Model Uncertainty in the Cross Section

Abstract

We develop a transparent Bayesian framework to measure uncertainty in asset pricing models. By assigning a modified class of g-priors to the risk prices of asset pricing factors, our method quantifies the trade-off between mean-variance efficiency and parsimony for asset pricing models to achieve high posterior probabilities. Model uncertainty is defined as the entropy of these model probabilities. We prove the model selection consistency property of our procedure, which is missing from the classic g-priors. Acknowledging the possibility of omitting true asset pricing factors in real applications, we also characterize the maximum degree of contamination that the omitted factors can introduce to our model uncertainty measure. Empirically, we find that model uncertainty escalates during major market events and carries a significantly negative risk premium of approximately half the magnitude of the market. Positive shocks to model uncertainty predict persistent outflows from US equity funds and inflows to Treasury funds.

Keywords: Model Uncertainty, Asset Pricing Factor, Bayesian Inference, Model Selection Consistency, Omitted Factors, Mutual Fund Flows

JEL Classification: C11, G11, G12.