Evolution of subjective hurricane risk perceptions: A Bayesian approach

David L. Kelly, David Letson, Forrest Nelson, David S. Nolan, Daniel Solís

Research output: Contribution to journalArticle

10 Scopus citations

Abstract

How do decision makers weight private and official information sources which are correlated and differ in accuracy and bias? This paper studies how traders update subjective risk perceptions after receiving expert opinions, using a unique data set from a prediction market, the Hurricane Futures Market (HFM). We derive a theoretical Bayesian framework which predicts how traders update the probability of a hurricane making landfall in a certain range of coastline, after receiving correlated track forecast information from official and unofficial sources. Our results suggest that traders behave in a way not inconsistent with Bayesian updating but this behavior is based on the perceived quality of the information received. Official information sources are discounted when a perception of bias and credible alternatives exist.

Original languageEnglish (US)
Pages (from-to)644-663
Number of pages20
JournalJournal of Economic Behavior and Organization
Volume81
Issue number2
DOIs
StatePublished - Feb 1 2012

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Keywords

  • Bayesian learning
  • Correlated information
  • Event markets
  • Favorite-longshot bias
  • Hurricanes
  • Prediction markets
  • Risk perceptions

ASJC Scopus subject areas

  • Economics and Econometrics
  • Organizational Behavior and Human Resource Management

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