Efficient risk estimation via nested sequential simulation / Mark Broadie in Management science, Vol. 57 N° 6 (Juin 2011)  

Public ISBD [article]  inManagement science > Vol. 57 N° 6 (Juin 2011) . - pp. 1172-1194 Titre : Efficient risk estimation via nested sequential simulation Type de document : texte imprimé Auteurs : Mark Broadie, Auteur ; Yiping Du, Auteur ; Ciamac C. Moallemi, Auteur Année de publication : 2011 Article en page(s) : pp. 1172-1194 Note générale : Management Langues : Anglais (eng) Mots-clés : Simulation Decision analysis Risk management Sequential Index. décimale : 658 Organisation des entreprises. Techniques du commerce Résumé : We analyze the computational problem of estimating financial risk in a nested simulation. In this approach, an outer simulation is used to generate financial scenarios, and an inner simulation is used to estimate future portfolio values in each scenario. We focus on one risk measure, the probability of a large loss, and we propose a new algorithm to estimate this risk. Our algorithm sequentially allocates computational effort in the inner simulation based on marginal changes in the risk estimator in each scenario. Theoretical results are given to show that the risk estimator has a faster convergence order compared to the conventional uniform inner sampling approach. Numerical results consistent with the theory are presented. DEWEY : 658 ISSN : 0025-1909 En ligne : http://mansci.journal.informs.org/content/57/6.toc [article] Efficient risk estimation via nested sequential simulation [texte imprimé] / Mark Broadie, Auteur ; Yiping Du, Auteur ; Ciamac C. Moallemi, Auteur . - 2011 . - pp. 1172-1194. Management Langues : Anglais (eng) in Management science > Vol. 57 N° 6 (Juin 2011) . - pp. 1172-1194 Mots-clés : Simulation Decision analysis Risk management Sequential Index. décimale : 658 Organisation des entreprises. Techniques du commerce Résumé : We analyze the computational problem of estimating financial risk in a nested simulation. In this approach, an outer simulation is used to generate financial scenarios, and an inner simulation is used to estimate future portfolio values in each scenario. We focus on one risk measure, the probability of a large loss, and we propose a new algorithm to estimate this risk. Our algorithm sequentially allocates computational effort in the inner simulation based on marginal changes in the risk estimator in each scenario. Theoretical results are given to show that the risk estimator has a faster convergence order compared to the conventional uniform inner sampling approach. Numerical results consistent with the theory are presented. DEWEY : 658 ISSN : 0025-1909 En ligne : http://mansci.journal.informs.org/content/57/6.toc

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