Testing Linear Factor Pricing Models with Large Cross-Sections: A Distribution-Free Approach
We develop a finite-sample procedure to test the beta-pricing representation of linear factor pricing models that is applicable even if the number of test assets is greater than the length of the time series. Our distribution-free framework leaves open the possibility of unknown forms of non-normalities, heteroskedasticity, time-varying correlations, and even outliers in the asset returns. The power of the proposed test procedure increases as the time-series lengthens and/or the cross-section becomes larger. This stands in sharp contrast to the usual tests that lose power or may not even be computable if the cross-section is too large. Finally, we revisit the CAPM and the Fama-French three factor model. Our results strongly support the mean-variance efficiency of the market portfolio.
Journal of Business & Economic Statistics (0735-0015 (version papier), 1537-2707 (Internet))
January 2013. Vol. 31, Iss. 1, pp. 66-77