A Hybrid Intelligent System for Designing a Contract Model for Weather Derivatives.

Abstract: In this paper, a hybrid intelligent system is proposed for designing a contract model of the weather derivatives between energy utilities. In recent years, the weather conditions often bring about uncertainties on profits to companies that are related to weather conditions. As a result, they require a strategy to equalize the profits and avoid unexpected deficits. To satisfy the requirements, weather derivatives are developed to the companies. They may be expressed as the function of the weather conditions such as the average, the maximum temperature, etc. So far, a lot of the derivatives have been developed, but it is not clear how to design them due to the nondisclosure on practical knowledge. In this paper, an efficient method is presented to determine a contract model of weather derivatives. As the first stage, this paper focuses on the normal data. A reasonable model is constructed by the learning process for the normal data. This paper formulates the contract model as a two-phased problem. Phase 1 carries out data clustering of curves to extract the normal data with DA (Deterministic Annealing) of global clustering technique. Phase 2 optimizes the parameters of the model that equalizes the payoffs between two companies in a sense that the mean and the variance of the payoff are equalized for both companies with EPSO (Evolutionary Particle Swarm Optimization) of meta- heuristics. The proposed method is successfully applied to the real data in Tokyo, Japan. [Copyright &y& Elsevier]