Post by Sapphire Capital on Jul 14, 2008 20:31:35 GMT 4
Evaluating Value-at-Risk Models with Desk-Level Data
JEREMY BERKOWITZ
University of Houston - Department of Finance
PETER F. CHRISTOFFERSEN
McGill University - Faculty of Management
DENIS PELLETIER
North Carolina State University - Department of Economics
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July 31, 2007
Abstract:
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 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.
papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID1127226_code39700.pdf?abstractid=1127226&mirid=3
JEREMY BERKOWITZ
University of Houston - Department of Finance
PETER F. CHRISTOFFERSEN
McGill University - Faculty of Management
DENIS PELLETIER
North Carolina State University - Department of Economics
--------------------------------------------------------------------------------
July 31, 2007
Abstract:
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 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.
papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID1127226_code39700.pdf?abstractid=1127226&mirid=3