Risk Premium Estimation with Multicollinear and Invariant Betas by the Two-Pass Cross-Sectional Regressions

This paper analyzes the finite-sample performance of the two-pass (TP) estimators of factor risk prices when betas have high cross-sectional correlations (Multicollinear) and when betas have small cross-sectional variations (Invariant). Our Monte Carlo simulations, calibrated using actual financial data, show that the TP estimation can lead to unreliable statistical inferences when betas are multicollinear and/or invariant. Estimates of factor risk prices and the pricing intercept can have biases larger than 100% of the true value. Also the t-test for hypothesis on risk prices and the intercept have very low statistical power in these cases. Various pre-diagnostic methods are proposed in order to evaluate the reliability of the inferences from the TP estimation in empirical work