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

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  • Kim, Soohun
  • Skoulakis, Georgios

Abstract

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.

Suggested Citation

  • Kim, Soohun & Skoulakis, Georgios, 2018. "Ex-post risk premia estimation and asset pricing tests using large cross sections: The regression-calibration approach," Journal of Econometrics, Elsevier, vol. 204(2), pages 159-188.
  • Handle: RePEc:eee:econom:v:204:y:2018:i:2:p:159-188
    DOI: 10.1016/j.jeconom.2018.01.007
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