Artificial Neural Network Models for Forecasting Global Oil Market Volatility

Energy market volatility affects macroeconomic conditions and can unduly affect the economies of energy-producing countries. Large price swings can be detrimental to both producers and consumers. Market volatility can cause infrastructure and capacity investments to be delayed, employment losses, and inefficient investments. In sum, the growth potential for energy-producing countries is adversely affected. Undoubtedly, greater stability of oil prices can reduce uncertainty in energy markets, for the benefit of consumers and producers alike. Therefore, modeling and forecasting crude oil price volatility is critical in many financial and investment applications. The purpose of this paper to develop new predictive models for describing and forecasting the global oil price volatility using artificial intelligence with artificial neural network (ANN) modeling technology. Applying the novel approach of ANN, two models were successfully developed: one for WTI futures price volatility and the other for WTI spot prices volatility. These models were successfully designed, trained, verified, and tested using historical oil market data. The estimations and predictions from the ANN models closely match the historical data of WTI from January 1994 to April 2012. They appear to capture very well the dynamics and the direction of the oil price volatility. These ANN models developed in this study can be used: as short-term as well as long-term predictive tools for the direction of oil price volatility, to quantitatively examine the effects of various physical and economic factors on future oil market volatility, to understand the effects of different mechanisms for reducing market volatility, and to recommend policy options and programs incorporating mechanisms that can potentially reduce the market volatility. With this improved method for modeling oil price volatility, experts and market analysts will be able to empirically test new approaches to mitigating market volatility. The outcome of this work provides a roadmap for research to improve predictability and accuracy of energy and crude models