Artificial Intelligence Approach for Modeling and Forecasting Oil-Price Volatility
- Saud M. Al-Fattah (Saudi Aramco)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Evaluation & Engineering
- Publication Date
- August 2019
- Document Type
- Journal Paper
- 817 - 826
- 2019.Society of Petroleum Engineers
- oil market modeling, oil price forecasting, oil price volatility, artificial intelligence, oil market analysis
- 15 in the last 30 days
- 175 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 12.00|
|SPE Non-Member Price:||USD 35.00|
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.
|File Size||689 KB||Number of Pages||10|
Al-Fattah, S. M. and Startzman, R. A. 2001. Predicting Natural Gas Production Using Artificial Neural Network. Presented at the SPE Hydrocarbon Economics and Evaluation Symposium, Dallas, Texas, 2–3 April. SPE-68593-MS. https://doi.org/10.2118/68593-MS.
Al-Fattah, S. M. and Startzman, R. A. 2003. Neural Network Approach Predicts U.S. Natural Gas Production. SPE Prod & Fac 18 (2): 84–91. SPE-82411-PA. https://doi.org/10.2118/82411-PA.
Al-Fattah, S. M. and Al-Naim, H. A. 2009. Artificial-Intelligence Technology Predicts Relative Permeability of Giant Carbonate Reservoirs. SPE Res Eval & Eng 12 (1): 96–103. SPE-109018-PA. https://doi.org/10.2118/109018-PA.
Azoff, E. M. 1994. Neural Network Time Series Forecasting of Financial Markets. Chichester, England: John Wiley & Sons Ltd. Inc.
Goldberg, D. E. 1989. Genetic Algorithms. Reading, Massachusetts: Addison Wesley.
Hill, T. and Lewicki, P. 2007. Statistics: Methods and Applications, digital edition. Tulsa, Oklahoma: StatSoft.
Huntington, H., Al-Fattah, S. M., Huang, Z. et al. 2013. Oil Markets and Price Movements: A Survey of Models. Social Science Research Network, USAEE Working Paper No. 13-129. http://ssrn.com/abstract=2277330 or https://doi.org/10.2139/ssrn.2277330.
Huntington, H., Al-Fattah, S. M., Huang, Z. et al. 2014. Oil Price Drivers and Movements: The Challenge for Future Research. Alternative Investment Analyst Review 2 (4): 11–28.
Matar, W., Al-Fattah, S. M., Atallah, T. et al. 2013. An Introduction to Oil Market Volatility Analysis. OPEC Energy Rev 37 (3): 247–269. OPEC-12007. https://doi.org/10.1111/opec.12007.
Mohaghegh, S. D. 2005. Recent Developments in Application of Artificial Intelligence in Petroleum Engineering. J Pet Technol 57 (4): 86–91. SPE-89033-JPT. https://doi.org/10.2118/89033-JPT.
Mohaghegh, S. D., Al-Fattah, S. M., and Popa, A. 2011. Artificial Intelligence and Data Mining Applications in the E&P Industry, digital edition. Richardson, Texas: Society of Petroleum Engineers.
Narayan, P. and Narayan, S. 2007. Modeling Oil Price Volatility. Energy Policy 35 (12): 6549–6553. https://doi.org/10.1016/j.enpol.2007.07.020.
Pesaran, M. H. and Timmermann, A. 2004. How Costly is it To Ignore Breaks When Forecasting the Direction of a Time Series? Int J Forecast 20 (3): 411–425. https://doi.org/10.1016/S0169-2070(03)00068-2.
Regnier, E. 2007. Oil and Energy Price Volatility. Energy Econ 29 (3): 405–427. https://doi.org/10.1016/j.eneco.2005.11.003.
Sadorsky, P. 2006. Modeling and Forecasting Petroleum Futures Volatility. Energy Econ 28 (4): 467–488. https://doi.org/10.1016/j.eneco.2006.04.005.
Schnader, M. H. and Stekler, H. O. 1990. Evaluating Predictions of Change. J Bus 63 (1): 99–107. https://doi.org/10.1086/296486.
Trippi, R. R. and Turban, E. 1996. Neural Networks in Finance and Investing: Using Artificial Intelligence to Improve Real-World Performance. Chicago: Irwin Professional Publishing.
Wang, Y., Wu, C., and Wei, Y. 2011. Can GARCH-Class Models Capture Long Memory in WTI Crude Oil Markets? Econ Model 28 (3): 921–927. https://doi.org/10.1016/j.econmod.2010.11.002.