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Portfolio optimization under a generalized hyperbolic skewed t distribution and exponential utility

dc.contributor.authorBirge, John R.
dc.contributor.authorChavez Bedoya Mercado, Luis Carlos
dc.date.accessioned2025-08-11T16:44:48Z
dc.date.issued2016
dc.description.abstractIn this paper, we show that if asset returns follow a generalized hyperbolic skewed t distribution, the investor has an exponential utility function and a riskless asset is available, the optimal portfolio weights can be found either in closed form or using a successive approximation scheme. We also derive lower bounds for the certainty equivalent return generated by the optimal portfolios. Finally, we present a study of the performance of mean–variance analysis and Taylor’s series expected utility expansion (up to the fourth moment) to compute optimal portfolios in this framework. © 2016 Informa UK Limited, trading as Taylor & Francis Group.
dc.identifier.doi10.1080/14697688.2015.1113307
dc.identifier.scopus2-s2.0-84955087434
dc.identifier.urihttps://cris.esan.edu.pe/handle/20.500.12640/893
dc.identifier.uuida460b417-a4c5-4052-bc33-b358ae17afee
dc.language.isoen
dc.publisherRoutledge
dc.relation.citationissue7
dc.relation.ispartofQuantitative Finance
dc.rightshttp://purl.org/coar/access_right/c_14cb
dc.subjectMean–variance
dc.subjectPortfolio optimization
dc.subjectSkewed t distribution
dc.titlePortfolio optimization under a generalized hyperbolic skewed t distribution and exponential utility
dc.typehttp://purl.org/coar/resource_type/c_2f33
dspace.entity.typePublication
oaire.citation.endPage1036
oaire.citation.startPage1019
organization.acronymUESAN
organization.identifier.ruc20136507720
organization.identifier.uuid8ea1bac9-00cb-495d-95f2-d5ff6637689d
person.affiliation.nameUNIVERSIDAD ESAN
person.identifier.orcid0000-0002-0992-9495
person.identifier.uuid2b2bb252-8895-4044-b56e-8a3e66bc1a7c
relation.isAuthorOfPublication2b2bb252-8895-4044-b56e-8a3e66bc1a7c
relation.isAuthorOfPublication.latestForDiscovery2b2bb252-8895-4044-b56e-8a3e66bc1a7c

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