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dc.contributor.authorNieto, Belén
dc.contributor.authorOrbe, Susan
dc.contributor.authorZarraga, Ainoha
dc.date.accessioned2016-07-20T09:49:14Z
dc.date.available2016-07-20T09:49:14Z
dc.date.issued2014-06-12
dc.identifier.citationNieto, Belén; Orbe, Susan; Zarraga, Ainoha. Time-Varying Market Beta: Does the estimation methodology matter?. "SORT", 12 Juny 2014, vol. 38, núm. 1, p. 13-42.
dc.identifier.issn1696-2281
dc.identifier.urihttp://hdl.handle.net/2117/88929
dc.description.abstractThis paper compares the performance of nine time-varying beta estimates taken from three different methodologies never previously compared: least-square estimators including nonparametric weights, GARCH-based estimators and Kalman filter estimators. The analysis is applied to the Mexican stock market (2003-2009) because of the high dispersion in betas. The comparison be- tween estimators relies on their financial applications: asset pricing and portfolio management. Results show that Kalman filter estimators with random coefficients outperform the others in capturing both the time series of market risk and their cross-sectional relation with mean returns, while more volatile estimators are better for diversification purposes.
dc.format.extent30 p.
dc.language.isoeng
dc.publisherInstitut d'Estadística de Catalunya
dc.relation.ispartofSORT. 2014, Vol. 38, Núm. 1
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica
dc.subject.otherTime-varying beta
dc.subject.othernonparametric estimator
dc.subject.otherGARCH-based beta estimator
dc.subject.otherKalman filter
dc.titleTime-Varying Market Beta: Does the estimation methodology matter?
dc.typeArticle
dc.description.peerreviewedPeer Reviewed
dc.subject.amsClassificació AMS::62 Statistics::62M Inference from stochastic processes
dc.subject.amsClassificació AMS::62 Statistics::62J Linear inference, regression
dc.subject.amsClassificació AMS::62 Statistics::62G Nonparametric inference
dc.rights.accessOpen Access
local.citation.publicationNameSORT
local.citation.volume38
local.citation.number1
local.citation.startingPage13
local.citation.endingPage42


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