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Hot or Cold? A Comparison of Different Approaches to the Pricing of Weather Derivatives
This article reviews six different temperature forecasting models proposed by prior literature for pricing weather derivatives. Simulation of these models is used to estimate daily temperature and, as a consequence, the metrics used for pricing temperature derivatives. The models that rely on an auto-regressive moving average (ARMA) process exhibit a better goodness-of-fit than those that are established under Monte Carlo simulations. However, the superiority of ARMA-type models is not reflected over the forecast horizon. Over that period, the models that rely on Monte Carlo simulations exhibit a tendency to over-forecast the monthly accumulated heating degree day (AccHDD) index and to under-forecast the monthly accumulated cooling degree day (AccCDD) index. Alternatively, models established under the ARMA approach both under-forecast and over-forecast the monthly accumulated indices. All models consistently over-forecast the average daily temperature. The most appropriate pricing model varies between cities and months. Finally, the models examined in this study generate a more accurate AccHDD futures price than the price traded on the market. However, the ability of these models to estimate the AccCDD futures price is significantly poorer than that of the market.
Key Words: Weather derivatives • heating degree days • cooling degree days • forecast error • settlement level • spot price • mis-pricing.
Journal of Emerging Market Finance, Vol. 4, No. 2,
101-133 (2005) | ||
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