On the estimation of asset pricing models using univariate betas

We derive asymptotic standard errors of risk premia estimates based on the popular two-pass cross-sectional regression methodology developed by Black, Jensen, and Scholes (1972) and Fama and MacBeth (1973) when univariate betas are used as regressors. Our standard errors are robust to model misspecification and allow for general distributional assumptions. In testing whether the beta risk of a given factor is priced, our misspecification robust standard error can lead to economically different conclusions from those based on the Jagannathan and Wang (1998) standard error which is derived under the correctly specified model.

Research Highlights► Jagannathan and Wang (1998) present an asymptotic theory for models with univariate betas under the assumption that the model is correctly specified. ► The main contribution of this paper is the proposal of misspecification robust asymptotic standard errors of the estimated zero-beta rate and risk premia in models with univariate betas. ► In the case of the generalized least squares cross-sectional regression estimators, we show that the asymptotic variances of the risk premia estimates are always larger when the model is misspecified. ► In testing whether the beta risk of a given factor is priced, our misspecification robust standard error can lead to economically different conclusions from those based on the Jagannathan and Wang (1998) standard error.