News-Driven Uncertainty Fluctuations
67 Pages Posted: 9 Aug 2018 Last revised: 21 Feb 2019
Date Written: 2018-01-01
We embed a news shock, a noisy indicator of the future state, in a two-state Markov-switching growth model. Our framework, combined with parameter learning, features rich history-dependent uncertainty dynamics. We show that bad news that arrives during a prolonged economic boom can trigger a â€œMinsky momentâ€�â€”a sudden collapse in asset values. The effect is greatly amplified when agents have a preference for early resolution of uncertainty. We leverage survey recession probability forecasts to solve a sequential learning problem and estimate the full posterior distribution of model primitives. We identify historical periods in which uncertainty and risk premia were elevated because of news shocks.
Keywords: Bayesian learning, discrete environment, Minsky moment, news shocks, recursive utility, risk premium, survey forecasts, uncertainty
JEL Classification: C11, E32, E37, G12
Suggested Citation: Suggested Citation