Weather forecasting for weather derivatives
Sean D. Campbell and
Francis Diebold
No 2004/10, CFS Working Paper Series from Center for Financial Studies (CFS)
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.
Keywords: Risk management; hedging; insurance; seasonality; temperature; financial derivatives (search for similar items in EconPapers)
Date: 2004
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Citations: View citations in EconPapers (4)
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https://www.econstor.eu/bitstream/10419/25436/1/515150657.PDF (application/pdf)
Related works:
Journal Article: Weather Forecasting for Weather Derivatives (2005) 
Working Paper: Weather Forecasting for Weather Derivatives (2003) 
Working Paper: Weather Forecasting for Weather Derivatives (2002) 
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:cfswop:200410
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