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Understanding Uncertainty Shocks and the Role of Black Swans

Listed author(s):
  • Orlik, Anna
  • Veldkamp, Laura

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.

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Paper provided by C.E.P.R. Discussion Papers in its series CEPR Discussion Papers with number 10147.

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Date of creation: Sep 2014
Handle: RePEc:cpr:ceprdp:10147
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  1. Susanto Basu & Brent Bundick, 2011. "Uncertainty Shocks in a Model of Effective Demand," Boston College Working Papers in Economics 774, Boston College Department of Economics, revised 01 Nov 2015.
  2. Jesus Fernandez-Villaverde & Pablo Guerron-Quintana & Juan F. Rubio-Ramirez & Martin Uribe, 2011. "Risk Matters: The Real Effects of Volatility Shocks," American Economic Review, American Economic Association, vol. 101(6), pages 2530-2561, October.
  3. Francois Gourio, 2012. "Disaster Risk and Business Cycles," American Economic Review, American Economic Association, vol. 102(6), pages 2734-2766, October.
  4. Born, Benjamin & Peter, Alexandra & Pfeifer, Johannes, 2013. "Fiscal news and macroeconomic volatility," Journal of Economic Dynamics and Control, Elsevier, vol. 37(12), pages 2582-2601.
  5. Jesús Fernández-Villaverde & Pablo Guerrón-Quintana & Keith Kuester & Juan Rubio-Ramírez, 2015. "Fiscal Volatility Shocks and Economic Activity," American Economic Review, American Economic Association, vol. 105(11), pages 3352-3384, November.
  6. James H. Stock & Mark W. Watson, 2012. "Disentangling the Channels of the 2007-09 Recession," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 43(1 (Spring), pages 81-156.
  7. Mathieu Taschereau-Dumouchel & Edouard Schaal & Pablo Fajgelbaum, 2013. "Uncertainty Traps," 2013 Meeting Papers 677, Society for Economic Dynamics.
  8. Bidder, R.M. & Smith, M.E., 2012. "Robust animal spirits," Journal of Monetary Economics, Elsevier, vol. 59(8), pages 738-750.
  9. Elias Albagli & Christian Hellwig & Aleh Tsyvinski, 2011. "A Theory of Asset Pricing Based on Heterogeneous Information," NBER Working Papers 17548, National Bureau of Economic Research, Inc.
  10. Timothy Cogley & Thomas J. Sargent, 2005. "The conquest of US inflation: Learning and robustness to model uncertainty," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 8(2), pages 528-563, April.
  11. R?diger Bachmann & Steffen Elstner & Eric R. Sims, 2013. "Uncertainty and Economic Activity: Evidence from Business Survey Data," American Economic Journal: Macroeconomics, American Economic Association, vol. 5(2), pages 217-249, April.
  12. Emi Nakamura & Dmitriy Sergeyev & Jón Steinsson, 2017. "Growth-Rate and Uncertainty Shocks in Consumption: Cross-Country Evidence," American Economic Journal: Macroeconomics, American Economic Association, vol. 9(1), pages 1-39, January.
  13. Bachmann, Rüdiger & Bayer, Christian, 2013. "‘Wait-and-See’ business cycles?," Journal of Monetary Economics, Elsevier, vol. 60(6), pages 704-719.
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