Asset Prices in a News Driven Real Business Cycle Model

We examine the implications of introducing anticipated productivity shocks for the ability of a real-business-cycle model to explain asset prices. Our theoretical framework is a real-business-cycle model in which agents receive news about future productivity shocks. We show that incorporating anticipated shocks, or news, creates a persistent predictable component in consumption growth, often referred to as long-run risk in the finance literature (Bansal and Yaron, 2004). Thus, in conjunction with Epstein and Zin (1989) preferences and under plausible parameter calibrations, news shocks help explain key observed asset pricing facts. Furthermore, we show that news shocks improve our prediction for the co-movement of macroeconomic and financial variables, and explain the asset returns' lead over the business cycle. We also model time-varying economic uncertainty (stochastic volatility), and show how under certain conditions this could lead to lower premia in a model where consumption is endogenous. Finally, we discuss how a class of dynamic stochastic general equilibrium models with recursive preferences can be solved using perturbation methods, which are more computationally efficient than the usual numerical techniques.