Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
Compared to the past literature on prediction markets that uses small-scale observational field data or experiments, this present research examines the efficiency of such markets by studying catastrophe (CAT) bonds. We collect actual catastrophe loss data, match them with the defined trigger events of each CAT bond contract, and then employ an empirical pricing framework to obtain the excess CAT premiums in order to represent the market-based forecasts. Our results indeed show that market-based forecasts have more significant predictive content for future CAT losses than professional forecasts that use natural catastrophe risk models. Although the predictive information for CAT events is specialized and complex, our evidence supports that CAT bond markets are successful prediction markets that efficiently aggregate information about future CAT losses. Our resultsalso highlight that actual CAT losses in future periods can explain the excess CAT bond spreads in the primary market and provide support for market efficiency when pricing CAT risk.