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dc.contributor.authorDouc, Randal
dc.contributor.authorGuillin, Arnaud
HAL ID: 5334
dc.contributor.authorMarin, Jean-Michel
HAL ID: 9121
ORCID: 0000-0001-7451-9719
dc.contributor.authorRobert, Christian P.
dc.date.accessioned2011-06-25T09:09:37Z
dc.date.available2011-06-25T09:09:37Z
dc.date.issued2007
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/6610
dc.language.isoenen
dc.subjectAdaptivityen
dc.subjectCox-Ingersoll-Ross modelen
dc.subjectEuler schemeen
dc.subjectimportance samplingen
dc.subjectmathematical financeen
dc.subjectmixturesen
dc.subjectmoderate deviationsen
dc.subjectpopulation Monte Carloen
dc.subjectvariance reductionen
dc.subject.ddc519en
dc.titleMinimum variance importance sampling via Population Monte Carloen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenVariance reduction has always been a central issue in Monte Carlo experiments. Population Monte Carlo can be used to this effect, in that a mixture of importance functions, called a D-kernel, can be iteratively optimized to achieve the minimum asymptotic variance for a function of interest among all possible mixtures. The implementation of this iterative scheme is illustrated for the computation of the price of a European option in the Cox-Ingersoll-Ross model. A Central Limit theorem as well as moderate deviations are established for the D-kernel Population Monte Carlo methodology.
en
dc.relation.isversionofjnlnameESAIM. Probability and Statistics
dc.relation.isversionofjnlvol11en
dc.relation.isversionofjnldate2007
dc.relation.isversionofjnlpages427-447en
dc.relation.isversionofdoihttp://dx.doi.org/10.1051/ps:2007028en
dc.description.sponsorshipprivateouien
dc.relation.isversionofjnlpublisherCambridge University Pressen
dc.subject.ddclabelProbabilités et mathématiques appliquéesen


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