Deregulation Of The US Energy Industry

The weather market follows its roots to deregulation of the U.S. energy industry. Variability in weather conditions perceived as one of the most vital factors that affect energy expenditure; however, the effects of inconstant seasonal weather patterns had previously been incorporated and operated within a regulated, monopoly environment. With deregulation, the various shareholders in the manner of producing, marketing, and delivering energy to U.S. households and corporations were left to confront weather as a new and significant risk to their bottom line. New pathfinders in the energy market traders Aquila, Koch, Enron, and Industries conceived of and executed the first weather derivative transactions in 1997. The first deals were all determined

The early market participants saw weather derivatives as both a mechanism to hedge inherent weather exposure in their essence energy assets and other energy commodity trading operations as well as a new risk management product to offer to regional utilities and other energy concerns alongside the array of structured products they were already providing.
Evolution of the Market
The earliest weather derivative contracts arose in the US in 1997, where many determinants promoted their foundation at this period. Federal deregulation of the power division created a competitive market for electricity. A U.S. Department of Commerce estimate indicates that more than $1 trillion of U.S. economic activity exhibited to the weather, and transactions over the past various years have provided weather assurance to companies in divisions as diverse as agriculture, construction, entertainment, and retail. Before deregulation,

These instruments allow organizations to protect themselves against the commercial risks posed by weather fluctuations and also provide investment opportunities for financial traders. In 2005/2006, the size of the market for weather derivatives is substantial, with a survey suggesting that the market size exceeded $45.2 Billion with most written contracts on temperature-based metrics. A critical problem faced by buyers and sellers of weather derivatives is the determination of an appropriate pricing model (and resulting price) for the financial instrument. Significant input into the pricing model is an accurate forecast of the underlying weather metric. This study adopted a time-series modeling approach to the production of annual weather-metric estimates, as a part of a general method for pricing weather derivatives. Two GP-based methods for time series modeling used; the first one based on standard symbolic regression; the second one based on autoregressive time-series modeling that realized via an iterated single-step prediction process and a specially crafted terminal set of historical time-series values. Results are very encouraging, suggesting that GP can successfully evolve accurate seasonal temperature forecasting models. The use of ensemble learning of 5-model predictors enhanced the