Spatial-temporal stochastic modelling of weather

Fred Espen Benth and Jūratė Šaltytė Benth
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Fred Espen Benth: University of Oslo, Norway
Jūratė Šaltytė Benth: University of Oslo, Norway

Chapter 3 in Modeling and Pricing in Financial Markets for Weather Derivatives, 2012, pp 35-73 from World Scientific Publishing Co. Pte. Ltd.

Abstract: AbstractOur main goal in this Chapter is to build stochastic models for weather variables that are simple statistically but yet sufficiently sophisticated to explain the basic stylized facts of the weather dynamics. We aim at building a model for each of the three variables; temperature, wind speed and precipitation. As it turns out, the wind speed and temperature dynamics share some statistical properties that makes it possible to apply the same kind of stochastic processes to model their dynamics, namely ARMA time series. Precipitation has different properties, and the model class for this weather variable will be chosen separately. Our aim is to construct models which allow for an explicit derivation of prices of various financial weather derivatives contracts. To reach this goal, time series models that can be formulated in continuous time are particularly attractive. The models suggested in this Chapter satisfy this property. In Chapter 4 we discuss the continuous-time analogues of the stochastic models introduced here.We applied Matlab v7.0, SPSS v16 and Excel in all the statistical analyses of this Chapter.

Keywords: Weather Derivatives; Stochastic Processes; HDD; CDD; Autoregressive Moving Average Time Series; Futures Contracts; Options; Utility Pricing; Girsanov Transform; Esscher Transform; Precipitation; Temperature; Wind Speed (search for similar items in EconPapers)
Date: 2012
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