Factor Models of Stock Returns: GARCH Errors versus Time‐Varying Betas
Copyright policy )Factor Models of Stock Returns: GARCH Errors versus Time‐Varying Betas
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. Copyright © 2016 John Wiley & Sons, Ltd.
- London School of Economics and Political Science United Kingdom
- University of Piraeus Greece
- Athens University of Economics and Business Greece
Applications of statistics to actuarial sciences and financial mathematics, in-sample performance, single-factor model, single factor model, out-of-sample performance, autoregressive beta, stock returns, single factor model, conditional heteroscedasticity, in-sample performance, out-of-sample performance, stock market, autoregressive beta, stock returns, homoscedastic errors, conditional heteroscedasticity, autogressive betas, jel: jel:C22, jel: jel:G12, jel: jel:G10, jel: jel:G11
Applications of statistics to actuarial sciences and financial mathematics, in-sample performance, single-factor model, single factor model, out-of-sample performance, autoregressive beta, stock returns, single factor model, conditional heteroscedasticity, in-sample performance, out-of-sample performance, stock market, autoregressive beta, stock returns, homoscedastic errors, conditional heteroscedasticity, autogressive betas, jel: jel:C22, jel: jel:G12, jel: jel:G10, jel: jel:G11
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