Evaluating Value-at-Risk models with desk-level data
We present new evidence on disaggregated profit and loss and 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 rich dataset, we provide an integrated, unifying framework for assessing the accuracy of VaR forecasts. A thorough Monte Carlo comparison of the various methods is conducted to provide guidance as to which of these many tests have the best finite-sample size and power properties. The Caviar test of Engle and Manganelli (2004) performs best overall but duration-based tests also perform well in many cases.
|Date of creation:||Oct 2005|
|Date of revision:||Dec 2006|
|Contact details of provider:|| Phone: (919) 515-3274|
Web page: http://www.mgt.ncsu.edu/faculty/economics.html
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:ncs:wpaper:010. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Theofanis Tsoulouhas)The email address of this maintainer does not seem to be valid anymore. Please ask Theofanis Tsoulouhas to update the entry or send us the correct address
If references are entirely missing, you can add them using this form.