Factor models of stock returns: GARCH errors versus time-varying betas

Phoebe Koundouri (), Nikolaos Kourogenis, Nikitas Pittis and Panagiotis Samartzis

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: This paper investigates the implications of time-varying betas in factor models for stock returns. It is shown that a single-factor model (SFMT) with autoregressive betas and homoscedastic errors (SFMT-AR) is capable of reproducing the most important stylized facts of stock returns. An empirical study on the major US stock market sectors shows that SFMT-AR outperforms, in terms of in-sample and out-of-sample performance, SFMT with constant betas and conditionally heteroscedastic (GARCH) errors, as well as two multivariate GARCH-type models.

Keywords: autoregressive beta; stock returns; single factor model; conditional heteroscedasticity; in-sample performance; out-of-sample performance (search for similar items in EconPapers)
JEL-codes: C22 G10 G11 G12 (search for similar items in EconPapers)
Date: 2016-01-14
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Citations: View citations in EconPapers (1)

Published in Journal of Forecasting, 14, January, 2016, 35(5), pp. 445-461. ISSN: 0277-6693

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http://eprints.lse.ac.uk/65548/ Open access version. (application/pdf)

Related works:
Journal Article: Factor Models of Stock Returns: GARCH Errors versus Time‐Varying Betas (2016)
Working Paper: Factor Models of Stock Returns: GARCH Errors versus Time - Varying Betas (2014)
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