News-driven uncertainty fluctuations

Dongho Song and Jenny Tang

We embed a news shock, a noisy indicator of the future state, in a two-state Markovswitching 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.

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Year of publication:	[2018] ; This version: January 2018
Authors:	Song, Dongho ; Tang, Jenny
Publisher:	[Boston, MA] : Federal Reserve Bank of Boston
Subject:	Bayesian learning \| discrete environment \| Minsky moment \| news shocks \| recursive utility \| risk premium \| survey forecasts \| uncertainty \| Risiko \| Risk \| Theorie \| Theory \| Schock \| Shock \| Risikoprämie \| Risk premium \| Prognoseverfahren \| Forecasting model \| Konjunktur \| Business cycle \| Bayes-Statistik \| Bayesian inference \| Entscheidung unter Unsicherheit \| Decision under uncertainty \| VAR-Modell \| VAR model \| Erwartungsbildung \| Expectation formation