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Asset pricing under rational learning about rare disasters

Author

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  • Koulovatianos, Christos
  • Wieland, Volker
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. Keywords: beliefs, Bayesian learning, controlled diffusions and jump processes, learning about jumps, adaptive learning, rational learning.

Suggested Citation

  • 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).
    • Handle: RePEc:zbw:imfswp:46
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    References listed on IDEAS

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    Cited by:

    1. Marfè, Roberto & Pénasse, Julien, 2024. "Measuring macroeconomic tail risk," Journal of Financial Economics, Elsevier, vol. 156(C).
    2. Gandré, Pauline, 2015. "Asset prices and information disclosure under recency-biased learning," CEPREMAP Working Papers (Docweb) 1515, CEPREMAP.
    3. Carolina Achury & Christos Koulovatianos & John Tsoukalas, 2011. "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).
    4. Christopher A Hennessy & Boris Radnaev, 2018. "Learning and Leverage Cycles in General Equilibrium: Theory and Evidence [How sensitive is investment to cash flow when financing is frictionless?]," Review of Finance, European Finance Association, vol. 22(1), pages 311-335.
    5. 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.
    6. Roberto Marfè & Julien Penasse, 2016. "The Time-Varying Risk of Macroeconomic Disasters," Carlo Alberto Notebooks 463, Collegio Carlo Alberto.
    7. 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.
    8. Koulovatianos, Christos & Mavridis, Dimitris, 2018. "Increasing taxes after a financial crisis: Not a bad idea after all ..," CFS Working Paper Series 614, Center for Financial Studies (CFS).

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    More about this item

    Keywords

    beliefs; Bayesian learning; controlled diffusions and jump processes; learning about jumps; adaptive learning; rational learning;
    All these keywords.

    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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