Publication Date: May 2020
This paper develops a new method informed by data and models to recover information about investor beliefs. Our approach uses information embedded in forward-looking asset prices in conjunction with asset pricing models. We step back from presuming rational expectations and entertain potential belief distortions bounded by a statistical measure of discrepancy. Additionally, our method allows for the direct use of sparse survey evidence to make these bounds more informative. Within our framework, market-implied beliefs may diﬀer from those implied by rational expectations due to behavioral/psychological biases of investors, ambiguity aversion, or omitted permanent components to valuation. Formally, we represent evidence about investor beliefs using a novel nonlinear expectation function deduced using model-implied moment conditions and bounds on statistical divergence. We illustrate our method with a prototypical example from macro-ﬁnance using asset market data to infer belief restrictions for macroeconomic growth rates.
Keywords: Asset pricing, Subjective beliefs, Long-term uncertainty, Ambiguity aversion, Cressie-Read divergence, Generalized empirical likelihood, Large deviation theory