Comparing Asset Pricing Models with Non-Traded Factors and Principal Components

This paper develops a Bayesian methodology to compare asset pricing models containing non-traded factors and principal components. Existing comparison procedures are inadequate when models include such factors due to estimation uncertainties in mimicking portfolios and return covariances. Furthermore, regressions of test assets on such factors are interdependent, rendering comparisons with recently proposed priors sensitive to subsets of the test assets. Thus, I derive novel, non-informative priors that deliver invariant inferences. Simulations suggest that my methodology substantially outperforms existing methods in identifying true non-traded models. I find that macroeconomic models dominate several, recent benchmark models with traded factors and principal components