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Asset Pricing under Rational Learning about Rare Disasters

Author

Listed:
  • Volker Wieland

    (Goethe University Frankfurt)

  • Christos Koulovatianos

    (University of Nottingham)

Abstract

This paper proposes a new approach for modeling investor fear after rare disasters. The key element is to take into account that investors' information about fundamentals driving rare downward jumps in the dividend process is not perfect. Bayesian learning implies that beliefs about the likelihood of rare disasters drop to a much more pessimistic level once a disaster has occurred. Such a shift in beliefs can trigger massive declines in price-dividend ratios. Pessimistic beliefs persist for some time. Thus, belief dynamics are a source of apparent excess volatility relative to a rational expectations benchmark. Due to the low frequency of disasters, even an infinitely-lived investor will remain uncertain about the exact probability. Our analysis is conducted in continuous time and offers closed-form solutions for asset prices. We distinguish between rational and adaptive Bayesian learning. Rational learners account for the possibility of future changes in beliefs in determining their demand for risky assets, while adaptive learners take beliefs as given. Thus, risky assets tend to be lower-valued and price-dividend ratios vary less under adaptive versus rational learning for identical priors.

Suggested Citation

  • Volker Wieland & Christos Koulovatianos, 2011. "Asset Pricing under Rational Learning about Rare Disasters," 2011 Meeting Papers 1417, Society for Economic Dynamics.
  • Handle: RePEc:red:sed011:1417
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Roberto Marfè & Julien Penasse, 2016. "The Time-Varying Risk of Macroeconomic Disasters," Carlo Alberto Notebooks 463, Collegio Carlo Alberto.
    2. Carolina Achury & Christos Koulovatianos & John Tsoukalas, "undated". "External Sovereign Debt in a Monetary Union: Bailouts and the Role of Corruption," Discussion Papers 11/11, University of Nottingham, Centre for Finance, Credit and Macroeconomics (CFCM).
    3. 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.
    4. Christos Koulovatianos, 2015. "Strategic Exploitation of a Common-Property Resource Under Rational Learning About its Reproduction," Dynamic Games and Applications, Springer, vol. 5(1), pages 94-119, March.

    More about this item

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
    • E21 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Consumption; Saving; Wealth
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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