Artificial Intelligence Approach for Modeling and Forecasting Oil-Price Volatility

Abstract

Summary Oil market volatility affects macroeconomic conditions and can unduly affect the economies of oil-producing countries. Large price swings can be detrimental to producers and consumers, causing infrastructure and capacity investments to be delayed, employment losses, inefficient investments, and/or the growth potential for energy-producing countries to be adversely affected. Undoubtedly, greater stability of oil prices increases the certainty of oil markets for the benefit of oil consumers and producers. Therefore, modeling and forecasting crude-oil price volatility is a strategic endeavor for many oil market and investment applications. This paper focuses on the development of a new predictive model for describing and forecasting the behavior and dynamics of global oil-price volatility. Using a hybrid approach of artificial intelligence with a genetic algorithm (GA), artificial neural network (ANN), and data mining (DM) time-series (TS), a (GANNATS) model was developed to forecast the futures price volatility of West Texas Intermediate (WTI) crude. The WTI price volatility model was successfully designed, trained, verified, and tested using historical oil market data. The predictions from the GANNATS model closely matched the historical data of WTI futures price volatility. The model not only described the behavior and captured the dynamics of oil-price volatility, but also demonstrated the capability for predicting the direction of movements of oil market volatility with an accuracy of 88%. The model is applicable as a predictive tool for oil-price volatility and its direction of movements, benefiting oil producers, consumers, investors, and traders. It assists these key market players in making sound decisions and taking corrective courses of action for oil market stability, development strategies, and future investments; this could lead to increased profits and to reduced costs and market losses. In addition, this improved method for modeling oil-price volatility enables experts and market analysts to empirically test new approaches for mitigating market volatility. It also provides a roadmap for improving the predictability and accuracy of energy and crude models.