Author(s)

Jianhua Yuan, Robert Savickas

Department of Finance, George Washington University, Washington, DC, USA.
Yuan is at Capital Market Research Division, Fannie Mae, Washington, DC, USA.

This paper shows that the classic cross-sectional asset pricing tests tend to suffer from severe risk-premium estimation errors because of small variation in betas. We explain how the conventional approach uses low criteria to validate an asset-pricing model and suffers from the model-misspecification issue because of the complication associated with the zero-beta excess return. We show that the resulting biases in estimates of risk premia and their standard errors are se-vere enough to lead researchers into inferring incorrect implications about some asset-pricing theories being tested. Further, we suggest that one simple method of mitigating these issues is to restrict the zero-beta excess returns to their theoretical values in the cross-sectional regressions and to conduct the straightforward test of whether the estimated ex-ante risk premia are consistent with the observed ex-post ones. The empirical testing results not only further affirm the higher efficiency of the estimates produces by the suggested method, but also show, contrary to some prior evidence, that the market factor is priced consistently.

Cross-Sectional Regression; Consistent Estimator; Efficient Estimator; Risk Premium; Zero-Beta Return; Model Misspecification; Beta-Variation

J. Yuan and R. Savickas, "Cross-Sectional Estimation Biases in Risk Premia and Ze-ro-Beta Excess Returns," Technology and Investment, Vol. 4 No. 1B, 2013, pp. 54-66. doi: 10.4236/ti.2013.41B010.