Tail Risk and Expectations

We incorporate tail risk in a Bayesian learning framework with information frictions and study how individuals' expectations respond to first and second moment shocks. In our model, we retain "rational news" through Bayesian learning and abstract from any behavioral biases. First, we show that individuals overreact under tail risk, that is, individuals are excessively optimistic and pessimistic as compared to a Bayesian learning framework without tail risk. Second, uncertainty shocks lead to more pessimistic forecasts. Third, the magnitude of overreaction depends on the level of uncertainty in the economy. We provide empirical support for our theoretical findings using quantile regressions and event studies. Our findings shed light on factors driving overreaction in expectations and highlight the importance of uncertainty shocks in propagating macroeconomic stability