This paper derives explicit expressions for the asymptotic variances of the maximum likelihood and continuously updated GMM estimators under potentially misspecified models. The proposed misspecification-robust variance estimators allow the researcher to conduct valid inference on the model parameters even when the model is rejected by the data. Although the results for the maximum likelihood estimator are only applicable to linear asset-pricing models, the asymptotic distribution of the continuously updated GMM estimator is derived for general, possibly nonlinear, models. The large corrections in the asymptotic variances, which arise from explicitly incorporating model misspecification in the analysis, are illustrated using simulations and an empirical application.
asset pricing model misspecification continuously updated GMM maximum likelihood asymptotic approximation misspecification-robust tests