Testing Linear Factor Pricing Models With Large Cross Sections: A Distribution-Free Approach
提出一种有限样本检验方法，用于检验线性因子定价模型的贝塔定价表示，即使测试资产数量超过时间序列长度也适用，且无需假设收益率的分布形式。
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.