Weather Forecasting for Weather Derivatives

30 Pages Posted: 10 Dec 2003 Last revised: 19 Mar 2021

See all articles by Sean D. Campbell

Sean D. Campbell

Board of Governors of the Federal Reserve System

Francis X. Diebold

University of Pennsylvania - Department of Economics; National Bureau of Economic Research (NBER)

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Date Written: December 2003

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.

Suggested Citation

Campbell, Sean D. and Diebold, Francis X., Weather Forecasting for Weather Derivatives (December 2003). NBER Working Paper No. w10141, Available at SSRN: https://ssrn.com/abstract=476098

Sean D. Campbell

Board of Governors of the Federal Reserve System ( email )

20th Street and Constitution Avenue NW
Washington, DC 20551
United States

Francis X. Diebold (Contact Author)

University of Pennsylvania - Department of Economics ( email )

Ronald O. Perelman Center for Political Science
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HOME PAGE: http://www.ssc.upenn.edu/~fdiebold/

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