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Factor Models of Stock Returns: GARCH Errors versus Autoregressive Betas

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  • Panagiotis Samartzis
  • Nikitas Pittis

    ()
    (University of Piraeus, Greece)

  • Nikolaos Kourogenis

    ()
    (Department of Banking and Financial Management, University of Piraeus.)

  • Phoebe Koundouri

Abstract

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 signi��cant time-variation and dynamic conditional heteroskedasticity, respectively. Out of these empirical failures, two extended versions of SFMT have emerged: the ��first (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: fi��rst 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.

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Paper provided by Athens University of Economics and Business in its series DEOS Working Papers with number 1318.

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Handle: RePEc:aue:wpaper:1318

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Keywords: autoregressive beta; stock returns; single factor model; conditional heteroscedasticity;

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