We examine a production-based asset-pricing model with an unobservable mean growth rate following a two-state Markov chain and with an ambiguity-averse representative agent. Our model requires a low coefficient of relative risk aversion to produce: (i) a high equity premium and volatile equity returns, (ii) a low and smooth risk-free rate, (iii) smooth consumption growth and volatile investment growth, (iv) countercyclical equity premium and market price of risk, (v) conditional heteroscedasticity in returns, and (vi) long-horizon predictability of excess returns.

We examine asset allocation decisions under smooth ambiguity aversion when an investor has a prior degree of belief in an asset pricing model (e.g., the domestic CAPM). Different from a Bayesian approach, the investor separately relies on the conditional distribution of returns and on the posterior over parameters to make decisions, rather than on the predictive distribution of returns that integrates priors and likelihood information. We find that in the perspective of US investors, ambiguity aversion generates strong home bias in equity holdings, regardless of beliefs in the CAPM or risk aversion. Results become stronger under regime-switching investment opportunities.

We develop a real options model in which an entrepreneur facing ambiguity makes optimal investment and financing decisions for an innovation project. We introduce jumps in innovation returns and model investors’ aversion to ambiguity in both diffusion and jump risks. Debt accelerates investment by lowering the threshold and shortening expected waiting time, thereby increasing project value. This effect strengthens under greater ambiguity, offering a novel rationale for why debt—not equity—fosters innovation. Our results provide a coherent explanation for recent empirical findings on debt’s role in innovation and contribute to the broader literature on investment under uncertainty.

We use the Bayesian method introduced by Gallant and McCulloch (2009) to estimate consumption-based asset pricing models featuring smooth ambiguity preferences. We rely on semi-nonparametric estimation of a flexible auxiliary model in our structural estimation. Based on the market and aggregate consumption data, our estimation provides statistical support for asset pricing models with smooth ambiguity. Statistical model comparison shows that models with ambiguity, learning, and time-varying volatility are preferred to the long-run risk model. We also analyze asset pricing implications of the estimated models. Received April 12, 2016; editorial decision September 11, 2018 by Editor Stijn Van Nieuwerburgh.