Ambiguity, Risk and Portfolio Choice under Incomplete Information

This paper studies optimal consumption and portfolio choice in a Merton-style model with incomplete information when there is a distinction between ambiguity and risk. The latter distinction is afforded by adoption of recursive multiple-priors utility. The fundamental issues are: (i) How does the agent optimally estimate the unobservable processes as new information arrives over time? (ii) What are the effects of ambiguity and incomplete information on behavior? This paper shows that it is optimal to first use any prior to perform Bayesian estimation and then to maximize expected utility with that prior based on the resulting estimates. Finally, the paper shows that a hedging demand arises that is affected by both ambiguity and estimation risk.