Federal Reserve Bank of Boston
News-driven uncertainty fluctuations
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.
Cite this item
Dongho Song & Jenny Tang, News-driven uncertainty fluctuations, Federal Reserve Bank of Boston, Working Papers 18-3, 01 Jan 2018.
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
- E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
- G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
Keywords: Bayesian learning; discrete environment; Minsky moment; news shocks; recursive utility; risk premium; survey forecasts; uncertainty
This item with handle RePEc:fip:fedbwp:18-3
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