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2008-05-27Buch DOI: 10.18452/4130
Dynamic Semiparametric Factor Models in RiskNeutral Density Estimation
dc.contributor.authorGiacomini, Enzo
dc.contributor.authorHärdle, Wolfgang
dc.contributor.authorKrätschmer, Volker
dc.date.accessioned2017-06-15T23:43:13Z
dc.date.available2017-06-15T23:43:13Z
dc.date.created2008-05-28
dc.date.issued2008-05-27
dc.identifier.issn1860-5664
dc.identifier.urihttp://edoc.hu-berlin.de/18452/4782
dc.description.abstractDimension reduction techniques for functional data analysis model and approximate smooth random functions by lower dimensional objects. In many applications the focus of interest lies not only in dimension reduction but also in the dynamic behaviour of the lower dimensional objects. The most prominent dimension reduction technique - functional principal components analysis - however, does not model time dependences embedded in functional data. In this paper we use dynamic semiparametric factor models (DSFM) to reduce dimensionality and analyse the dynamic structure of unknown random functions by means of inference based on their lower dimensional representation. We apply DSFM to estimate the dynamic structure of risk neutral densities implied by prices of option on the DAX stock index.eng
dc.language.isoeng
dc.publisherHumboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät
dc.subjectdynamic factor modelseng
dc.subjectdimension reductioneng
dc.subjectrisk neutral densityeng
dc.subject.ddc330 Wirtschaft
dc.titleDynamic Semiparametric Factor Models in RiskNeutral Density Estimation
dc.typebook
dc.identifier.urnurn:nbn:de:kobv:11-10088640
dc.identifier.doihttp://dx.doi.org/10.18452/4130
local.edoc.container-titleSonderforschungsbereich 649: Ökonomisches Risiko
local.edoc.pages19
local.edoc.type-nameBuch
local.edoc.container-typeseries
local.edoc.container-type-nameSchriftenreihe
local.edoc.container-volume2008
local.edoc.container-issue38
local.edoc.container-year2008
local.edoc.container-erstkatid2195055-6

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