Weather Forecasting for Weather Derivatives / Sean D. Campbell, Francis X. Diebold.

Format:

Book

Author/Creator:

Campbell, Sean D.

Contributor:

National Bureau of Economic Research.

Diebold, Francis X.

Series:

Working Paper Series (National Bureau of Economic Research) no. w10141.

NBER working paper series no. w10141

Language:

English

Physical Description:

1 online resource: illustrations (black and white);

Place of Publication:

Cambridge, Mass. National Bureau of Economic Research 2003.

Summary:

We take a simple time-series approach to modeling and forecasting daily average temperature in 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. The approach can easily be used to produce not only short-horizon point forecasts, but also the long-horizon density forecasts of maximal relevance in weather derivatives contexts. We produce and evaluate both, with some success. We conclude that additional inquiry into nonstructural weather forecasting methods will likely prove useful in weather derivatives contexts.

Notes:

Print version record

December 2003.