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

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.