Publication: Semiparametric estimation of risk-return relationships
dc.affiliation.dpto | UC3M. Departamento de Economía | es |
dc.contributor.author | Escanciano, Juan Carlos | |
dc.contributor.author | Pardo-Fernandez, Juan Carlos | |
dc.contributor.author | Van Keilegom, Ingrid | |
dc.contributor.funder | Ministerio de Economía y Competitividad (España) | es |
dc.contributor.funder | European Commission | en |
dc.date.accessioned | 2022-06-13T17:00:43Z | |
dc.date.available | 2022-06-13T17:00:43Z | |
dc.date.issued | 2017-01-04 | |
dc.description.abstract | This 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.sponsorship | The 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.bibliographicCitation | Escanciano, 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.doi | https://doi.org/10.1080/07350015.2015.1052879 | |
dc.identifier.issn | 0735-0015 | |
dc.identifier.publicationfirstpage | 40 | es |
dc.identifier.publicationissue | 1 | es |
dc.identifier.publicationlastpage | 52 | es |
dc.identifier.publicationtitle | JOURNAL OF BUSINESS & ECONOMIC STATISTICS | en |
dc.identifier.publicationvolume | 35 | es |
dc.identifier.uri | https://hdl.handle.net/10016/35098 | |
dc.identifier.uxxi | AR/0000029584 | |
dc.language.iso | eng | es |
dc.publisher | Taylor & Francis | es |
dc.relation.projectID | Gobierno de España. ECO2014-55858-P | es |
dc.relation.projectID | Gobierno de España. MTM2014-55966-P | es |
dc.relation.projectID | info:eu-repo/grantAgreement/EC/FP7/203650 | es |
dc.rights | © 2017 American Statistical Association | en |
dc.subject.eciencia | Economía | es |
dc.subject.other | Asset pricing | en |
dc.subject.other | Conditional variance | en |
dc.subject.other | Kernel estimation | en |
dc.subject.other | Nonparametric inference | en |
dc.subject.other | Risk premium | en |
dc.subject.other | Volatility | en |
dc.title | Semiparametric estimation of risk-return relationships | en |
dc.type | research article | * |
dc.type.hasVersion | AM | * |
dspace.entity.type | Publication |
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