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Time-series predictability in the disaster model

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  • François Gourio

    () (Boston University, Department of Economics)

Abstract

This paper studies whether the Rietz–Barro “disaster” model, extended for a time-varying probability of disaster, can match the empirical evidence on predictability of stock returns. It is shown that when utility is CRRA, the model cannot replicate this evidence, regardless of parameter values. This motivates extending the disaster model to allow for Epstein–Zin utility. Analytical results show that when the probability of disaster is i.i.d., the model with Epstein–Zin utility can match the evidence on predictability qualitatively if the intertemporal elasticity of substitution is greater than unity. The case of a persistent probability of disaster is studied numerically, with partial success.

Suggested Citation

  • François Gourio, 2008. "Time-series predictability in the disaster model," Boston University - Department of Economics - Working Papers Series wp2008-016, Boston University - Department of Economics.
  • Handle: RePEc:bos:wpaper:wp2008-016
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    Cited by:

    1. François Gourio, 2013. "Credit Risk and Disaster Risk," American Economic Journal: Macroeconomics, American Economic Association, vol. 5(3), pages 1-34, July.
    2. Favero, Carlo A. & Ortu, Fulvio & Tamoni, Andrea & Yang, Haoxi, 2016. "Implications of Return Predictability across Horizons for Asset Pricing Models," CEPR Discussion Papers 11645, C.E.P.R. Discussion Papers.
    3. Henk Berkman & Ben Jacobsen & John B. Lee, 2017. "Rare disaster risk and the expected equity risk premium," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 57(2), pages 351-372, June.
    4. Francois Gourio, 2012. "Disaster Risk and Business Cycles," American Economic Review, American Economic Association, vol. 102(6), pages 2734-2766, October.
    5. Demirer, Riza & Gupta, Rangan & Suleman, Tahir & Wohar, Mark E., 2018. "Time-varying rare disaster risks, oil returns and volatility," Energy Economics, Elsevier, vol. 75(C), pages 239-248.
    6. Gandhi, Priyank & Lustig, Hanno & Plazzi, Alberto, 2016. "Equity Is Cheap for Large Financial Institutions: The International Evidence," Research Papers 3454, Stanford University, Graduate School of Business.
    7. Ludvigson, Sydney C., 2013. "Advances in Consumption-Based Asset Pricing: Empirical Tests," Handbook of the Economics of Finance, in: G.M. Constantinides & M. Harris & R. M. Stulz (ed.), Handbook of the Economics of Finance, volume 2, chapter 0, pages 799-906, Elsevier.
    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. Jerry Tsai & Jessica A. Wachter, 2014. "Rare Booms and Disasters in a Multi-sector Endowment Economy," NBER Working Papers 20062, National Bureau of Economic Research, Inc.
    10. Jerry Tsai, 2013. "Rare Disasters and the Term Structure of Interest Rates," Economics Series Working Papers 665, University of Oxford, Department of Economics.
    11. Koulovatianos, Christos & Wieland, Volker, 2011. "Asset pricing under rational learning about rare disasters," IMFS Working Paper Series 46, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS).
    12. Max Gillman & Michal Kejak & Michal Pakos, 2014. "Learning about Disaster Risk: Joint Implications for Consumption and Asset Prices," CERGE-EI Working Papers wp507, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    13. Manela, Asaf & Moreira, Alan, 2017. "News implied volatility and disaster concerns," Journal of Financial Economics, Elsevier, vol. 123(1), pages 137-162.
    14. Berkman, Henk & Jacobsen, Ben & Lee, John B., 2011. "Time-varying rare disaster risk and stock returns," Journal of Financial Economics, Elsevier, vol. 101(2), pages 313-332, August.
    15. Huynh, Thanh D. & Nguyen, Thu Ha & Truong, Cameron, 2020. "Climate risk: The price of drought," Journal of Corporate Finance, Elsevier, vol. 65(C).
    16. Max Gillman & Michal Kejak & Michal Pakoš, 2015. "Learning about Rare Disasters: Implications For Consumption and Asset Prices," Review of Finance, European Finance Association, vol. 19(3), pages 1053-1104.
    17. Priyank Gandhi & Hanno Lustig, 2010. "Size Anomalies in U.S. Bank Stock Returns: A Fiscal Explanation," NBER Working Papers 16553, National Bureau of Economic Research, Inc.
    18. Jim Dolmas, 2013. "Disastrous disappointments: asset-pricing with disaster risk and disappointment aversion," Working Papers 1309, Federal Reserve Bank of Dallas.
    19. Gourio, François, 2012. "Macroeconomic implications of time-varying risk premia," Working Paper Series 1463, European Central Bank.
    20. Rangan Gupta & Tahir Suleman & Mark E. Wohar, 2019. "The role of time‐varying rare disaster risks in predicting bond returns and volatility," Review of Financial Economics, John Wiley & Sons, vol. 37(3), pages 327-340, July.

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    More about this item

    Keywords

    Rare events; Jumps; Disasters; Equity premium; Return predictability;
    All these keywords.

    JEL classification:

    • E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Interest Rates: Determination, Term Structure, and Effects
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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