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Learning by Failing: A Simple VaR Buffer

Christophe Boucher () and Bertrand Maillet
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Christophe Boucher: A.A.Advisors-QCG - ABN AMRO, CEREFIGE - Centre Européen de Recherche en Economie Financière et Gestion des Entreprises - UL - Université de Lorraine

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Abstract: We study in this article the problem of model risk in VaR computations and document a procedure for correcting the bias due to specification and estimation errors. This practical method consists of “learning from model mistakes”, since it dynamically relies on an adjustment of the VaR estimates – based on a back-testing framework – such as the frequency of past VaR exceptions always matches the expected probability. We finally show that integrating the model risk into the VaR computations implies a substantial minimum correction to the order of 10–40% of VaR levels.

Keywords: C14; C50; G11; G32; Revue AERES (search for similar items in EconPapers)
Date: 2013
Note: View the original document on HAL open archive server: http://hal.univ-reunion.fr/hal-01243425
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Published in Financial Markets, Institutions & Instruments, 2013, 22 (2), pp.113--127. <10.1111/fmii.12006>

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