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dc.contributor.authorBouchard, Bruno
dc.contributor.authorTan, Xiaolu
dc.contributor.authorWarin, Xavier
dc.contributor.authorZou, Yiyi
dc.date.accessioned2017-12-15T14:45:19Z
dc.date.available2017-12-15T14:45:19Z
dc.date.issued2017
dc.identifier.issn0929-9629
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/17241
dc.language.isoenen
dc.subjectBSDEen
dc.subjectMonte Carlo methodsen
dc.subjectbranching processen
dc.subject.ddc519en
dc.titleNumerical approximation of BSDEs using local polynomial drivers and branching processesen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenWe propose a new numerical scheme for Backward Stochastic Differential Equations based on branching processes. We approximate an arbitrary (Lipschitz) driver by local polynomials and then use a Picard iteration scheme. Each step of the Picard iteration can be solved by using a representation in terms of branching diffusion systems , thus avoiding the need for a fine time discretization. In contrast to the previous literature on the numerical resolution of BSDEs based on branching processes, we prove the convergence of our numerical scheme without limitation on the time horizon. Numerical simulations are provided to illustrate the performance of the algorithm.en
dc.relation.isversionofjnlnameMonte Carlo Methods and Applications
dc.relation.isversionofjnldate2017
dc.relation.isversionofdoi10.1515/mcma-2017-0116en
dc.relation.isversionofjnlpublisherDe Gruyteren
dc.subject.ddclabelProbabilités et mathématiques appliquéesen
dc.relation.forthcomingnonen
dc.relation.forthcomingprintnonen
dc.description.ssrncandidatenonen
dc.description.halcandidatenonen
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.relation.Isversionofjnlpeerreviewedouien
dc.relation.Isversionofjnlpeerreviewedouien
dc.date.updated2017-12-15T14:27:01Z
hal.person.labIds60
hal.person.labIds60
hal.person.labIds26554
hal.person.labIds60


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