Evaluating Value-at-Risk Models with Desk-Level Data
AbstractWe 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.
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Bibliographic InfoPaper provided by School of Economics and Management, University of Aarhus in its series CREATES Research Papers with number 2009-35.
Date of creation: 30 Oct 2008
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Web page: http://www.econ.au.dk/afn/
Risk Management; Backtesting; Volatility; Disclosure;
Other versions of this item:
- Jeremy Berkowitz & Peter Christoffersen & Denis Pelletier, 2011. "Evaluating Value-at-Risk Models with Desk-Level Data," Management Science, INFORMS, vol. 57(12), pages 2213-2227, December.
- Jeremy Berkowitz & Peter Christoffersen & Denis Pelletier, 2005. "Evaluating Value-at-Risk models with desk-level data," Working Paper Series 010, North Carolina State University, Department of Economics, revised Dec 2006.
- G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
- G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
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