This paper derives explicit expressions for the asymptotic variances of the maximum likelihood
and continuously-updated GMM estimators in models that may not satisfy the fundamental assetpricing
restrictions in population. 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. While 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,
that arise from explicitly incorporating model misspecification in the analysis, are illustrated using
simulations and an empirical application.