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Learning by Failing: A Simple VaR Buffer
Author(s) -
Boucher Christophe M.,
Maillet Bertrand B.
Publication year - 2013
Publication title -
financial markets, institutions and instruments
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.386
H-Index - 23
eISSN - 1468-0416
pISSN - 0963-8008
DOI - 10.1111/fmii.12006
Subject(s) - computation , computer science , simple (philosophy) , vector autoregression , econometrics , value at risk , order (exchange) , estimation , algorithm , mathematics , risk management , economics , finance , philosophy , management , epistemology
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.