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News-driven uncertainty fluctuations

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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.

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  • Dongho Song & Jenny Tang, 2018. "News-driven uncertainty fluctuations," Working Papers 18-3, Federal Reserve Bank of Boston.
    • Handle: RePEc:fip:fedbwp:18-3
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    More about this item

    Keywords

    Bayesian learning; discrete environment; Minsky moment; news shocks; recursive utility; risk premiums; survey forecasts; uncertainty;
    All these keywords.

    JEL classification:

    • 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

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