Evaluating Value-at-Risk Models with Desk-Level Data
Jeremy Berkowitz () and
Denis Pelletier ()
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Jeremy Berkowitz: University of Houston
CREATES Research Papers from Department of Economics and Business Economics, Aarhus University
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 dataset 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 dataset, 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 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)
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Journal Article: Evaluating Value-at-Risk Models with Desk-Level Data (2011)
Working Paper: Evaluating Value-at-Risk models with desk-level data (2006)
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Persistent link: https://EconPapers.repec.org/RePEc:aah:create:2009-35
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