Publication Abstract

Temperature Modeling and Weather Derivative Pricing

J. Lee* and R. Craine

Most of the weather derivatives traded are based on temperature among the end-users - gas and power, utilities and retail, agriculture, travel, transport and distribution leisure and tourism firms. We have shown that the mean reverting nature of temperature models, which appears to be a necessary component, tend to drag temperatures back towards average, reducing the possibility of a persistently warmer or colder season appearing in the modeled trajectories. Anecdotally we have all observed particularly mild winters or hot summers, and examination of the data reveals this to be more than just selective confirmation bias – there are certain winters that are persistently warmer than average throughout – an occurrence that may happen more often due to Climate Change. It is possible that by incorporating long range weather forecasts, or by using a variable speed of mean reversion as applied in Zapranis and Alexandridis (2008), this weakness may be overcome. The variability of temperature is shown greatest during the winter periods, which suggests that where a company’s results are correlated with temperature, the use of weather derivatives would be of most benefit during these periods.

Reference

J. Lee* and R. Craine (2012) Temperature Modeling and Weather Derivative Pricing. American Journal of Scientific Research, 77, 93-109.

http://www.eurojournals.com/AJSR_77_11.pdf