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dc.contributor.authorMencía, Javier
dc.contributor.authorSentana, Enrique
dc.date.accessioned2019-08-10T17:55:04Z
dc.date.available2019-08-10T17:55:04Z
dc.date.issued2009-06-02
dc.identifier.issnISSN: 0213-2710 (en papel)
dc.identifier.issnISSN: 1579-8666 (en línea)
dc.identifier.urihttps://repositorio.bde.es/handle/123456789/6988
dc.description.abstractWe show that the distribution of any portfolio whose components jointly follow a location-scale mixture of normals can be characterised solely by its mean, variance and skewness. Under this distributional assumption, we derive the mean-variance-skewness frontier in closed form, and show that it can be spanned by three funds. For practical purposes, we derive a standardised distribution, provide analytical expressions for the log-likelihood score and explain how to evaluate the information matrix. Finally, we present an empirical application in which we obtain the mean-variance-skewness frontier generated by the ten Datastream US sectoral indices, and conduct spanning tests
dc.format.extent51 p. : tab.
dc.language.isoeng
dc.publisherBanco de España
dc.relation.ispartofDocumentos de Trabajo / Banco de España, 0909
dc.rightsReconocimiento-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0)
dc.rightsIn Copyright - Non Commercial Use Permitted
dc.rights.urihttps://creativecommons.org/licenses/by-nc-sa/4.0/deed.es_ES
dc.rights.urihttp://rightsstatements.org/vocab/InC-NC/1.0/
dc.subjectGeneralised hyperbolic distribution
dc.subjectMaximum likelihood
dc.subjectPortfolio frontiers
dc.subjectSortino ratio
dc.subjectSpanning tests
dc.subjectTail dependence
dc.titleMultivariate location-scale mixtures of normals and mean-variance-skewness portfolio allocation
dc.typeDocumento de trabajo
dc.identifier.bdebib000229553
dc.identifier.bdepubDTRA-200909-eng
dc.subject.bdeModelos de series temporales
dc.subject.bdeModelización econométrica
dc.subject.bdeValoración de activos
dc.publisher.bdeMadrid : Banco de España, 2009
dc.subject.jelC52
dc.subject.jelC32
dc.subject.jelG11
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