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

  • Jeremy Berkowitz

    ()

    (Department of Finance, University of Houston, Houston, Texas 77004)

  • Peter Christoffersen

    ()

    (McGill University, Montreal, Quebec H3A 2T5, Canada; and CREATES, School of Economics and Management, University of Aarhus, DK-8000 Aarhus C, Denmark)

  • Denis Pelletier

    ()

    (Department of Economics, College of Management, North Carolina State University, Raleigh, North Carolina 27695)

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.

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File URL: http://dx.doi.org/10.1287/mnsc.1080.0964
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Article provided by INFORMS in its journal Management Science.

Volume (Year): 57 (2011)
Issue (Month): 12 (December)
Pages: 2213-2227

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Handle: RePEc:inm:ormnsc:v:57:y:2011:i:12:p:2213-2227
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