News-driven uncertainty fluctuations

Dongho Song and Jenny Tang

No 18-3, Working Papers from Federal Reserve Bank of Boston

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 premiums; survey forecasts; uncertainty (search for similar items in EconPapers)
JEL-codes: C11 E32 E37 G12 (search for similar items in EconPapers)
Pages: 67 pages
Date: 2018-01-01
New Economics Papers: this item is included in nep-his, nep-mac and nep-upt
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Journal Article: News-Driven Uncertainty Fluctuations (2023)
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