Ambiguity, learning, and asset returns


We develop a consumption-based asset-pricing model in which the representative agent is ambiguous about the hidden state in consumption growth. He learns about the hidden state under ambiguity by observing past consumption data. His preferences are represented by the smooth ambiguity model axiomatized by Klibanoff et al. (2005, 2006). Unlike the standard Bayesian theory, this utility model implies that the posterior of the hidden state and the conditional distribution of the consumption process given a state cannot be reduced to a predictive distribution. By calibrating the ambiguity aversion parameter, the subjective discount factor, and the risk aversion parameter (with the latter two values between zero and one), our model can match the first moments of the equity premium and riskfree rate found in the data. In addition, our model can generate a variety of dynamic asset pricing phenomena, including the procyclical variation of price-dividend ratios, the countercyclical variation of equity premia and equity volatility, and the mean reversion and long horizon predictability of excess returns

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