Weather Derivatives and Seasonal Forecast

The purpose of this paper is to incorporate the CPC (Climate Prediction Center) seasonal forecast in the temperature process so that conditional means and conditional variances of both the temperature and the CDD (Cooling Degree Day) may be redetermined in accordance with the seasonal forecasting probabilities. Under the Gaussian property of the underlying process, the prices of the CDD options conditioned on the seasonal forecasting can be calculated by both the pricing formula and the Monte Carlo simulation. Using the temperature data of five cities in the east coast of the United States, first in the case where there is no truncation in the temperature process, the Monte Carlo simulation shows the appropriate accuracy, which means that the CDD option values obtained through both the pricing formula and the Monte Carlo simulation are close enough. And the magnitude of changes in option values conditional on the seasonal forecast are relatively small. In cases where temperature paths less than 65◦F are truncated, however, the option values obtained by the Monte Carlo simulation are very sensitive to the seasonal forecast probabilities and the magnitude of their variations is very substantial. This is because the density of the CDD conditional on the seasonal forecast shifts a large amount as a result of both truncation and aggregation. JEL Classification: C15, G13.