Learning by Failing: A Simple VaR Buffer

Christophe Boucher () and Bertrand Maillet ()
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Christophe Boucher: UL - Université de Lorraine
Bertrand Maillet: UO - Université d'Orléans, A.A.Advisors-QCG - ABN AMRO

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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.

Date: 2013-05-01
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Published in Financial Markets, Institutions and Instruments, 2013, 22 (2), 113-127 p. ⟨10.1111/fmii.12006⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-02312335

DOI: 10.1111/fmii.12006

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