Documents disponibles écrits par cet auteur

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

Public ISBD [article]  in Management 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  Risk  management  Sequential  analysis 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  Risk  management  Sequential  analysis 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

Pathwise optimization for optimal stopping problems / Vijay V. Desai in Management science, Vol. 58 N° 12 (Décembre 2012) 

Public ISBD [article]  in Management science > Vol. 58 N° 12 (Décembre 2012) . - pp. 2292-2308 Titre : Pathwise optimization for optimal stopping problems Type de document : texte imprimé Auteurs : Vijay V. Desai, Auteur ; Vivek F. Farias, Auteur ; Ciamac C. Moallemi, Auteur Année de publication : 2013 Article en page(s) : pp. 2292-2308 Note générale : Management Langues : Anglais (eng) Mots-clés : Dynamic  programming  Optimal  control  Optimal  stopping  American  options  Bermudian  options Résumé : We introduce the pathwise optimization (PO) method, a new convex optimization procedure to produce upper and lower bounds on the optimal value (the “price”) of a high-dimensional optimal stopping problem. The PO method builds on a dual characterization of optimal stopping problems as optimization problems over the space of martingales, which we dub the martingale duality approach. We demonstrate via numerical experiments that the PO method produces upper bounds of a quality comparable with state-of-the-art approaches, but in a fraction of the time required for those approaches. As a by-product, it yields lower bounds (and suboptimal exercise policies) that are substantially superior to those produced by state-of-the-art methods. The PO method thus constitutes a practical and desirable approach to high-dimensional pricing problems. Furthermore, we develop an approximation theory relevant to martingale duality approaches in general and the PO method in particular. Our analysis provides a guarantee on the quality of upper bounds resulting from these approaches and identifies three key determinants of their performance: the quality of an input value function approximation, the square root of the effective time horizon of the problem, and a certain spectral measure of “predictability” of the underlying Markov chain. As a corollary to this analysis we develop approximation guarantees specific to the PO method. Finally, we view the PO method and several approximate dynamic programming methods for high-dimensional pricing problems through a common lens and in doing so show that the PO method dominates those alternatives. ISSN : 0025-1909 En ligne : http://mansci.journal.informs.org/content/58/12/2292.abstract [article] Pathwise optimization for optimal stopping problems [texte imprimé] / Vijay V. Desai, Auteur ; Vivek F. Farias, Auteur ; Ciamac C. Moallemi, Auteur . - 2013 . - pp. 2292-2308. Management Langues : Anglais (eng) in Management science > Vol. 58 N° 12 (Décembre 2012) . - pp. 2292-2308 Mots-clés : Dynamic  programming  Optimal  control  Optimal  stopping  American  options  Bermudian  options Résumé : We introduce the pathwise optimization (PO) method, a new convex optimization procedure to produce upper and lower bounds on the optimal value (the “price”) of a high-dimensional optimal stopping problem. The PO method builds on a dual characterization of optimal stopping problems as optimization problems over the space of martingales, which we dub the martingale duality approach. We demonstrate via numerical experiments that the PO method produces upper bounds of a quality comparable with state-of-the-art approaches, but in a fraction of the time required for those approaches. As a by-product, it yields lower bounds (and suboptimal exercise policies) that are substantially superior to those produced by state-of-the-art methods. The PO method thus constitutes a practical and desirable approach to high-dimensional pricing problems. Furthermore, we develop an approximation theory relevant to martingale duality approaches in general and the PO method in particular. Our analysis provides a guarantee on the quality of upper bounds resulting from these approaches and identifies three key determinants of their performance: the quality of an input value function approximation, the square root of the effective time horizon of the problem, and a certain spectral measure of “predictability” of the underlying Markov chain. As a corollary to this analysis we develop approximation guarantees specific to the PO method. Finally, we view the PO method and several approximate dynamic programming methods for high-dimensional pricing problems through a common lens and in doing so show that the PO method dominates those alternatives. ISSN : 0025-1909 En ligne : http://mansci.journal.informs.org/content/58/12/2292.abstract