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Discrete-Time Approximation and Monte-Carlo Simulation of Backward Stochastic Differential Equations

dc.contributor.authorTouzi, Nizar
dc.contributor.authorBouchard, Bruno
dc.date.accessioned2009-09-21T09:17:00Z
dc.date.available2009-09-21T09:17:00Z
dc.date.issued2004
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/1804
dc.language.isoenen
dc.subjectMéthode de Monte-Carloen
dc.subjectNumerical Probabilityen
dc.subject.ddc519en
dc.titleDiscrete-Time Approximation and Monte-Carlo Simulation of Backward Stochastic Differential Equationsen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenWe suggest a discrete-time approximation for decoupled forward–backward stochastic differential equations. The Lp norm of the error is shown to be of the order of the time step. Given a simulation-based estimator of the conditional expectation operator, we then suggest a backward simulation scheme, and we study the induced Lp error. This estimate is more investigated in the context of the Malliavin approach for the approximation of conditional expectations. Extensions to the reflected case are also considered. Author Keywords: Monte-Carlo methods for (reflected) forward–backward SDEs; Malliavin calculus; Regression estimationen
dc.relation.isversionofjnlnameStochastic Processes and their Applications
dc.relation.isversionofjnlvol111en
dc.relation.isversionofjnlissue2en
dc.relation.isversionofjnldate2004-06
dc.relation.isversionofjnlpages175-206en
dc.relation.isversionofdoihttp://dx.doi.org/10.1016/j.spa.2004.01.001en
dc.description.sponsorshipprivateouien
dc.relation.isversionofjnlpublisherElsevieren
dc.subject.ddclabelProbabilités et mathématiques appliquéesen


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