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Semiparametric estimation of risk-return relationships

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dc.affiliation.dptoUC3M. Departamento de Economíaes
dc.contributor.authorEscanciano, Juan Carlos
dc.contributor.authorPardo-Fernandez, Juan Carlos
dc.contributor.authorVan Keilegom, Ingrid
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.contributor.funderEuropean Commissionen
dc.date.accessioned2022-06-13T17:00:43Z
dc.date.available2022-06-13T17:00:43Z
dc.date.issued2017-01-04
dc.description.abstractThis article proposes semiparametric generalized least-squares estimation of parametric restrictions between the conditional mean and the conditional variance of excess returns given a set of parametric factors. A distinctive feature of our estimator is that it does not require a fully parametric model for the conditional mean and variance. We establish consistency and asymptotic normality of the estimates. The theory is nonstandard due to the presence of estimated factors. We provide sufficient conditions for the estimated factors not to have an impact in the asymptotic standard error of estimators. A simulation study investigates the finite sample performance of the estimates. Finally, an application to the CRSP value-weighted excess returns highlights the merits of our approach. In contrast to most previous studies using nonparametric estimates, we find a positive and significant price of risk in our semiparametric setting.en
dc.description.sponsorshipThe first author acknowledges research support from the Spanish Plan Nacional de I+D+I, reference number ECO2014-55858-P. The second author acknowledges research support from the Ministerio de Economía y Competitividad (grant MTM2014-55966-P). The third author acknowledges research support from the European Research Council under the European Community's Seventh Framework Programme (FP7/2007–2013)/ERC Grant agreement No. 203650, from IAP research network grant no. P7/06 of the Belgian government (Belgian Science Policy), and from the contract “Projet d’Actions de Recherche Concerté es” (ARC) 11/16-039 of the “Communauté française de Belgique” (granted by the “Académie universitaire Louvain”).en
dc.identifier.bibliographicCitationEscanciano, J. C., Pardo-Fernández, J. C., & Van Keilegom, I. (2017). Semiparametric Estimation of Risk–Return Relationships.Journal of Business & Economic Statistics, 35 (1), pp. 40-52.en
dc.identifier.doihttps://doi.org/10.1080/07350015.2015.1052879
dc.identifier.issn0735-0015
dc.identifier.publicationfirstpage40es
dc.identifier.publicationissue1es
dc.identifier.publicationlastpage52es
dc.identifier.publicationtitleJOURNAL OF BUSINESS & ECONOMIC STATISTICSen
dc.identifier.publicationvolume35es
dc.identifier.urihttps://hdl.handle.net/10016/35098
dc.identifier.uxxiAR/0000029584
dc.language.isoenges
dc.publisherTaylor & Francises
dc.relation.projectIDGobierno de España. ECO2014-55858-Pes
dc.relation.projectIDGobierno de España. MTM2014-55966-Pes
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/FP7/203650es
dc.rights© 2017 American Statistical Associationen
dc.subject.ecienciaEconomíaes
dc.subject.otherAsset pricingen
dc.subject.otherConditional varianceen
dc.subject.otherKernel estimationen
dc.subject.otherNonparametric inferenceen
dc.subject.otherRisk premiumen
dc.subject.otherVolatilityen
dc.titleSemiparametric estimation of risk-return relationshipsen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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