Ex-post risk premia estimation and asset pricing tests using large cross sections: The regression-calibration approach

We propose a modification of the two-pass cross-sectional regression approach for estimating ex-post risk premia in linear asset pricing models, suitable for the case of large cross sections and short time series. Employing the regression-calibration method, we provide a beta correction method, which deals with the error-in-variables problem, based on which we construct an N-consistent estimator of ex-post risk premia and develop associated novel asset pricing tests. Empirically, we reject the implications of the CAPM and the Fama–French three-factor and five-factor models but also offer new evidence on the relevance of the HML factor for pricing large cross sections of individual stocks.