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Rare Events and Long-Run Risks

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  • Robert J. Barro
  • Tao Jin

Abstract

Rare events (RE) and long-run risks (LRR) are complementary elements for understanding asset-pricing patterns, including the average equity premium and the volatility of equity returns. We construct a model with RE (temporary and permanent parts) and LRR (including stochastic volatility) and estimate this model with long-term data on aggregate consumption for 42 economies. RE typically associates with major historical episodes, such as the world wars and the Great Depression and analogous country- specific events. LRR reflects gradual and evolving processes that influence long-run growth rates and volatility. A match between the model and observed average rates of return requires a coefficient of relative risk aversion, γ, around 6. Most of the explanation for the equity premium derives from RE, although LRR makes a moderate contribution. We think the required γ will decline (and, thereby, become more realistic) if we allow for incomplete information about the underlying shocks, including the breakdown of RE into temporary and permanent parts. We thought that the addition of LRR to the RE framework would help to match the observed volatility of equity returns. However, the joint model still substantially understates this volatility. We think this aspect of the model will improve if we allow for stochastic evolution of the disaster probability.

Suggested Citation

  • Robert J. Barro & Tao Jin, 2016. "Rare Events and Long-Run Risks," NBER Working Papers 21871, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:21871
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    References listed on IDEAS

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    1. Mariano M. Croce & Martin Lettau & Sydney C. Ludvigson, 2015. "Investor Information, Long-Run Risk, and the Term Structure of Equity," Review of Financial Studies, Society for Financial Studies, vol. 28(3), pages 706-742.
    2. M. Hashem Pesaran & Davide Pettenuzzo & Allan Timmermann, 2006. "Forecasting Time Series Subject to Multiple Structural Breaks," Review of Economic Studies, Oxford University Press, vol. 73(4), pages 1057-1084.
    3. Fatih Guvenen, 2009. "A Parsimonious Macroeconomic Model for Asset Pricing," Econometrica, Econometric Society, vol. 77(6), pages 1711-1750, November.
    4. Riccardo Colacito & Mariano M. Croce, 2011. "Risks for the Long Run and the Real Exchange Rate," Journal of Political Economy, University of Chicago Press, vol. 119(1), pages 153-181.
    5. 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.
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    Cited by:

    1. Robert J. Barro & Jesús Fernández-Villaverde & Oren Levintal & Andrew Mollerus, 2014. "Safe Assets," NBER Working Papers 20652, National Bureau of Economic Research, Inc.
      • Barro, Robert J. & Fern�ndez-Villaverde, Jes�s & Levintal, Oren & Mollerus, Andrew, 2017. "Safe Assets," CEPR Discussion Papers 12043, C.E.P.R. Discussion Papers.
      • Robert Barro & Jesus Fernandez-Villaverde & Oren Levintal & Andrew Mollerus, 2017. "Safe Assets," PIER Working Paper Archive 17-008, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 10 May 2017.

    More about this item

    JEL classification:

    • E0 - Macroeconomics and Monetary Economics - - General
    • G01 - Financial Economics - - General - - - Financial Crises
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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