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Uncertainty Shocks and the Role of the Black Swan

Author

Listed:
  • Laura Veldkamp

    (NYU Stern)

  • Anna Orlik

    (Federal Reserve Board of Governors)

Abstract
A recent literature explores many ways in which uncertainty shocks can have important economic effects. But how large are uncertainty shocks and where do they come from? Researchers typically estimate a model with stochastic volatility, using all available data, then condition on the estimated model to infer volatility. This volatility is the uncertainty of an agent who knows the true probability of outcomes and whose only uncertainty is about what the draw from that distribution will be. We model a Bayesian forecaster who uses new data released each quarter to re-estimate the parameters that govern the shape of the probability distribution of GDP growth. Although the forecaster's parameter revisions are small, the probability of black swans (extreme events) is very sensitive to these revisions. Our real-time measure of GDP forecast uncertainty reveals that changes in the risk of a black swan explain most of the shocks to uncertainty.

Suggested Citation

  • Laura Veldkamp & Anna Orlik, 2014. "Uncertainty Shocks and the Role of the Black Swan," 2014 Meeting Papers 275, Society for Economic Dynamics.
    • Handle: RePEc:red:sed014:275
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Fabrice Collard & Sujoy Mukerji & Kevin Sheppard & Jean‐Marc Tallon, 2018. "Ambiguity and the historical equity premium," Quantitative Economics, Econometric Society, vol. 9(2), pages 945-993, July.
    2. Julian Kozlowski & Laura Veldkamp & Venky Venkateswaran, 2015. "The Tail that Wags the Economy: Belief-Driven Business Cycles and Persistent Stagnation," Working Papers 15-10, New York University, Leonard N. Stern School of Business, Department of Economics.
    3. Bartram, Söhnke M. & Brown, Gregory W. & Stulz, René M., 2016. "Why does idiosyncratic risk increase with market risk?," CFS Working Paper Series 533, Center for Financial Studies (CFS).
    4. Julian Kozlowski & Laura Veldkamp & Venky Venkateswaran, 2020. "The Tail That Wags the Economy: Beliefs and Persistent Stagnation," Journal of Political Economy, University of Chicago Press, vol. 128(8), pages 2839-2879.
    5. Veldkamp, Laura & Kozeniauskas, Nicholas & Orlik, Anna, 2016. "What Are Uncertainty Shocks?," CEPR Discussion Papers 11501, C.E.P.R. Discussion Papers.
    6. Saygin Sahinoz & Evren Erdogan Cosar, 2020. "Quantifying uncertainty and identifying its impacts on the Turkish economy," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 47(2), pages 365-387, May.
    7. Tatsuro Senga, 2014. "A New Look at Uncertainty Shocks: Imperfect Information and Misallocation," UTokyo Price Project Working Paper Series 042, University of Tokyo, Graduate School of Economics.
    8. Laura Veldkamp & Anna Orlik & Nicholas Kozeniauskas, 2015. "Black Swans and the Many Shades of Uncertainty," 2015 Meeting Papers 677, Society for Economic Dynamics.
    9. Julian Kozlowski & Laura Veldkamp & Venky Venkateswaran, 2019. "The Tail That Keeps the Riskless Rate Low," NBER Macroeconomics Annual, University of Chicago Press, vol. 33(1), pages 253-283.
    10. Straub, Ludwig & Ulbricht, Robert, 2019. "Endogenous second moments: A unified approach to fluctuations in risk, dispersion, and uncertainty," Journal of Economic Theory, Elsevier, vol. 183(C), pages 625-660.
    11. Shen, Wenyi, 2015. "News, disaster risk, and time-varying uncertainty," Journal of Economic Dynamics and Control, Elsevier, vol. 51(C), pages 459-479.
    12. Nicholas Kozeniauskas & Anna Orlik & Laura Veldkamp, 2016. "The Common Origin of Uncertainty Shocks," NBER Working Papers 22384, National Bureau of Economic Research, Inc.

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