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Weather Derivative Pricing with Nonlinear Weather Forecasting

Authors: Gal Zahavi; Yaron Rosenstein;

Weather Derivative Pricing with Nonlinear Weather Forecasting

Abstract

In recent years we witnessed a rapid growth of weather derivatives market. These derivatives are used to hedge energy contracts and distribute weather risk. While most derivative markets are complete and contingent climes replications are standard procedure, this special market is incomplete, and therefore modeling the weather is a more appropriate approach to pricing. In this work we base our modeling on a widely accepted physical approach, we use Navier-Stokes equations applied to a thin atmosphere as presented by Lorentz 1962. This modeling is considered by meteorologists a “very-long-weather” prediction, allows for an accurate and robust temperature forecasting. We show that under this setting we empirically outperform the standard approach to weather derivative pricing.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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