Evaluating value-at-risk models with desk-level data / Jeremy Berkowitz in Management science, Vol. 57 N° 12 (Décembre 2011)  

Public ISBD [article]  inManagement science > Vol. 57 N° 12 (Décembre 2011) . - pp. 2213-2227 Titre : Evaluating value-at-risk models with desk-level data Type de document : texte imprimé Auteurs : Jeremy Berkowitz, Auteur ; Peter Christoffersen ; Denis Pelletier, Auteur Année de publication : 2012 Article en page(s) : pp. 2213-2227 Note générale : Management Langues : Anglais (eng) Mots-clés : Risk management Backtesting Volatility Disclosure Résumé : 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. DEWEY : 658 ISSN : 0025-1909 En ligne : http://mansci.journal.informs.org/content/57/12/2213.abstract [article] Evaluating value-at-risk models with desk-level data [texte imprimé] / Jeremy Berkowitz, Auteur ; Peter Christoffersen ; Denis Pelletier, Auteur . - 2012 . - pp. 2213-2227. Management Langues : Anglais (eng) in Management science > Vol. 57 N° 12 (Décembre 2011) . - pp. 2213-2227 Mots-clés : Risk management Backtesting Volatility Disclosure Résumé : 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. DEWEY : 658 ISSN : 0025-1909 En ligne : http://mansci.journal.informs.org/content/57/12/2213.abstract

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