Dynamic Asset Allocation with Ambiguous Return Predictability

Abstract

We study an investor's optimal consumption and portfolio choice problem when he is confronted with two possibly misspecified submodels of stock returns: one with IID returns and the other with predictability. We adopt a generalized recursive ambiguity model to accommodate the investor's aversion to model uncertainty. The investor deals with specification doubts by slanting his beliefs about submodels of returns pessimistically, causing his investment strategy to be more conservative than the Bayesian strategy. This effect is especially strong when the submodel with a low Bayesian probability delivers a much smaller continuation value. Unlike in the Bayesian framework, the hedging demand against model uncertainty may cause the investor's stock allocation to decrease sharply given a small doubt of return predictability, even though the predictive variable is large. Adopting the Bayesian strategy can lead to sizable welfare costs for an ambiguity-averse investor, especially when he has a strong prior of return predictability.

Keywords: generalized recursive ambiguity utility, ambiguity aversion, model uncertainty, learning, portfolio choice, robustness, return predictability

JEL Classification: G11