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Recursive preferences, learning and large deviations

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  • Dave, Chetan
  • Tsang, Kwok Ping

Abstract

We estimate the relative contribution of recursive preferences versus adaptive learning in accounting for the tail thickness of price–dividends/rents ratios. We find that both of these sources of volatility account for volatility in liquid (stocks) but not illiquid (housing) assets.

Suggested Citation

  • Dave, Chetan & Tsang, Kwok Ping, 2014. "Recursive preferences, learning and large deviations," Economics Letters, Elsevier, vol. 124(3), pages 329-334.
    • Handle: RePEc:eee:ecolet:v:124:y:2014:i:3:p:329-334
    DOI: 10.1016/j.econlet.2014.06.014
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    References listed on IDEAS

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    1. Jess Benhabib & Alberto Bisin & Shenghao Zhu, 2011. "The Distribution of Wealth and Fiscal Policy in Economies With Finitely Lived Agents," Econometrica, Econometric Society, vol. 79(1), pages 123-157, January.
    2. Larry G. Epstein & Stanley E. Zin, 2013. "Substitution, risk aversion and the temporal behavior of consumption and asset returns: A theoretical framework," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 12, pages 207-239, World Scientific Publishing Co. Pte. Ltd..
    3. Epstein, Larry G & Zin, Stanley E, 1991. "Substitution, Risk Aversion, and the Temporal Behavior of Consumption and Asset Returns: An Empirical Analysis," Journal of Political Economy, University of Chicago Press, vol. 99(2), pages 263-286, April.
    4. Lucas, Robert E, Jr, 1978. "Asset Prices in an Exchange Economy," Econometrica, Econometric Society, vol. 46(6), pages 1429-1445, November.
    5. Jess Benhabib & Chetan Dave, 2014. "Learning, Large Deviations and Rare Events," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 17(3), pages 367-382, July.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Chetan Dave & Scott J. Dressler & Samreen Malik, 2022. "A Cautionary Tale of Fat Tails," Villanova School of Business Department of Economics and Statistics Working Paper Series 53, Villanova School of Business Department of Economics and Statistics.
    2. Michele Berardi, 2020. "A probabilistic interpretation of the constant gain learning algorithm," Bulletin of Economic Research, Wiley Blackwell, vol. 72(4), pages 393-403, October.
    3. Dave, Chetan & Sorge, Marco, 2023. "Fat Tailed DSGE Models: A Survey and New Results," Working Papers 2023-3, University of Alberta, Department of Economics.
    4. Dave, Chetan & Sorge, Marco M., 2020. "Sunspot-driven fat tails: A note," Economics Letters, Elsevier, vol. 193(C).
    5. Dave, Chetan & Sorge, Marco, 2020. "Equilibrium Indeterminacy and Extreme Outcomes: A Fat Sunspot Ta(i)l(e)," Working Papers 2020-12, University of Alberta, Department of Economics.
    6. Dave, Chetan & Sorge, Marco M., 2021. "Equilibrium indeterminacy and sunspot tales," European Economic Review, Elsevier, vol. 140(C).

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    More about this item

    Keywords

    Recursive preferences; Adaptive learning; Large deviations; Fat tails; Asset prices;
    All these keywords.

    JEL classification:

    • D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations

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