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In the data, asset prices exhibit large negative moves at frequencies of about 18 months. These large moves are puzzling as they do not coincide, nor are they followed by any significant moves in the real side of the economy. On the other hand, we find that measures of investor's uncertainty about their estimate of future growth have significant information about large moves in returns. We set-up a recursive-utility based model in which investors learn about the latent expected growth using the cross-section of signals. The uncertainty (confidence measure) about investor's growth expectations, as in the data, is time-varying and subject to large moves. The fluctuations in confidence measure affect the distribution of future consumption given investors' information, and consequently influence equilibrium asset prices and risk premia. In calibrations we show that the model can account for the large return move evidence in the data, distribution of asset prices, predictability of excess returns and other key asset market facts.
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We develop a general equilibrium model in which income and dividends are smooth, but asset prices are subject to large moves (jumps). A prominent feature of the model is that the optimal decision of investors to learn the unobserved state triggers large asset-price jumps. We show that the learning choice is critically determined by preference parameters and the conditional volatility of income process. An important prediction of the model is that income volatility predicts future jumps, while the variation in the level of income does not. We find that indeed in the data large moves in returns are predicted by consumption volatility, but not by the changes in the consumption level. We show that the model can quantitatively capture these novel features of the data.