Backtesting Value-at-Risk: A GMM Duration-Based Test

Bertrand Candelon, Gilbert Colletaz, Christophe Hurlin and Sessi Tokpavi ()

Journal of Financial Econometrics, 2011, vol. 9, issue 2, 314-343

Abstract: This paper proposes a new duration-based backtesting procedure for value-at-risk (VaR) forecasts. The GMM test framework proposed by Bontemps (2006) to test for the distributional assumption (i.e., the geometric distribution) is applied to the case of the VaR forecasts validity. Using simple J-statistic based on the moments defined by the orthonormal polynomials associated with the geometric distribution, this new approach tackles most of the drawbacks usually associated to duration-based backtesting procedures. An empirical application for Nasdaq returns confirms that using GMM test leads to major consequences for the expost evaluation of the risk by regulation authorities. JEL: C22, C52, G28 Copyright The Author 2010. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org, Oxford University Press.

Date: 2011
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Related works:
Working Paper: Backtesting Value-at-Risk: A GMM Duration-Based Test (2009)
Working Paper: Backtesting Value-at-Risk: A GMM Duration-based Test (2008)
Working Paper: Backtesting Value-at-Risk: A GMM Duration-based Test (2008)
Working Paper: Backtesting Value-at-Risk: A GMM Duration-based Test (2008)
Working Paper: Backtesting Value-at-Risk: A GMM Duration-Based Test (2008)
Working Paper: Backtesting Value-at-Risk: A GMM Duration-Based-Test (2008)
Working Paper: Backtesting Value-at-Risk: A GMM Duration-Based Test (2008)
Working Paper: Backtesting Value-at-Risk: A GMM Duration-Based Test (2008)
Working Paper: Backtesting Value-at-Risk: A GMM Duration-Based Test (2008)
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