Asset Pricing With Incomplete Information and Fat Tails

We study a consumption-based asset pricing model with incomplete information and a- stable shocks. Incomplete information leads to a non-Gaussian filtering problem. Bayesian updating generates fluctuating confidence in the agents' estimate of the persistent component of the dividends' growth rate. This has the potential to generate time variation in the volatility of model-implied returns, without relying on discrete shifts in the drift rate of dividend growth rates. A test of the model using US consumption data shows that implied returns display significant volatility persistence of a magnitude comparable to that in the data