Evolution of Subjective Hurricane Risk Perceptions: A Bayesian Approach

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.risk perceptions, learning, Bayesian learning, event markets, prediction markets, favorite-longshot bias, hurricanes