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

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Author Info

  • Jeremy Berkowitz

    ()
    (University of Houston)

  • Peter Christoffersen

    ()
    (McGill University)

  • Denis Pelletier

    ()
    (Department of Economics, North Carolina State University)

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.

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Bibliographic Info

Paper provided by North Carolina State University, Department of Economics in its series Working Paper Series with number 010.

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Length: 32 pages
Date of creation: Oct 2005
Date of revision: Dec 2006
Handle: RePEc:ncs:wpaper:010

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Related research

Keywords: risk management; backtesting; volatility; disclosure;

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