Nonconcave Portfolio Choice under Smooth Ambiguity

TL;DR

This paper develops a comprehensive framework for dynamic portfolio choice under smooth ambiguity and Bayesian learning, accommodating nonlinear payoffs and broad utility classes, with explicit trading rules and applications to delegated management.

Contribution

It introduces a novel approach to non-concave portfolio optimization under ambiguity, providing explicit solutions and insights into how ambiguity aversion affects investment behavior.

Findings

Ambiguity aversion shifts beliefs toward adverse states.

It limits aggressive risk-taking in uncertain environments.

Reduces volatility by lowering risky asset exposure.

Abstract

We study continuous-time portfolio choice with nonlinear payoffs under smooth ambiguity and Bayesian learning. We develop a general framework for dynamic, non-concave asset allocation that accommodates nonlinear payoffs, broad utility classes, and flexible ambiguity attitudes. Dynamic consistency is obtained by a robust representation that recasts the ambiguity-averse problem as ambiguity-neutral with distorted priors. This structure delivers explicit trading rules by combining nonlinear filtering with the martingale approach and nests standard concave and linear-payoff benchmarks. As a leading application, delegated management with convex incentives illustrates that ambiguity aversion shifts beliefs toward adverse states, limits the range of states that would otherwise trigger more aggressive risk taking, and reduces volatility through lower risky exposure.