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Backtesting trading risk of commercial banks using expected shortfall

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  • Wong, Woon K.

Abstract

This paper uses saddlepoint technique to backtest the trading risk of commercial banks using expected shortfall. It is found that four out of six US commercial banks have excessive trading risks. Monte Carlo simulation studies show that the proposed backtest is very accurate and powerful even for small test samples. More importantly, risk managers can carry out the proposed backtest based on any number of exceptions, so that incorrect risk models can be promptly detected before any further huge losses are realized.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Banking & Finance.

Volume (Year): 32 (2008)
Issue (Month): 7 (July)
Pages: 1404-1415

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Handle: RePEc:eee:jbfina:v:32:y:2008:i:7:p:1404-1415

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References

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  1. Philippe Artzner & Freddy Delbaen & Jean-Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228.
  2. Eberlein, Ernst & Keller, Ulrich & Prause, Karsten, 1998. "New Insights into Smile, Mispricing, and Value at Risk: The Hyperbolic Model," The Journal of Business, University of Chicago Press, vol. 71(3), pages 371-405, July.
  3. M.J.B. Hall, 1996. "The amendment to the capital accord to incorporate market risk," BNL Quarterly Review, Banca Nazionale del Lavoro, vol. 49(197), pages 271-277.
  4. Berkowitz, Jeremy, 2001. "Testing Density Forecasts, with Applications to Risk Management," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(4), pages 465-74, October.
  5. François Longin, 2001. "Extreme Correlation of International Equity Markets," Journal of Finance, American Finance Association, vol. 56(2), pages 649-676, 04.
  6. Kerkhof, Jeroen & Melenberg, Bertrand, 2004. "Backtesting for risk-based regulatory capital," Journal of Banking & Finance, Elsevier, vol. 28(8), pages 1845-1865, August.
  7. 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.).
  8. Yamai, Yasuhiro & Yoshiba, Toshinao, 2005. "Value-at-risk versus expected shortfall: A practical perspective," Journal of Banking & Finance, Elsevier, vol. 29(4), pages 997-1015, April.
  9. Szego, Giorgio, 2002. "Measures of risk," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1253-1272, July.
  10. M.J.B. Hall, 1996. "The amendment to the capital accord to incorporate market risk," Banca Nazionale del Lavoro Quarterly Review, Banca Nazionale del Lavoro, vol. 49(197), pages 271-277.
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Cited by:
  1. Weiß, Gregor N.F., 2011. "Are Copula-GoF-tests of any practical use? Empirical evidence for stocks, commodities and FX futures," The Quarterly Review of Economics and Finance, Elsevier, vol. 51(2), pages 173-188, May.
  2. Wong, Woon K & Copeland, Laurence, 2008. "Risk Measurement and Management in a Crisis-Prone World," Cardiff Economics Working Papers E2008/14, Cardiff University, Cardiff Business School, Economics Section.
  3. Chen, Qian & Gerlach, Richard H., 2013. "The two-sided Weibull distribution and forecasting financial tail risk," International Journal of Forecasting, Elsevier, vol. 29(4), pages 527-540.
  4. Lönnbark, Carl, 2013. "On the role of the estimation error in prediction of expected shortfall," Journal of Banking & Finance, Elsevier, vol. 37(3), pages 847-853.
  5. Chen, Qian & Gerlach, Richard & Lu, Zudi, 2012. "Bayesian Value-at-Risk and expected shortfall forecasting via the asymmetric Laplace distribution," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3498-3516.
  6. Ralf Sabiwalsky, 2012. "Does Basel II Pillar 3 Risk Exposure Data help to Identify Risky Banks?," SFB 649 Discussion Papers SFB649DP2012-008, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.

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