Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/25436 
Erscheinungsjahr: 
2004
Schriftenreihe/Nr.: 
CFS Working Paper No. 2004/10
Verlag: 
Goethe University Frankfurt, Center for Financial Studies (CFS), Frankfurt a. M.
Zusammenfassung: 
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.
Schlagwörter: 
Risk management
hedging
insurance
seasonality
temperature
financial derivatives
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Dokumentart: 
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