Heteroskedasticity in Stock Returns

Authors

  • G. WILLIAM SCHWERT,

  • PAUL J. SEGUIN

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    • G. W. Schwert is from William E. Simon Graduate School of Business Administration, University of Rochester, and National Bureau of Economic Research. P. J. Seguin is from William E. Simon Graduate School of Business Administration, University of Rochester. We received helpful comments from Robert Engle, Michael Gibbons, Campbell Harvey, James Poterba, René Stulz, and two anonymous referees. Support from the Bradley Policy Research Center at the Univerity of Rochester and from the Olin Foundation is gratefully acknowledged.


ABSTRACT

We use predictions of aggregate stock return variances from daily data to estimate time-varying monthly variances for size-ranked portfolios. We propose and estimate a single factor model of heteroskedasticity for portfolio returns. This model implies time-varying betas. Implications of heteroskedasticity and time-varying betas for tests of the capital asset pricing model (CAPM) are then documented. Accounting for heteroskedasticity increases the evidence that risk-adjusted returns are related to firm size. We also estimate a constant correlation model. Portfolio volatilities predicted by this model are similar to those predicted by more complex multivariate generalized-autoregressive-conditional-heteroskedasticity (GARCH) procedures.

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