-Risk Estimates By Combining Kernel Estimation With Historical Simulation," Finance 9605001, EconWPA.
  11. 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.
  12. 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.
  13. Francis X. Diebold & Jose A. Lopez, 1996. "Forecast Evaluation and Combination," NBER Technical Working Papers 0192, National Bureau of Economic Research, Inc.
  14. 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.).
  15. Brock, W.A. & Dechert, W.D. & LeBaron, B. & Scheinkman, J.A., 1995. "A Test for Independence Based on the Correlation Dimension," Working papers 9520, Wisconsin Madison - Social Systems.
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