Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/25436
Authors: 
Campbell, Sean D.
Diebold, Francis X.
Year of Publication: 
2004
Series/Report no.: 
CFS Working Paper 2004/10
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.
Subjects: 
Risk management
hedging
insurance
seasonality
temperature
financial derivatives
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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