Evolution of subjective hurricane risk perceptions: A Bayesian approach

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

doi:10.1016/j.jebo.2011.10.004

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 journal › Article › peer-review

12 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 language	English (US)
Pages (from-to)	644-663
Number of pages	20
Journal	Journal of Economic Behavior and Organization
Volume	81
Issue number	2
DOIs	https://doi.org/10.1016/j.jebo.2011.10.004
State	Published - Feb 2012

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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10.1016/j.jebo.2011.10.004

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title = "Evolution of subjective hurricane risk perceptions: A Bayesian approach",

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.",

keywords = "Bayesian learning, Correlated information, Event markets, Favorite-longshot bias, Hurricanes, Prediction markets, Risk perceptions",

author = "Kelly, {David L.} and David Letson and Forrest Nelson and Nolan, {David S.} and Daniel Sol{\'i}s",

note = "Funding Information: Research support grants from the University of Miami Abess Center for Ecosystems Science and Policy , and the University of Miami School of Business Administration are gratefully acknowledged. We would like to thank three anonymous referees, Nuray Akin, Ralphael Boleslavsky, Richard Howarth, Oscar Mitnik, Elizabeth Witham, Nick Vriend, and seminar participants at the American Meteorological Society Meetings, the University of Miami Rosenstiel School of Marine and Atmospheric Science, the Eleventh Occasional Workshop on Environmental and Resource Economics, the Conference on Uncertainty and Learning in the Management of Environmental and Resource Economics at UC-Santa Barbara, and the Wharton Center for Risk Management at the University of Pennsylvania for comments and suggestions. We also thank two anonymous traders for discussing their trading strategies with us in interviews. ",

year = "2012",

month = feb,

doi = "10.1016/j.jebo.2011.10.004",

language = "English (US)",

volume = "81",

pages = "644--663",

journal = "Journal of Economic Behavior and Organization",

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TY - JOUR

T1 - Evolution of subjective hurricane risk perceptions

T2 - A Bayesian approach

AU - Kelly, David L.

AU - Letson, David

AU - Nelson, Forrest

AU - Nolan, David S.

AU - Solís, Daniel

N1 - Funding Information: Research support grants from the University of Miami Abess Center for Ecosystems Science and Policy , and the University of Miami School of Business Administration are gratefully acknowledged. We would like to thank three anonymous referees, Nuray Akin, Ralphael Boleslavsky, Richard Howarth, Oscar Mitnik, Elizabeth Witham, Nick Vriend, and seminar participants at the American Meteorological Society Meetings, the University of Miami Rosenstiel School of Marine and Atmospheric Science, the Eleventh Occasional Workshop on Environmental and Resource Economics, the Conference on Uncertainty and Learning in the Management of Environmental and Resource Economics at UC-Santa Barbara, and the Wharton Center for Risk Management at the University of Pennsylvania for comments and suggestions. We also thank two anonymous traders for discussing their trading strategies with us in interviews.

PY - 2012/2

Y1 - 2012/2

N2 - 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.

AB - 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.

KW - Bayesian learning

KW - Correlated information

KW - Event markets

KW - Favorite-longshot bias

KW - Hurricanes

KW - Prediction markets

KW - Risk perceptions

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Evolution of subjective hurricane risk perceptions: A Bayesian approach

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