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Margin Backtesting

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Author Info

  • Christophe Hurlin

    ()
    (LEO - Laboratoire d'économie d'Orleans - CNRS : UMR6221 - Université d'Orléans)

  • Christophe Pérignon

    (GREGH - Groupement de Recherche et d'Etudes en Gestion à HEC - GROUPE HEC - CNRS : UMR2959)

Abstract

This paper presents a validation framework for collateral requirements or margins on a derivatives exchange. It can be used by investors, risk managers, and regulators to check the accuracy of a margining system. The statistical tests presented in this study are based either on the number, frequency, magnitude, or timing of margin exceedances, which are defined as situations in which the trading loss of a market participant exceeds his or her margin. We also propose an original way to validate globally the margining system by aggregating individual backtesting statistics obtained for each market participant.

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Bibliographic Info

Paper provided by HAL in its series Working Papers with number halshs-00746274.

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Date of creation: 28 Oct 2012
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Handle: RePEc:hal:wpaper:halshs-00746274

Note: View the original document on HAL open archive server: http://halshs.archives-ouvertes.fr/halshs-00746274
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Related research

Keywords: Collateral Requirements; Futures Markets; Tail Risk; Derivatives Clearing;

This paper has been announced in the following NEP Reports:

References

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  1. Peter Christoffersen, 2004. "Backtesting Value-at-Risk: A Duration-Based Approach," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 2(1), pages 84-108.
  2. Bertrand Candelon & Gilbert Colletaz & Christophe Hurlin & Sessi Tokpavi, 2011. "Backtesting Value-at-Risk: A GMM Duration-Based Test," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 9(2), pages 314-343, Spring.
  3. Markus K. Brunnermeier & Lasse Heje Pedersen, 2007. "Market Liquidity and Funding Liquidity," NBER Working Papers 12939, National Bureau of Economic Research, Inc.
  4. Jeremy Berkowitz & Peter Christoffersen & Denis Pelletier, 2005. "Evaluating Value-at-Risk models with desk-level data," Working Paper Series, North Carolina State University, Department of Economics 010, North Carolina State University, Department of Economics, revised Dec 2006.
  5. Robert F. Engle & Simone Manganelli, 2004. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 22, pages 367-381, October.
  6. Pérignon, Christophe & Smith, Daniel R., 2010. "The level and quality of Value-at-Risk disclosure by commercial banks," Journal of Banking & Finance, Elsevier, Elsevier, vol. 34(2), pages 362-377, February.
  7. Pasaran, M.H. & Im, K.S. & Shin, Y., 1995. "Testing for Unit Roots in Heterogeneous Panels," Cambridge Working Papers in Economics, Faculty of Economics, University of Cambridge 9526, Faculty of Economics, University of Cambridge.
  8. Jeremy Berkowitz & James O'Brien, 2002. "How Accurate Are Value-at-Risk Models at Commercial Banks?," Journal of Finance, American Finance Association, American Finance Association, vol. 57(3), pages 1093-1111, 06.
  9. Paul H. Kupiec, 1995. "Techniques for verifying the accuracy of risk measurement models," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.) 95-24, Board of Governors of the Federal Reserve System (U.S.).
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Citations

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Cited by:
  1. Jorge Cruz Lopez & Jeffrey Harris & Christophe Hurlin & Christophe Pérignon, 2014. "CoMargin," Working Papers halshs-00979440, HAL.
  2. Gilbert Colletaz & Christophe Hurlin & Christophe Pérignon, 2012. "The Risk Map: A New Tool for Validating Risk Models," Working Papers halshs-00746273, HAL.

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