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Robust Backtesting Tests for Value-at-Risk Models
Year Of Publication: 2008
Month Of Publication: November
Pages: 32
Download Count: 31
View Count: 1976
Comment Num: 0
Language: English
Source: working paper
Who Can Read: Free
Date: 5-3-2010
Publisher: Administrator
Summary
Backtesting methods are statistical tests designed to uncover excessive risk-taking from financial institutions. We show in this paper that these methods are subject to the presence of model risk produced by a wrong specification of the conditional VaR model, and derive its effect on the asymptotic distribution of the relevant out-of-sample tests. We also show that in the absence of estimation risk, the unconditional backtest is affected by model misspecification but the independence test is not. Our solution for the general case consists on proposing robust subsampling techniques to approximate the true sampling distribution of these tests. We carry out a Monte Carlo study to see the importance of these effects in finite samples for location-scale models that are wrongly specified but correct on average. An application to Dow-Jones Index shows the impact of correcting for model risk on backtesting procedures for different dynamic VaR models measuring risk exposure.
This document is published in Journal of Financial Econometrics (volume 9, number 1) 2010, pp., 132-161
http://dx.doi.org/10.1093/jjfinec/nbq021
Author(s)
Olmo, Jose Sign in to follow this author
Escanciano, J. Carlos Sign in to follow this author
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