Learning by Failing: A Simple VaR Buffer
Christophe Boucher () and
Additional contact information
Christophe Boucher: A.A.Advisors-QCG - ABN AMRO, CEREFIGE - Centre Européen de Recherche en Economie Financière et Gestion des Entreprises - UL - Université de Lorraine
Post-Print from HAL
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)
Note: View the original document on HAL open archive server: http://hal.univ-reunion.fr/hal-01243425
References: Add references at CitEc
Citations View citations in EconPapers (1) Track citations by RSS feed
Published in Financial Markets, Institutions & Instruments, 2013, 22 (2), pp.113--127. 〈10.1111/fmii.12006〉
There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-01243425
Access Statistics for this paper
More papers in Post-Print from HAL
Series data maintained by CCSD ().