Home About Latest Browse RSS Advanced Search

Federal Reserve Bank of Boston
Working Papers
News-driven uncertainty fluctuations
Dongho Song
Jenny Tang

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 “Minsky moment”—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.

Download Summary
Download Full text
Cite this item
Dongho Song & Jenny Tang, News-driven uncertainty fluctuations, Federal Reserve Bank of Boston, Working Papers 18-3, 01 Jan 2018.
More from this series
JEL Classification:
Subject headings:
Keywords: Bayesian learning; discrete environment; Minsky moment; news shocks; recursive utility; risk premium; survey forecasts; uncertainty
For corrections, contact Catherine Spozio ()
Fed-in-Print is the central catalog of publications within the Federal Reserve System. It is managed and hosted by the Economic Research Division, Federal Reserve Bank of St. Louis.

Privacy Legal