Heteroskedasticity in Stock Returns
34 Pages Posted: 15 Jan 2007 Last revised: 18 Apr 2022
Date Written: May 1989
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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