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dc.contributor.authorTan, Xiaolu
dc.date.accessioned2014-08-27T08:18:37Z
dc.date.available2014-08-27T08:18:37Z
dc.date.issued2014
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/13842
dc.language.isoenen
dc.subjectinvariance principleen
dc.subjectweak convergenceen
dc.subjectpath-dependent stochastic controlen
dc.subjectNumerical schemeen
dc.subject.ddc519en
dc.titleDiscrete-time probabilistic approximation of path-dependent stochastic control problemsen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenWe give a probabilistic interpretation of the Monte Carlo scheme proposed by Fahim, Touzi and Warin [Ann. Appl. Probab. 21 (2011) 1322–1364] for fully nonlinear parabolic PDEs, and hence generalize it to the path-dependent (or non-Markovian) case for a general stochastic control problem. A general convergence result is obtained by a weak convergence method in the spirit of Kushner and Dupuis [Numerical Methods for Stochastic Control Problems in Continuous Time (1992) Springer]. We also get a rate of convergence using the invariance principle technique as in Dolinsky [Electron. J. Probab. 17 (2012) 1–5], which is better than that obtained by viscosity solution method. Finally, by approximating the conditional expectations arising in the numerical scheme with simulation-regression method, we obtain an implementable scheme.en
dc.relation.isversionofjnlnameThe Annals of Applied Probability
dc.relation.isversionofjnlvol24en
dc.relation.isversionofjnlissue5en
dc.relation.isversionofjnldate2014
dc.relation.isversionofjnlpages1803-1834en
dc.relation.isversionofdoihttp://dx.doi.org/10.1214/13-AAP963en
dc.relation.isversionofjnlpublisherInstitute of Mathematical Statisticsen
dc.subject.ddclabelProbabilités et mathématiques appliquéesen
dc.relation.forthcomingnonen
dc.relation.forthcomingprintnonen


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