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Rare disaster concerns and economic fluctuations

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  • Hao, Yijun
  • Su, Hao
  • Zhu, Xiaoneng

Abstract

This paper provides empirical evidence that disaster risk concerns predict real economic fluctuations. We construct a forward-looking disaster concerns measure using Manela and Moreira (2017) indicators. Consistent with theories on rare disaster risks and macro-economy, this measure forecasts future economic activities.

Suggested Citation

  • Hao, Yijun & Su, Hao & Zhu, Xiaoneng, 2020. "Rare disaster concerns and economic fluctuations," Economics Letters, Elsevier, vol. 195(C).
  • Handle: RePEc:eee:ecolet:v:195:y:2020:i:c:s0165176520302810
    DOI: 10.1016/j.econlet.2020.109454
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    References listed on IDEAS

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    1. Ivo Welch & Amit Goyal, 2008. "A Comprehensive Look at The Empirical Performance of Equity Premium Prediction," Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1455-1508, July.
    2. François Gourio, 2013. "Credit Risk and Disaster Risk," American Economic Journal: Macroeconomics, American Economic Association, vol. 5(3), pages 1-34, July.
    3. Francois Gourio, 2012. "Disaster Risk and Business Cycles," American Economic Review, American Economic Association, vol. 102(6), pages 2734-2766, October.
    4. David López-Salido & Jeremy C. Stein & Egon Zakrajšek, 2017. "Credit-Market Sentiment and the Business Cycle," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 132(3), pages 1373-1426.
    5. Dashan Huang & Fuwei Jiang & Jun Tu & Guofu Zhou, 2015. "Investor Sentiment Aligned: A Powerful Predictor of Stock Returns," Review of Financial Studies, Society for Financial Studies, vol. 28(3), pages 791-837.
    6. Newey, Whitney K & West, Kenneth D, 1987. "Hypothesis Testing with Efficient Method of Moments Estimation," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 28(3), pages 777-787, October.
    7. Emmanuel Farhi & Xavier Gabaix, "undated". "Rare Disasters and Exchange Rates," Working Paper 71001, Harvard University OpenScholar.
    8. Jessica A. Wachter, 2013. "Can Time-Varying Risk of Rare Disasters Explain Aggregate Stock Market Volatility?," Journal of Finance, American Finance Association, vol. 68(3), pages 987-1035, June.
    9. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    10. James H. Stock & Mark W.Watson, 2003. "Forecasting Output and Inflation: The Role of Asset Prices," Journal of Economic Literature, American Economic Association, vol. 41(3), pages 788-829, September.
    11. Manela, Asaf & Moreira, Alan, 2017. "News implied volatility and disaster concerns," Journal of Financial Economics, Elsevier, vol. 123(1), pages 137-162.
    12. Kelly, Bryan & Pruitt, Seth, 2015. "The three-pass regression filter: A new approach to forecasting using many predictors," Journal of Econometrics, Elsevier, vol. 186(2), pages 294-316.
    13. Bryan Kelly & Seth Pruitt, 2013. "Market Expectations in the Cross-Section of Present Values," Journal of Finance, American Finance Association, vol. 68(5), pages 1721-1756, October.
    14. John Y. Campbell & Samuel B. Thompson, 2008. "Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?," Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1509-1531, July.
    15. Simon Gilchrist & Egon Zakrajsek, 2012. "Credit Spreads and Business Cycle Fluctuations," American Economic Review, American Economic Association, vol. 102(4), pages 1692-1720, June.
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    More about this item

    Keywords

    Disaster risk; Predictability; Economic forecast;
    All these keywords.

    JEL classification:

    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles
    • G1 - Financial Economics - - General Financial Markets

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