Weather forecasting for weather derivatives : [revised version: January 2, 2004]

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 we
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. 
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Metadaten
Author:Sean D. Campbell, Francis X. Diebold
URN:urn:nbn:de:hebis:30-10621
Parent Title (German):Center for Financial Studies (Frankfurt am Main): CFS working paper series ; No. 2004,10
Series (Serial Number):CFS working paper series (2004, 10)
Document Type:Working Paper
Language:English
Year of Completion:2004
Year of first Publication:2004
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2005/06/13
Tag:financial derivatives; hedging; insurance; risk management; seasonality; temperature
SWD-Keyword:Derivat, Wertpapier; USA; Wettervorhersage; Zeitreihe
Issue:revised version: January 2, 2004
Pagenumber:30
HeBIS PPN:221931112
Institutes:Center for Financial Studies (CFS)
Dewey Decimal Classification:330 Wirtschaft
Sammlungen:Universitätspublikationen
Licence (German):License Logo Veröffentlichungsvertrag für Publikationen

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