Evaluating the accuracy of time-varying beta. The evidence from Poland

Barbara Będowska-Sójka


This paper empirically investigates various approaches to model time-varying systematic risk on the Polish capital market. A plenty of methods is examined in the developed markets and the Kalman filter approach is usually indicated as the best method for estimation of time-varying beta. However, there exists a gap in the studies for the emerging markets. In the paper we apply weekly data of fifteen stocks listed on the Warsaw Stock Exchange from banking and informatics sector. The sample starts at the beginning of 2001 and ends in 2015 including the hectic crisis period. We estimate beta within few competing approaches: two MGARCH models, BEKK and DCC, unobserved component model, and static beta from linear regression. All beta estimates are compared in the securities market line framework. We find that unobserved component beta together with beta from DCC model have higher predictive accuracy than beta from BEKK model or static beta. The beta estimates are positively correlated within the industry and negatively correlated for stocks from different sectors. Finally, the prediction of beta coefficients are more accurate for stocks from banking sector than for IT companies.
Author Barbara Będowska-Sójka (WIiGE / KE)
Barbara Będowska-Sójka,,
- Department of Econometrics
Journal seriesDynamic Econometric Models, ISSN 1234-3862, (B 13 pkt)
Issue year2017
Publication size in sheets0.75
Keywords in EnglishBEKK, DCC, Kalman filter, MGARCH, time-varying beta
URL http://apcz.umk.pl/czasopisma/index.php/DEM/article/view/DEM.2017.010/14208
Languageen angielski
Score (nominal)13
Score sourcejournalList
ScoreMinisterial score = 13.0, 11-03-2020, ArticleFromJournal
Citation count*3 (2020-09-23)
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