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Backtesting Parametric Value-at-Risk with Estimation Risk

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Author Info
Juan Carlos Escanciano () (Indiana University Bloomington)
Jose Olmo () (City University, London)

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Abstract

One of the implications of the creation of Basel Committee on Banking Supervision was the implementation of Value-at-Risk (VaR) as the standard tool for measuring market risk. Since then, the capital requirements of commercial banks with trading activities are based on VaR estimates. Therefore, appropriately constructed tests for assessing the out-of-sample forecast accuracy of the VaR model (backtesting procedures) have become of crucial practical importance. In this paper we show that the use of the standard unconditional and independence backtesting procedures to assess VaR models in out-of-sample composite environments can be misleading. These tests do not consider the impact of estimation risk and therefore may use wrong critical values to assess market risk. The purpose of this paper is to quantify such estimation risk in a very general class of dynamic parametric VaR models and to correct standard backtesting procedures to provide valid inference in out-of-sample analyses. A Monte Carlo study illustrates our theoretical findings in finite-samples and shows that our corrected unconditional test can provide more accurately sized and more powerful tests than the uncorrected one. Finally, an application to S&P500 Index shows the importance of this correction and its impact on capital requirements as imposed by Basel Accord.

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Paper provided by Center for Applied Economics and Policy Research, Economics Department, Indiana University Bloomington in its series Caepr Working Papers with number 2007-005_updated.

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Length: 39 pages
Date of creation: Mar 2007
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Handle: RePEc:inu:caeprp:2007005updated

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Related research
Keywords: Backtesting; Basel Accord; Conditional Quantile; Estimation Risk; Forecast evaluation; Fixed; rolling and recursive forecasting scheme; Risk management; Value at Risk;

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Find related papers by JEL classification:
C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation and Testing
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions
G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Mortgages
G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Capital and Ownership Structure

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  1. Carlos Escanciano, J., 2008. "Joint and marginal specification tests for conditional mean and variance models," Journal of Econometrics, Elsevier, vol. 143(1), pages 74-87, March. [Downloadable!] (restricted)
  2. Oliver Linton & Yoon-Jae Whang, 2003. "A Quantilogram Approach to Evaluating Directional Predictability," STICERD - Econometrics Paper Series /2003/463, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE. [Downloadable!]
    Other versions:
  3. Christoffersen, Peter & Hahn, Jinyong & Inoue, Atsushi, 2001. "Testing and comparing Value-at-Risk measures," Journal of Empirical Finance, Elsevier, vol. 8(3), pages 325-342, July. [Downloadable!] (restricted)
    Other versions:
  4. Valentina Corradi & Norman R. Swanson, 2007. "Nonparametric Bootstrap Procedures For Predictive Inference Based On Recursive Estimation Schemes," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 48(1), pages 67-109, 02. [Downloadable!] (restricted)
    Other versions:
  5. repec:bep:sndecm:10:2006:2:1304-1304 is not listed on IDEAS
  6. Jeremy Berkowitz & James O'Brien, 2002. "How Accurate Are Value-at-Risk Models at Commercial Banks?," Journal of Finance, American Finance Association, vol. 57(3), pages 1093-1111, 06. [Downloadable!] (restricted)
  7. Koenker, Roger & Zhao, Quanshui, 1996. "Conditional Quantile Estimation and Inference for Arch Models," Econometric Theory, Cambridge University Press, vol. 12(05), pages 793-813, December. [Downloadable!]
  8. Mc Cracken, Michael W., 2000. "Robust out-of-sample inference," Journal of Econometrics, Elsevier, vol. 99(2), pages 195-223, December. [Downloadable!] (restricted)
  9. J. Carlos Escanciano & Jose Olmo, 2007. "Estimation risk effects on backtesting for parametric value-at-risk models," City University Economics Discussion Papers 07/11, Department of Economics, City University, London. [Downloadable!]
  10. Keith Kuester & Stefan Mittnik & Marc S. Paolella, 2006. "Value-at-Risk Prediction: A Comparison of Alternative Strategies," Journal of Financial Econometrics, Oxford University Press, vol. 4(1), pages 53-89. [Downloadable!] (restricted)
  11. Giacomini, Raffaella & Komunjer, Ivana, 2005. "Evaluation and Combination of Conditional Quantile Forecasts," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 416-431, October. [Downloadable!] (restricted)
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  12. Li, W K & Ling, Shiqing & McAleer, Michael, 2002. " Recent Theoretical Results for Time Series Models with GARCH Errors," Journal of Economic Surveys, Blackwell Publishing, vol. 16(3), pages 245-69, July. [Downloadable!] (restricted)
  13. Joan Jasiak & C. Gourieroux, 2006. "Dynamic Quantile Models," Working Papers 2006_4, York University, Department of Economics. [Downloadable!]
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  14. Jeremy Berkowitz & Peter Christoffersen & Denis Pelletier, 2005. "Evaluating Value-at-Risk models with desk-level data," Working Paper Series 010, North Carolina State University, Department of Economics, revised Dec 2006. [Downloadable!]
    Other versions:
  15. Durlauf, Steven N., 1991. "Spectral based testing of the martingale hypothesis," Journal of Econometrics, Elsevier, vol. 50(3), pages 355-376, December. [Downloadable!] (restricted)
    Other versions:
  16. Jianqing Fan & Juan Gu, 2003. "Semiparametric estimation of Value at Risk," Econometrics Journal, Royal Economic Society, vol. 6(2), pages 261-290, December. [Downloadable!] (restricted)
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