Factor Models of Stock Returns: GARCH Errors versus Autoregressive Betas
Nikitas Pittis (),
Nikolaos Kourogenis () and
Phoebe Koundouri ()
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Nikitas Pittis: University of Piraeus, Greece
No 1318, DEOS Working Papers from Athens University of Economics and Business
The Single Factor Model (SFMT) of stock returns in its simplest form, namely the one that assumes time-invariant beta and homoskedastic error has been found to be empirically inadequate.The beta coefficient and the error process exhibit signiÃƒï¿½Ã¯Â¿Â½cant time-variation and dynamic conditional heteroskedasticity, respectively. Out of these empirical failures, two extended versions of SFMT have emerged: the Ãƒï¿½Ã¯Â¿Â½first (SFMT-AR) assumes that the beta coefficient is an autoregressive process, whereas the second (SFMT-GARCH) maintains the assumption of time-invariant beta but assumes that the error follows a GARCH process. The purpose of this paper is twofold: fiÃƒï¿½Ã¯Â¿Â½rst to show that SFMT-AR is capable of reproducing the most important stylized facts of stock returns, namely conditional heteroskedasticity and leptokurtosis, even in the case in which the factor is an independent process; second to compare SFMT-GARCH and SFMT-AR in terms of their in-sample and out-of-sample performance. The most important result from these comparisons is that SFMT-AR dominates SFMT-GARCH in terms of forecasting the second moments of stock returns.
Keywords: autoregressive beta; stock returns; single factor model; conditional heteroscedasticity (search for similar items in EconPapers)
JEL-codes: C22 G10 G11 G12 (search for similar items in EconPapers)
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