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Understanding Uncertainty Shocks and the Role of Black Swans

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  • Anna Orlik
  • Laura Veldkamp

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 distributions 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

  • Anna Orlik & Laura Veldkamp, 2014. "Understanding Uncertainty Shocks and the Role of Black Swans," NBER Working Papers 20445, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:20445
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    Cited by:

    1. 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.
    2. Cezar, Rafael & Gigout, Timothée & Tripier, Fabien, 2020. "Cross-border investments and uncertainty: Firm-level evidence," Journal of International Money and Finance, Elsevier, vol. 108(C).
    3. Drobetz, Wolfgang & El Ghoul, Sadok & Guedhami, Omrane & Janzen, Malte, 2018. "Policy uncertainty, investment, and the cost of capital," Journal of Financial Stability, Elsevier, vol. 39(C), pages 28-45.
    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. Nicholas Bloom & Fatih Guvenen & Sergio Salgado, 2016. "Skewed Business Cycles," 2016 Meeting Papers 1621, Society for Economic Dynamics.
    6. 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.
    7. Michele Piffer & Maximilian Podstawski, 2018. "Identifying Uncertainty Shocks Using the Price of Gold," Economic Journal, Royal Economic Society, vol. 128(616), pages 3266-3284, December.
    8. 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.
    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. Liyan Han & Mengchao Qi & Libo Yin, 2016. "Macroeconomic policy uncertainty shocks on the Chinese economy: a GVAR analysis," Applied Economics, Taylor & Francis Journals, vol. 48(51), pages 4907-4921, November.
    11. Byrne, Joseph P. & Cao, Shuo & Korobilis, Dimitris, 2019. "Decomposing global yield curve co-movement," Journal of Banking & Finance, Elsevier, vol. 106(C), pages 500-513.
    12. Gorgi, Paolo & Koopman, Siem Jan & Li, Mengheng, 2019. "Forecasting economic time series using score-driven dynamic models with mixed-data sampling," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1735-1747.
    13. Söhnke M. Bartram & Gregory Brown & René M. Stulz, 2017. "Why Does Idiosyncratic Risk Increase with Market Risk?," CESifo Working Paper Series 6560, CESifo.
    14. Shen, Wenyi, 2015. "News, disaster risk, and time-varying uncertainty," Journal of Economic Dynamics and Control, Elsevier, vol. 51(C), pages 459-479.
    15. Giovanni Caggiano & Efrem Castelnuovo & Gabriela Nodari, 2014. "Uncertainty and Monetary Policy in Good and Bad Times," "Marco Fanno" Working Papers 0188, Dipartimento di Scienze Economiche "Marco Fanno".
    16. 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.
    17. Rangan Gupta & Chi Keung Marco Lau & Mark E. Wohar, 2019. "The impact of US uncertainty on the Euro area in good and bad times: evidence from a quantile structural vector autoregressive model," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 46(2), pages 353-368, May.
    18. Gigout, Timothee, 2019. "Firm dynamics in an global and uncertain economy," MPRA Paper 96569, University Library of Munich, Germany, revised 16 Oct 2019.
    19. Graziano Moramarco, 2020. "Measuring Global Macroeconomic Uncertainty," Working Papers wp1148, Dipartimento Scienze Economiche, Universita' di Bologna.
    20. Lautenbacher, Stefan, 2020. "Subjective Uncertainty, Expectations, and Firm Behavior," MPRA Paper 103516, University Library of Munich, Germany.
    21. Tosapol Apaitan & Pongsak Luangaram & Pym Manopimoke, 2020. "Uncertainty and Economic Activity: Does it Matter for Thailand?," PIER Discussion Papers 130, Puey Ungphakorn Institute for Economic Research, revised Apr 2020.
    22. 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.
    23. Han, Liyan & Liu, Yang & Yin, Libo, 2019. "Uncertainty and currency performance: A quantile-on-quantile approach," The North American Journal of Economics and Finance, Elsevier, vol. 48(C), pages 702-729.
    24. George P. Gao & Xiaomeng Lu & Zhaogang Song, 2019. "Tail Risk Concerns Everywhere," Management Science, INFORMS, vol. 65(7), pages 3111-3130, July.
    25. Hie Joo Ahn & Ling Shao, 2017. "Precautionary On-the-Job Search over the Business Cycle," Finance and Economics Discussion Series 2017-025, Board of Governors of the Federal Reserve System (U.S.).

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    More about this item

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • G01 - Financial Economics - - General - - - Financial Crises
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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