Evaluating Value-At-Risk Models With Desk-Level Data
Source: City University of London (Cass)
This paper presents new evidence on disaggregated profit and loss (P/L) and Value-at-Risk (VaR) forecasts obtained from a large international commercial bank. The 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 unique dataset, the paper provides an integrated, unifying framework for assessing the accuracy of VaR forecasts. The authors uses a comprehensive Monte Carlo study to assess which of these many tests have the best finite-sample size and power properties. The 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.