dc.contributor.author |
Nieto, Belén |
dc.contributor.author |
Orbe, Susan |
dc.contributor.author |
Zarraga, Ainhoa |
dc.date |
2014 |
dc.identifier |
https://ddd.uab.cat/record/118907 |
dc.identifier |
urn:oai:ddd.uab.cat:118907 |
dc.identifier |
urn:articleid:20138830v38n1p13 |
dc.identifier |
urn:oai:raco.cat:article/277216 |
dc.format |
application/pdf |
dc.language |
eng |
dc.publisher |
|
dc.relation |
SORT : statistics and operations research transactions ; Vol. 38, Núm. 1 (January-June 2014), p. 13-42 |
dc.rights |
open access |
dc.rights |
Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades. |
dc.rights |
https://creativecommons.org/licenses/by-nc-nd/3.0/ |
dc.subject |
Time-varying beta |
dc.subject |
Nonparametric estimator |
dc.subject |
GARCH-based beta estimator |
dc.subject |
Kalman filter |
dc.title |
Time-varying market beta : does the estimation methodology matter? |
dc.type |
Article |
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 between 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. |