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Pitfalls in Backtesting Historical Simulation VaR Models
Company: Center for Applied Economics and Policy Research, Indiana University Bloomington
Company Url: Click here to open
Year Of Publication: 2012
Month Of Publication: February
Pages: 41
Download Count: 6
View Count: 1581
Comment Num: 0
Language: English
Source: working paper
Who Can Read: Free
Date: 5-6-2012
Publisher: Administrator
Summary
Historical Simulation (HS) and its variant, the Filtered Historical Simulation (FHS), are the most widely used Value-at-Risk forecast methods at commercial banks. These forecast methods are traditionally evaluated by means of the unconditional backtest. This paper formally shows that the unconditional backtest is always inconsistent for backtesting HS and FHS models, with a power function that can be even smaller than the nominal level in large samples. Our findings have fundamental implications in the determination of market risk capital requirements, and also explain Monte Carlo and empirical findings in previous studies. We also propose a data-driven weighted backtest with good power properties to evaluate HS and FHS forecasts. Finally, our theoretical findings are confirmed in a Monte Carlo simulation study and an empirical application with three U.S. stocks. The empirical application shows that multiplication factors computed under the current regulatory framework are downward biased, as they inherit the inconsistency of the unconditional backtest.
CAEPR WP #2012-003
Author(s)
Escanciano, J. Carlos Sign in to follow this author
Pei, Pei Sign in to follow this author
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