Weather Forecasting for Weather Derivatives

Sean D. Campbell and Francis Diebold

No 10141, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: 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.

JEL-codes: G1 (search for similar items in EconPapers)
Date: 2003-12
New Economics Papers: this item is included in nep-cfn
Note: AP
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Citations: View citations in EconPapers (8)

Published as Sean D. Campbell & Francis X. Diebold, 2005. "Weather Forecasting for Weather Derivatives," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 6-16, March.

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Related works:
Journal Article: Weather Forecasting for Weather Derivatives (2005)
Working Paper: Weather forecasting for weather derivatives (2004)
Working Paper: Weather Forecasting for Weather Derivatives (2002)
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