Model Uncertainty in the Cross Section
46 Pages Posted: 14 Sep 2021 Last revised: 29 Oct 2021
Date Written: September 12, 2021
We develop a transparent Bayesian approach to quantify uncertainty in linear stochastic discount factor (SDF) models. We show that, for a Bayesian decision maker, posterior model probabilities increase with maximum in-sample Sharpe ratios and decrease with model dimensions. Entropy of posterior probabilities represents model uncertainty. We apply our approach to quantify the time series of model uncertainty in North American, European, and Asian Pacific equity markets. Model uncertainty is countercyclical in these markets. It predicts investors’ asset allocation decisions across equity and fixed-income funds. In survey data, investors tend to be more pessimistic about equity performance during periods of higher model uncertainty.
Keywords: Model Uncertainty, Linear Stochastic Discount Factor, Bayesian Inference
JEL Classification: C11, G11, G12.
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