Wavelet Neural Networks: With Applications in Financial Engineering, Chaos, and Classification

8 Modeling Financial Temperature Derivatives

In this chapter a real data set is used to demonstrate the application of our proposed framework. We focus on a financial application in forecasting the prices of weather derivatives. A weather derivative is a financial instrument that has a payoff derived from variables such as temperature, snowfall, humidity, and rainfall. Since their inception in 1996, weather derivatives have shown substantial growth. The first parties to arrange for, and issue, weather derivatives in 1996 were energy companies, which, after the deregulation of energy markets, were exposed to weather risk. In September 1999, the Chicago Mercantile Exchange (CME) launched the first exchange-traded weather derivatives. Today, weather derivatives are being used for hedging purposes by companies and industries whose profits can be affected adversely by unseasonal weather or, for speculative purposes, by hedge funds and others interested in capitalizing on those volatile markets.

Weather risk is unique in that it is highly localized, and despite great advances in meteorological science, still cannot be predicted precisely and consistently. Weather derivatives are also different from other financial derivatives in that the underlying weather index (i.e., HDD, CDD, CAT, etc.) cannot be traded. Furthermore, the corresponding market is relatively illiquid. Consequently, since weather derivatives cannot be replicated cost-efficiently by other weather derivatives, arbitrage ...