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Time-Varying Market Beta: Does the estimation methodology matter?
dc.contributor.author | Nieto, Belén |
dc.contributor.author | Orbe, Susan |
dc.contributor.author | Zarraga, Ainoha |
dc.date.accessioned | 2016-07-20T09:49:14Z |
dc.date.available | 2016-07-20T09:49:14Z |
dc.date.issued | 2014-06-12 |
dc.identifier.citation | Nieto, 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.issn | 1696-2281 |
dc.identifier.uri | http://hdl.handle.net/2117/88929 |
dc.description.abstract | This 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.extent | 30 p. |
dc.language.iso | eng |
dc.publisher | Institut d'Estadística de Catalunya |
dc.relation.ispartof | SORT. 2014, Vol. 38, Núm. 1 |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 Spain |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
dc.subject | Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica |
dc.subject.other | Time-varying beta |
dc.subject.other | nonparametric estimator |
dc.subject.other | GARCH-based beta estimator |
dc.subject.other | Kalman filter |
dc.title | Time-Varying Market Beta: Does the estimation methodology matter? |
dc.type | Article |
dc.description.peerreviewed | Peer Reviewed |
dc.subject.ams | Classificació AMS::62 Statistics::62M Inference from stochastic processes |
dc.subject.ams | Classificació AMS::62 Statistics::62J Linear inference, regression |
dc.subject.ams | Classificació AMS::62 Statistics::62G Nonparametric inference |
dc.rights.access | Open Access |
local.citation.publicationName | SORT |
local.citation.volume | 38 |
local.citation.number | 1 |
local.citation.startingPage | 13 |
local.citation.endingPage | 42 |