Weather Forecasting for Weather Derivatives
45 Pages Posted: 22 Dec 2002
Date Written: December 4, 2002
We take a nonstructural time-series approach to modeling and forecasting daily average temperature in ten U.S. cities, and we inquire systematically as to whether it may prove useful from the vantage point of participants in the weather derivatives market. The answer is, perhaps surprisingly, yes. Time series modeling reveals both strong conditional mean dynamics and conditional variance dynamics in daily average temperature, and it reveals sharp differences between the distribution of temperature and the distribution of temperature surprises. Most importantly, it adapts readily to produce the long-horizon forecasts of relevance in weather derivatives contexts. We produce and evaluate both point and distributional forecasts of average temperature, with some success. We conclude that additional inquiry into nonstructural weather forecasting methods, as relevant for weather derivatives, will likely prove useful.
Note: An earlier version of this paper can be downloaded at http://ssrn.com/abstract=284950
Keywords: Risk Management, Hedging, Insurance, Seasonality, Average Temperature, Financial Derivatives, Density Forecasting
JEL Classification: G0, C1
Suggested Citation: Suggested Citation