Learning under ambiguity

This paper considers learning when the distinction between risk and ambigu-ity (Knightian uncertainty) matters. Working within the framework of recursive multiple-priors utility, the paper formulates a counterpart of the Bayesian model of learning about an uncertain parameter from conditionally i.i.d. signals. Am-biguous signals capture differences in information quality that cannot be captured by noisy signals. They may increase the volatility of conditional actions and they prevent ambiguity from vanishing in the limit. Properties of the model are illustrated with two applications. First, in a dy-namic portfolio choice model, stock market participation costs arise endogenously from preferences and depend on past market performance. Second, ambiguous news induce negative skewness of asset returns and may increase price volatility. Shocks that trigger a period of ambiguous news induce a price discount on impact and are likely to be followed by further negative price changes