Randomized optimal stopping algorithms and their convergence analysis

dc.bibliographicCitation.seriesTitleWIAS Preprintseng
dc.bibliographicCitation.volume2697
dc.contributor.authorBayer, Christian
dc.contributor.authorBelomestny, Denis
dc.contributor.authorHager, Paul
dc.contributor.authorPigato, Paolo
dc.contributor.authorSchoenmakers, John G. M.
dc.date.accessioned2022-06-30T12:42:34Z
dc.date.available2022-06-30T12:42:34Z
dc.date.issued2020
dc.description.abstractIn this paper we study randomized optimal stopping problems and consider corresponding forward and backward Monte Carlo based optimization algorithms. In particular we prove the convergence of the proposed algorithms and derive the corresponding convergence rates.eng
dc.description.versionpublishedVersioneng
dc.identifier.urihttps://oa.tib.eu/renate/handle/123456789/9347
dc.identifier.urihttps://doi.org/10.34657/8385
dc.language.isoeng
dc.publisherBerlin : Weierstraß-Institut für Angewandte Analysis und Stochastik
dc.relation.doihttps://doi.org/10.20347/WIAS.PREPRINT.2697
dc.relation.hasversionhttps://doi.org/10.1137/20M1373876
dc.relation.issn2198-5855
dc.rights.licenseThis document may be downloaded, read, stored and printed for your own use within the limits of § 53 UrhG but it may not be distributed via the internet or passed on to external parties.eng
dc.rights.licenseDieses Dokument darf im Rahmen von § 53 UrhG zum eigenen Gebrauch kostenfrei heruntergeladen, gelesen, gespeichert und ausgedruckt, aber nicht im Internet bereitgestellt oder an Außenstehende weitergegeben werden.ger
dc.subject.ddc510
dc.subject.otherRandomized optimal stoppingeng
dc.subject.otherconvergence rateseng
dc.subject.otherBermudan optionseng
dc.titleRandomized optimal stopping algorithms and their convergence analysiseng
dc.typeReporteng
dc.typeTexteng
dcterms.extent20 S.
tib.accessRightsopenAccess
wgl.contributorWIAS
wgl.subjectMathematik
wgl.typeReport / Forschungsbericht / Arbeitspapier
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