We study asset pricing implications of a revealing and tractable formulation of smooth ambiguity investor preferences in a continuous-time environment. Investors do not observe a hidden Markov state and instead make inferences about this state using past data. We show that ambiguity about this hidden state distribution alters investor decisions and equi-librium asset prices. Our continuous-time formulation allows us to apply recursive ﬁltering and Hamilton-Jacobi-Bellman methods to solve the modiﬁed decision problem. Using such methods, we show how characterizations of portfolio allocations and local uncertainty-return trade-oﬀs change when investors are ambiguity-averse.
Keywords— Risk, ambiguity, robustness, asset pricing, portfolio allocation, continuous time