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2009-05-05Buch DOI: 10.18452/4191
Regression methods for stochastic control problems and their convergence analysis
dc.contributor.authorBelomestny, Denis
dc.contributor.authorKolodko, Anastasia
dc.contributor.authorSchoenmakers, John
dc.date.accessioned2017-06-15T23:55:32Z
dc.date.available2017-06-15T23:55:32Z
dc.date.created2009-05-20
dc.date.issued2009-05-05
dc.identifier.issn1860-5664
dc.identifier.urihttp://edoc.hu-berlin.de/18452/4843
dc.description.abstractIn this paper we develop several regression algorithms for solving general stochastic optimal control problems via Monte Carlo. This type of algorithms is particularly useful for problems with a highdimensional state space and complex dependence structure of the underlying Markov process with respect to some control. The main idea behind the algorithms is to simulate a set of trajectories under some reference measure and to use the Bellman principle combined with fast methods for approximating conditional expectations and functional optimization. Theoretical properties of the presented algorithms are investigated and the convergence to the optimal solution is proved under some assumptions. Finally, the presented methods are applied in a numerical example of a high-dimensional controlled Bermudan basket option in a financial market with a large investor.eng
dc.language.isoeng
dc.publisherHumboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectOptimal stochastic controleng
dc.subjectRegression methodseng
dc.subjectConvergence analysiseng
dc.subject.ddc330 Wirtschaft
dc.titleRegression methods for stochastic control problems and their convergence analysis
dc.typebook
dc.identifier.urnurn:nbn:de:kobv:11-10097759
dc.identifier.doihttp://dx.doi.org/10.18452/4191
local.edoc.container-titleSonderforschungsbereich 649: Ökonomisches Risiko
local.edoc.pages34
local.edoc.type-nameBuch
local.edoc.container-typeseries
local.edoc.container-type-nameSchriftenreihe
local.edoc.container-volume2009
local.edoc.container-issue26
local.edoc.container-year2009
local.edoc.container-erstkatid2195055-6

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