Seasonality in the Statistics of Surface Air Temperature and the Pricing of Weather Derivatives

22 Pages Posted: 25 Jul 2003

See all articles by Stephen Jewson

Stephen Jewson

Risk Management Solutions

Rodrigo Caballero

University of Chicago - Department of the Geophysical Sciences

Date Written: October 2002

Abstract

The pricing of weather derivatives motivates the need to build accurate statistical models of daily temperature variability. Current published models are shown to be inaccurate for locations that show strong seasonality in the probability distribution and autocorrelation structure of temperature anomalies. With respect to the first of these problems, we present a new transform that allows seasonally varying non-normal temperature anomaly distributions to be cast into normal distributions. With respect to the second we present a new parametric time-series model that captures both the seasonality and the slow decay of the autocorrelation structure of observed temperature anomalies. This model is valid when the seasonality is slowly varying. Finally we present a simple non-parametric method for the modelling of daily temperatures that is accurate in all cases including extreme non-normality and rapidly varying seasonality.

Keywords: weather derivatives, weather risk, weather forecasts, ensemble forecasts, probabilistic forecasts, daily temperatures, temperature time-series

JEL Classification: G12, G13

Suggested Citation

Jewson, Stephen and Caballero, Rodrigo, Seasonality in the Statistics of Surface Air Temperature and the Pricing of Weather Derivatives (October 2002). Available at SSRN: https://ssrn.com/abstract=405781 or http://dx.doi.org/10.2139/ssrn.405781

Stephen Jewson (Contact Author)

Risk Management Solutions ( email )

London EC3R 8NB
United Kingdom

Rodrigo Caballero

University of Chicago - Department of the Geophysical Sciences ( email )

5734 S. Ellis Avenue
Chicago, IL 60637
United States
001 773 702 9505 (Phone)

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