Learning and Asset-price Jumps

Abstract

W e develop a general equilibrium model in which income and dividends are smooth but asset prices contain large moves (jumps). These large price jumps are triggered by optimal decisions of investors to learn the unobserved state. We show that learning choice is determined by preference parameters and the conditional volatility of income process. An important model prediction is that income volatility predicts future jump periods, while income growth does not. Consistent with the model, large moves in returns in the data are predicted by consumption volatility but not by consumption growth. The model quantitatively captures these novel features of the data. ( JEL G00, G12, G14, D83) A prominent feature of financial markets is infrequent but large price movements (jumps). 1 In this article, we develop a model in which income and dividends have smooth Gaussian dynamics; however, asset prices are subject to large infrequent jumps. In our model, large moves in asset prices obtain from the actions of the representative agent to acquire more information about the unobserved state of the economy for a cost. We show that the optimal decision to incur a cost and learn the true economic state is directly related to the level of uncertainty in the economy. This implies that aggregate economic volatility, as well as market volatility, should predict jumps in returns. We show that indeed in the data, consistent with the model, return jumps are predicted by consumption volatility (market volatility). Further, the implied asset-price implications from our model are consistent with the key findings from parametric models about frequency and predictability of jumps as well as nonparametric jump-detection analysis of Barndorff-Nielsen and Shephard (2006). Based on