News-Driven Uncertainty Fluctuations

67 Pages Posted: 9 Aug 2018 Last revised: 21 Feb 2019

See all articles by Dongho Song

Dongho Song

Johns Hopkins University - Carey Business School

Jenny Tang

Federal Reserve Banks - Federal Reserve Bank of Boston

Date Written: 2018-01-01

Abstract

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.

Keywords: Bayesian learning, discrete environment, Minsky moment, news shocks, recursive utility, risk premium, survey forecasts, uncertainty

JEL Classification: C11, E32, E37, G12

Suggested Citation

Song, Dongho and Tang, Jenny, News-Driven Uncertainty Fluctuations (2018-01-01). FRB of Boston Working Paper No. 18-3. Available at SSRN: https://ssrn.com/abstract=3228455

Dongho Song (Contact Author)

Johns Hopkins University - Carey Business School ( email )

Baltimore, MD 20036-1984
United States

Jenny Tang

Federal Reserve Banks - Federal Reserve Bank of Boston ( email )

600 Atlantic Avenue
Boston, MA 02210
United States

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