Evaluating Value-at-Risk Models with Desk-Level Data

Jeremy Berkowitz (), Peter Christoffersen and Denis Pelletier
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Jeremy Berkowitz: Department of Finance, University of Houston, Houston, Texas 77004

Management Science, 2011, vol. 57, issue 12, 2213-2227

Abstract: We present new evidence on disaggregated profit and loss (P/L) and value-at-risk (VaR) forecasts obtained from a large international commercial bank. Our data set includes the actual daily P/L generated by four separate business lines within the bank. All four business lines are involved in securities trading and each is observed daily for a period of at least two years. Given this unique data set, we provide an integrated, unifying framework for assessing the accuracy of VaR forecasts. We use a comprehensive Monte Carlo study to assess which of these many tests have the best finite-sample size and power properties. Our desk-level data set provides importance guidance for choosing realistic P/L-generating processes in the Monte Carlo comparison of the various tests. The conditional autoregressive value-at-risk test of Engle and Manganelli (2004) performs best overall, but duration-based tests also perform well in many cases. This paper was accepted by John Birge, focused issue editor.

Keywords: risk management; backtesting; volatility; disclosure (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (143)

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http://dx.doi.org/10.1287/mnsc.1080.0964 (application/pdf)

Related works:
Working Paper: Evaluating Value-at-Risk Models with Desk-Level Data (2008)
Working Paper: Evaluating Value-at-Risk models with desk-level data (2006)
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:57:y:2011:i:12:p:2213-2227

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