Modeling Energy Price Volatility

Say the words “Energy price volatility” and many people will think of the OPEC oil price hikes of the 1970s or perhaps the more recent 2004 sharp upswing in the price of crude oil. Yet price volatility is a characteristic of capitalism and freely operating energy markets are no exception. Accurate estimates of the variation in energy asset values over time are important for the valuation of financial contracts, retail obligations, physical assets, and in solving portfolio allocation problems. As a consequence, modeling and forecasting of price volatility has acquired an unprecedented significance in the industry. In response, various attempts have been made to develop statistical tools to help characterize and predict price volatility. In general these models fall into three categories, Exponentially Weighted Moving Average models, Generalized Autoregressive Conditional Hetroscedasticity models, and Stochastic Volatility Differential Equations. In this chapter we introduce the first two of these modeling approaches. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.