Evaluating Value-at-Risk models with desk-level data

Jeremy Berkowitz (), Peter Christoffersen and Denis Pelletier
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Jeremy Berkowitz: University of Houston

No 10, Working Paper Series from North Carolina State University, Department of Economics

Abstract: We present new evidence on disaggregated profit and loss and VaR forecasts obtained from a large international commercial bank. Our dataset includes 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 rich dataset, we provide an integrated, unifying framework for assessing the accuracy of VaR forecasts. A thorough Monte Carlo comparison of the various methods is conducted to provide guidance as to which of these many tests have the best finite-sample size and power properties. The Caviar test of Engle and Manganelli (2004) performs best overall but duration-based tests also perform well in many cases.

Keywords: risk management; backtesting; volatility; disclosure (search for similar items in EconPapers)
JEL-codes: G21 G32 (search for similar items in EconPapers)
Pages: 32 pages
Date: 2005-10, Revised 2006-12
New Economics Papers: this item is included in nep-ban, nep-ecm, nep-for and nep-rmg
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Related works:
Journal Article: Evaluating Value-at-Risk Models with Desk-Level Data (2011)
Working Paper: Evaluating Value-at-Risk Models with Desk-Level Data (2008)
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