Documents disponibles écrits par cet auteur

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

Public ISBD [article]  in Management 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

The shape and term structure of the index option smirk / Peter Christoffersen in Management science, Vol. 55 N° 12 (Décembre 2009) 

Public ISBD [article]  in Management science > Vol. 55 N° 12 (Décembre 2009) . - pp. 1914-1932 Titre : The shape and term structure of the index option smirk : why multifactor stochastic volatility models work so well Type de document : texte imprimé Auteurs : Peter Christoffersen, Auteur ; Steven Heston, Auteur ; Kris Jacobs, Auteur Article en page(s) : pp. 1914-1932 Note générale : Gestion Langues : Anglais (eng) Mots-clés : Stochastic  correlation  Stochastic  volatility  Equity  index  options  Multifactor  model  Persistence  Affine  Out-of-sample Index. décimale : 658 Organisation des entreprises. Techniques du commerce Résumé : State-of-the-art stochastic volatility models generate a "volatility smirk" that explains why out-of-the-money index puts have high prices relative to the Black-Scholes benchmark. These models also adequately explain how the volatility smirk moves up and down in response to changes in risk. However, the data indicate that the slope and the level of the smirk fluctuate largely independently. Although single-factor stochastic volatility models can capture the slope of the smirk, they cannot explain such largely independent fluctuations in its level and slope over time. We propose to model these movements using a two-factor stochastic volatility model. Because the factors have distinct correlations with market returns, and because the weights of the factors vary over time, the model generates stochastic correlation between volatility and stock returns. Besides providing more flexible modeling of the time variation in the smirk, the model also provides more flexible modeling of the volatility term structure. Our empirical results indicate that the model improves on the benchmark Heston stochastic volatility model by 24% in-sample and 23% out-of-sample. The better fit results from improvements in the modeling of the term structure dimension as well as the moneyness dimension. En ligne : http://mansci.journal.informs.org/cgi/content/abstract/55/12/1914?maxtoshow=&hit [...] [article] The shape and term structure of the index option smirk : why multifactor stochastic volatility models work so well [texte imprimé] / Peter Christoffersen, Auteur ; Steven Heston, Auteur ; Kris Jacobs, Auteur . - pp. 1914-1932. Gestion Langues : Anglais (eng) in Management science > Vol. 55 N° 12 (Décembre 2009) . - pp. 1914-1932 Mots-clés : Stochastic  correlation  Stochastic  volatility  Equity  index  options  Multifactor  model  Persistence  Affine  Out-of-sample Index. décimale : 658 Organisation des entreprises. Techniques du commerce Résumé : State-of-the-art stochastic volatility models generate a "volatility smirk" that explains why out-of-the-money index puts have high prices relative to the Black-Scholes benchmark. These models also adequately explain how the volatility smirk moves up and down in response to changes in risk. However, the data indicate that the slope and the level of the smirk fluctuate largely independently. Although single-factor stochastic volatility models can capture the slope of the smirk, they cannot explain such largely independent fluctuations in its level and slope over time. We propose to model these movements using a two-factor stochastic volatility model. Because the factors have distinct correlations with market returns, and because the weights of the factors vary over time, the model generates stochastic correlation between volatility and stock returns. Besides providing more flexible modeling of the time variation in the smirk, the model also provides more flexible modeling of the volatility term structure. Our empirical results indicate that the model improves on the benchmark Heston stochastic volatility model by 24% in-sample and 23% out-of-sample. The better fit results from improvements in the modeling of the term structure dimension as well as the moneyness dimension. En ligne : http://mansci.journal.informs.org/cgi/content/abstract/55/12/1914?maxtoshow=&hit [...]