Learning by Failing: A Simple VaR Buffer

Christophe M. Boucher and Bertrand B. Maillet

Financial Markets, Institutions & Instruments, 2013, vol. 22, issue 2, 113-127

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
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1111/fmii.12006

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:finmar:v:22:y:2013:i:2:p:113-127

Access Statistics for this article

More articles in Financial Markets, Institutions & Instruments from John Wiley & Sons
Bibliographic data for series maintained by Wiley Content Delivery ().