CONDITIONAL HETEROSCEDASTICITY IN THE MARKET MODEL AND EFFICIENT ESTIMATES OF BETAS |
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| Authors: | Anil Bera Edward Bubnys Hun Park |
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| Affiliation: | University of Illinois at Urbana-Champaign, Champaign, IL 61820.;Memphis State University, Memphis, TN 38152.;University of Illinois at Urbana-Champaign. This research is partly supported by the Investors in Business Education at the University of Illinois. The authors are grateful to C. F. Lee and anonymous referees for their helpful comments. |
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| Abstract: | ![]()
Previous studies have investigated only unconditional heteroscedasticity in the market model. This paper tests for both conditional and unconditional heteroscedasticities as well as normality. Using the monthly stock rate of return data secured from the Center for Research in Security Prices (CRSP) tape for 1976 through 1983, this paper shows that conditional heteroscedasticity is more widespread than unconditional heteroscedasticity, suggesting the necessity of model refinements that take conditional heteroscedasticity into account. This paper provides an alternative estimation of betas of individual securities and portfolios based on the autoregressive conditional heteroscedastic (ARCH) model introduced by Engle. The efficiency of the market model coefficients is markedly improved across all firms in the sample through the ARCH technique. |
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