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hal.structure.identifierCEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
dc.contributor.authorBouchard, Bruno*
hal.structure.identifierNatixis Asset Management
dc.contributor.authorReghai, Adil*
hal.structure.identifierCEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
dc.contributor.authorVirrion, Benjamin*
dc.date.accessioned2020-06-12T09:50:25Z
dc.date.available2020-06-12T09:50:25Z
dc.date.issued2021
dc.identifier.issn1469-7688
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/20880
dc.language.isoenen
dc.subjectranking and selection
dc.subjectsequential design
dc.subjectExpected Shortfall
dc.subjectBayesian filter
dc.subject.ddc332en
dc.subject.classificationjelG.G0.G00en
dc.titleComputation of Expected Shortfall by fast detection of worst scenarios
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenWe consider multi-step algorithms for the computation of the historical expected shortfall. At each step of the algorithms, we use Monte Carlo simulations to reduce the number of historical scenarios that potentially belong to the set of worst-case scenarios. We show how this can be optimized by either solving a simple deterministic dynamic programming algorithm or in an adaptive way by using a stochastic dynamic programming procedure under a given prior. We prove Lp-error bounds and numerical tests are performed.
dc.publisher.cityParisen
dc.relation.isversionofjnlnameQuantitative Finance
dc.relation.isversionofjnldate2021
dc.relation.isversionofjnlpages1-22
dc.relation.isversionofdoi10.1080/14697688.2021.1880618
dc.relation.isversionofjnlpublisherTaylor & Francis
dc.subject.ddclabelEconomie financièreen
dc.description.ssrncandidatenon
dc.description.halcandidatenon
dc.description.readershiprecherche
dc.description.audienceInternational
dc.relation.Isversionofjnlpeerreviewedoui
dc.date.updated2021-03-29T08:34:03Z
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