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How accurate are Value-at-Risk models at commercial banks?

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Author Info
Jeremy Berkowitz
James O'Brien
Abstract

In recent years, the trading accounts at large commercial banks have grown substantially and become progressively more diverse and complex. We provide descriptive statistics on the trading revenues from such activities and on the associated Value-at-Risk forecasts internally estimated by banks. For a sample of large bank holding companies, we evaluate the performance of banks' trading risk models by examining the statistical accuracy of the VaR forecasts. Although a substantial literature has examined the statistical and economic meaning of Value-at-Risk models, this article is the first to provide a detailed analysis of the performance of models actually in use.

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Paper provided by Board of Governors of the Federal Reserve System (U.S.) in its series Finance and Economics Discussion Series with number 2001-31.

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Date of creation: 2001
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Handle: RePEc:fip:fedgfe:2001-31

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Keywords: Risk ; Econometric models;

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This paper has been announced in the following NEP Reports: References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. Jose A. Lopez & Christian A. Walter, 2000. "Evaluating covariance matrix forecasts in a value-at-risk framework," Working Papers in Applied Economic Theory 2000-21, Federal Reserve Bank of San Francisco. [Downloadable!]
  2. Matthew Pritsker, 1997. "Evaluating Value at Risk Methodologies: Accuracy versus Computational Time," Journal of Financial Services Research, Springer, vol. 12(2), pages 201-242, October. [Downloadable!] (restricted)
  3. Peter F. Christoffersen & Francis X. Diebold, 2000. "How Relevant is Volatility Forecasting for Financial Risk Management?," The Review of Economics and Statistics, MIT Press, vol. 82(1), pages 12-22, February. [Downloadable!] (restricted)
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  4. 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. [Downloadable!]
  5. Darryll Hendricks, 1996. "Evaluation of value-at-risk models using historical data," Proceedings, Federal Reserve Bank of Chicago, issue May, pages 334-362.
  6. Gray, Stephen F., 1996. "Modeling the conditional distribution of interest rates as a regime-switching process," Journal of Financial Economics, Elsevier, vol. 42(1), pages 27-62, September. [Downloadable!] (restricted)
  7. Robert Engle & Simone Manganelli, 1999. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," University of California at San Diego, Economics Working Paper Series 1999-20, Department of Economics, UC San Diego. [Downloadable!]
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  8. Dimson, Elroy & Marsh, Paul, 1995. " Capital Requirements for Securities Firms," Journal of Finance, American Finance Association, vol. 50(3), pages 821-51, July. [Downloadable!] (restricted)
  9. Francis X. Diebold & Todd A. Gunther & Anthony S. Tay, 1997. "Evaluating Density Forecasts," NBER Technical Working Papers 0215, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
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  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. 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.
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(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Christian A. Johnson, 2002. "Value at Risk: Teoría y Aplicaciones," Working Papers Central Bank of Chile 136, Central Bank of Chile. [Downloadable!]
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