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Evolution of Subjective Hurricane Risk Perceptions: A Bayesian Approach

Author

Listed:
  • David Kelly

    (Department of Economics, University of Miami)

  • David Letson

    (Rosenstiel School of Marine and Atmospheric Science, University of Miami)

  • Forest Nelson

    (Department of Economics, Henry B. Tippie College of Business Administration, University of Iowa)

  • David S. Nolan

    (Rosenstiel School of Marine and Atmospheric Science, University of Miami)

  • Daniel Solis

    (Rosenstiel School of Marine and Atmospheric Science, University of Miami)

Abstract

This paper studies how individuals update subjective risk perceptions in response to hurricane track forecast information, using a unique data set from an event market, the Hurricane Futures Market (HFM). We derive a theoretical Bayesian framework which predicts how traders update their perceptions of the probability of a hurricane making landfall in a certain range of coastline. Our results suggest that traders behave in a way consistent with Bayesian updating but this behavior is based on the perceived quality of the information received.

Suggested Citation

  • David Kelly & David Letson & Forest Nelson & David S. Nolan & Daniel Solis, 2009. "Evolution of Subjective Hurricane Risk Perceptions: A Bayesian Approach," Working Papers 0905, University of Miami, Department of Economics.
    • Handle: RePEc:mia:wpaper:0905
    as

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    File URL: https://www.herbert.miami.edu/_assets/files/repec/wp2009-05-kelly-hfm3_1_09.pdf
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    References listed on IDEAS

    as
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    Cited by:

    1. Derek Lemoine & Sarah Kapnick, 2024. "Financial markets value skillful forecasts of seasonal climate," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    2. repec:cup:judgdm:v:8:y:2013:i:2:p:91-105 is not listed on IDEAS
    3. Conte, Marc N. & Kelly, David L., 2018. "An imperfect storm: Fat-tailed tropical cyclone damages, insurance, and climate policy," Journal of Environmental Economics and Management, Elsevier, vol. 92(C), pages 677-706.
    4. Craig W. Trumbo & Lori Peek & Michelle A. Meyer & Holly L. Marlatt & Eve Gruntfest & Brian D. McNoldy & Wayne H. Schubert, 2016. "A Cognitive‐Affective Scale for Hurricane Risk Perception," Risk Analysis, John Wiley & Sons, vol. 36(12), pages 2233-2246, December.
    5. Kajii, Atsushi & Watanabe, Takahiro, 2017. "Favorite–longshot bias in pari-mutuel betting: An evolutionary explanation," Journal of Economic Behavior & Organization, Elsevier, vol. 140(C), pages 56-69.
    6. Xu, Haifeng & Ding, Yi & Zhang, Cheng & Tan, Bernard C.Y., 2023. "Too official to be effective: An empirical examination of unofficial information channel and continued use of retracted articles," Research Policy, Elsevier, vol. 52(7).
    7. Karsten Hueffer & Miguel A. Fonseca & Anthony Leiserowitz & Karen M. Taylor, 2013. "The wisdom of crowds: Predicting a weather and climate-related event," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 8(2), pages 91-105, March.

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    More about this item

    Keywords

    risk perceptions; learning; Bayesian learning; event markets; prediction markets; favorite-longshot bias; hurricanes;
    All these keywords.

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • C9 - Mathematical and Quantitative Methods - - Design of Experiments

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