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Understanding Uncertainty Shocks and the Role of Black Swans

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  • Orlik, Anna
  • Veldkamp, Laura

Abstract

A fruitful emerging literature reveals that shocks to uncertainty can explain asset returns, business cycles and financial crises. The literature equates uncertainty shocks with changes in the variance of an innovation whose distribution is common knowledge. But how do such shocks arise? This paper argues that people do not know the true distribution of macroeconomic outcomes. Like Bayesian econometricians, they estimate a distribution. Using real-time GDP data, we measure uncertainty as the conditional standard deviation of GDP growth, which captures uncertainty about the distribution’s estimated parameters. When the forecasting model admits only normally-distributed outcomes, we find small, acyclical changes in uncertainty. But when agents can also estimate parameters that regulate skewness, uncertainty fluctuations become large and counter-cyclical. The reason is that small changes in estimated skewness whip around probabilities of unobserved tail events (black swans). The resulting forecasts resemble those of professional forecasters. Our uncertainty estimates reveal that revisions in parameter estimates, especially those that affect the risk of a black swan, explain most of the shocks to uncertainty.

Suggested Citation

  • Orlik, Anna & Veldkamp, Laura, 2014. "Understanding Uncertainty Shocks and the Role of Black Swans," CEPR Discussion Papers 10147, C.E.P.R. Discussion Papers.
  • Handle: RePEc:cpr:ceprdp:10147
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Venky Venkateswaran & Laura Veldkamp & Julian Kozlowski, 2015. "The Tail that Wags the Economy: Belief-Driven Business Cycles and Persistent Stagnation," 2015 Meeting Papers 800, Society for Economic Dynamics.
    2. Bartram, Sohnke M. & Brown, Gregory W. & Stulz, Rene M., 2016. "Why Does Idiosyncratic Risk Increase with Market Risk?," Working Paper Series 2016-13, Ohio State University, Charles A. Dice Center for Research in Financial Economics.
    3. Julian Kozlowski & Laura Veldkamp & Venky Venkateswaran, 2015. "The Tail that Wags the Economy: Beliefs and Persistent Stagnation," NBER Working Papers 21719, National Bureau of Economic Research, Inc.
    4. Dow, Sheila, 2016. "Uncertainty: A diagrammatic treatment," Economics - The Open-Access, Open-Assessment E-Journal, Kiel Institute for the World Economy (IfW), vol. 10, pages 1-25.
    5. Michele Piffer & Maximilian Podstawski, 2016. "Identifying Uncertainty Shocks Using the Price of Gold," Discussion Papers of DIW Berlin 1549, DIW Berlin, German Institute for Economic Research.
    6. Shen, Wenyi, 2015. "News, disaster risk, and time-varying uncertainty," Journal of Economic Dynamics and Control, Elsevier, vol. 51(C), pages 459-479.
    7. Rangan Gupta & Chi Keung Marco Lau & Mark E. Wohar, 2016. "The Impact of US Uncertainty on the Euro Area in Good and Bad Times: Evidence from a Quantile Structural Vector Autoregressive Model," Working Papers 201681, University of Pretoria, Department of Economics.

    More about this item

    Keywords

    forecasting; rare events; Uncertainty;

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles
    • G1 - Financial Economics - - General Financial Markets

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