Identification Robust Testing of Risk Premia in Finite Samples

AbstractThe reliability of tests on the risk premia in linear factor models is threatened by limited sample sizes and weak identification of risk premia frequently encountered in applied work. We, therefore, propose novel tests on the risk premia that are robust to both limited sample sizes and the identification strength of the risk premia as reflected by the quality of the risk factors. These tests are appealing for empirically relevant settings, and lead to confidence sets of risk premia that can substantially differ from conventional ones. To show the latter, we revisit two high-profile empirical applications.