Learning by Failing: A Simple VaR Buffer

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.
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Article dans une revue
Financial Markets, Institutions & Instruments, 2013, 22 (2), pp.113--127. 〈10.1111/fmii.12006〉
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http://hal.univ-reunion.fr/hal-01243425
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Soumis le : mardi 15 décembre 2015 - 07:37:19
Dernière modification le : vendredi 14 septembre 2018 - 08:15:20

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Christophe Boucher, Bertrand Maillet. Learning by Failing: A Simple VaR Buffer. Financial Markets, Institutions & Instruments, 2013, 22 (2), pp.113--127. 〈10.1111/fmii.12006〉. 〈hal-01243425〉

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