Model uncertainty in the cross-section of stock returns
股票收益横截面中的模型不确定性
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…