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Weather forecasting for weather derivatives

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  • Campbell, Sean D.
  • Diebold, Francis X.

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 conditional mean dynamics, and crucially, strong conditional variance dynamics, in daily average temperature, and it reveals sharp differences between the distribution of temperature and the distribution of temperature surprises. As we argue, it also holds promise for producing the long-horizon predictive densities crucial for pricing weather derivatives, so that additional inquiry into time-series weather forecasting methods will likely prove useful in weather derivatives contexts.

Suggested Citation

  • Campbell, Sean D. & Diebold, Francis X., 2004. "Weather forecasting for weather derivatives," CFS Working Paper Series 2004/10, Center for Financial Studies (CFS).
  • Handle: RePEc:zbw:cfswop:200410
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    References listed on IDEAS

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    More about this item

    Keywords

    Risk management; hedging; insurance; seasonality; temperature; financial derivatives;

    JEL classification:

    • G1 - Financial Economics - - General Financial Markets

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