IDEAS home Printed from https://ideas.repec.org/a/eee/enepol/v68y2014icp371-382.html
   My bibliography  Save this article

Predicting oil price movements: A dynamic Artificial Neural Network approach

Author

Listed:
  • Godarzi, Ali Abbasi
  • Amiri, Rohollah Madadi
  • Talaei, Alireza
  • Jamasb, Tooraj

Abstract

Price of oil is important for the economies of oil exporting and oil importing countries alike. Therefore, insight into the likely future behaviour and patterns of oil prices can improve economic planning and reduce the impacts of oil market fluctuations. This paper aims to improve the application of Artificial Neural Network (ANN) techniques to prediction of oil price. We develop a dynamic Nonlinear Auto Regressive model with eXogenous input (NARX) as a form of ANN to account for the time factor. We estimate the model using macroeconomic data from OECD countries. In order to compare the results, we develop time series and ANN static models. We then use the output of time series model to develop a NARX model. The NARX model is trained with historical data from 1974 to 2004 and the results are verified with data from 2005 to 2009. The results show that NARX model is more accurate than time series and static ANN models in predicting oil prices in general as well as in predicting the occurrence of oil price shocks.

Suggested Citation

  • Godarzi, Ali Abbasi & Amiri, Rohollah Madadi & Talaei, Alireza & Jamasb, Tooraj, 2014. "Predicting oil price movements: A dynamic Artificial Neural Network approach," Energy Policy, Elsevier, vol. 68(C), pages 371-382.
    • Handle: RePEc:eee:enepol:v:68:y:2014:i:c:p:371-382
    DOI: 10.1016/j.enpol.2013.12.049
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S030142151301313X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.enpol.2013.12.049?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ben S. Bernanke & Mark Gertler & Mark Watson, 1997. "Systematic Monetary Policy and the Effects of Oil Price Shocks," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 28(1), pages 91-157.
    2. World Bank, 2011. "The Changing Wealth of Nations : Measuring Sustainable Development in the New Millennium," World Bank Publications - Books, The World Bank Group, number 2252.
    3. Nakamura, Emi, 2005. "Inflation forecasting using a neural network," Economics Letters, Elsevier, vol. 86(3), pages 373-378, March.
    4. Busch, Timo & Raschky, Paul, 2004. "Value-at-risk of carbon constraints : an input oriented approach of resource scarcity," Wuppertal Papers 144, Wuppertal Institute for Climate, Environment and Energy.
    5. Yu, Lean & Wang, Shouyang & Lai, Kin Keung, 2008. "Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm," Energy Economics, Elsevier, vol. 30(5), pages 2623-2635, September.
    6. Finn, Mary G, 2000. "Perfect Competition and the Effects of Energy Price Increases on Economic Activity," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 32(3), pages 400-416, August.
    7. Rotemberg, Julio J & Woodford, Michael, 1996. "Imperfect Competition and the Effects of Energy Price Increases on Economic Activity," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 28(4), pages 550-577, November.
    8. He, Kaijian & Yu, Lean & Lai, Kin Keung, 2012. "Crude oil price analysis and forecasting using wavelet decomposed ensemble model," Energy, Elsevier, vol. 46(1), pages 564-574.
    9. Adrangi, Bahram & Chatrath, Arjun & Dhanda, Kanwalroop Kathy & Raffiee, Kambiz, 2001. "Chaos in oil prices? Evidence from futures markets," Energy Economics, Elsevier, vol. 23(4), pages 405-425, July.
    10. World Bank, 2007. "World Development Indicators 2007," World Bank Publications - Books, The World Bank Group, number 8150.
    11. Jammazi, Rania & Aloui, Chaker, 2012. "Crude oil price forecasting: Experimental evidence from wavelet decomposition and neural network modeling," Energy Economics, Elsevier, vol. 34(3), pages 828-841.
    12. Xu, Xinzhong & Taylor, Stephen J., 1995. "Conditional volatility and the informational efficiency of the PHLX currency options market," Journal of Banking & Finance, Elsevier, vol. 19(5), pages 803-821, August.
    13. Kalogirou, Soteris A., 2000. "Applications of artificial neural-networks for energy systems," Applied Energy, Elsevier, vol. 67(1-2), pages 17-35, September.
    14. Kim, In-Moo & Loungani, Prakash, 1992. "The role of energy in real business cycle models," Journal of Monetary Economics, Elsevier, vol. 29(2), pages 173-189, April.
    15. Robert B. Barsky & Lutz Kilian, 2002. "Do We Really Know That Oil Caused the Great Stagflation? A Monetary Alternative," NBER Chapters, in: NBER Macroeconomics Annual 2001, Volume 16, pages 137-198, National Bureau of Economic Research, Inc.
    16. Tang, Linghui & Hammoudeh, Shawkat, 2002. "An empirical exploration of the world oil price under the target zone model," Energy Economics, Elsevier, vol. 24(6), pages 577-596, November.
    17. Movagharnejad, Kamyar & Mehdizadeh, Bahman & Banihashemi, Morteza & Kordkheili, Masoud Sheikhi, 2011. "Forecasting the differences between various commercial oil prices in the Persian Gulf region by neural network," Energy, Elsevier, vol. 36(7), pages 3979-3984.
    18. Zhang, G. Peter & Qi, Min, 2005. "Neural network forecasting for seasonal and trend time series," European Journal of Operational Research, Elsevier, vol. 160(2), pages 501-514, January.
    19. Amano, Akihiro, 1987. "A small forecasting model of the world oil market," Journal of Policy Modeling, Elsevier, vol. 9(4), pages 615-635.
    20. Ghaffari, Ali & Zare, Samaneh, 2009. "A novel algorithm for prediction of crude oil price variation based on soft computing," Energy Economics, Elsevier, vol. 31(4), pages 531-536, July.
    21. Kang, Sang Hoon & Kang, Sang-Mok & Yoon, Seong-Min, 2009. "Forecasting volatility of crude oil markets," Energy Economics, Elsevier, vol. 31(1), pages 119-125, January.
    22. Panas, Epaminondas & Ninni, Vassilia, 2000. "Are oil markets chaotic? A non-linear dynamic analysis," Energy Economics, Elsevier, vol. 22(5), pages 549-568, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sun, Jingyun & Zhao, Panpan & Sun, Shaolong, 2022. "A new secondary decomposition-reconstruction-ensemble approach for crude oil price forecasting," Resources Policy, Elsevier, vol. 77(C).
    2. Yuanrong Wang & Yinsen Miao & Alexander CY Wong & Nikita P Granger & Christian Michler, 2023. "Domain-adapted Learning and Interpretability: DRL for Gas Trading," Papers 2301.08359, arXiv.org, revised Sep 2023.
    3. Liyang Tang, 2020. "Application of Nonlinear Autoregressive with Exogenous Input (NARX) neural network in macroeconomic forecasting, national goal setting and global competitiveness assessment," Papers 2005.08735, arXiv.org.
    4. Zhao, Yang & Li, Jianping & Yu, Lean, 2017. "A deep learning ensemble approach for crude oil price forecasting," Energy Economics, Elsevier, vol. 66(C), pages 9-16.
    5. Li, Mingchen & Cheng, Zishu & Lin, Wencan & Wei, Yunjie & Wang, Shouyang, 2023. "What can be learned from the historical trend of crude oil prices? An ensemble approach for crude oil price forecasting," Energy Economics, Elsevier, vol. 123(C).
    6. Karasu, Seçkin & Altan, Aytaç, 2022. "Crude oil time series prediction model based on LSTM network with chaotic Henry gas solubility optimization," Energy, Elsevier, vol. 242(C).
    7. Jun Hao & Xiaolei Sun & Qianqian Feng, 2020. "A Novel Ensemble Approach for the Forecasting of Energy Demand Based on the Artificial Bee Colony Algorithm," Energies, MDPI, vol. 13(3), pages 1-25, January.
    8. Sepehr Ramyar & Farhad Kianfar, 2019. "Forecasting Crude Oil Prices: A Comparison Between Artificial Neural Networks and Vector Autoregressive Models," Computational Economics, Springer;Society for Computational Economics, vol. 53(2), pages 743-761, February.
    9. Liu, Mingxi & Li, Guowen & Li, Jianping & Zhu, Xiaoqian & Yao, Yinhong, 2021. "Forecasting the price of Bitcoin using deep learning," Finance Research Letters, Elsevier, vol. 40(C).
    10. Zeng, Sheng & Su, Bin & Zhang, Minglong & Gao, Yuan & Liu, Jun & Luo, Song & Tao, Qingmei, 2021. "Analysis and forecast of China's energy consumption structure," Energy Policy, Elsevier, vol. 159(C).
    11. Haas, Christian & Budin, Constantin & d’Arcy, Anne, 2024. "How to select oil price prediction models — The effect of statistical and financial performance metrics and sentiment scores," Energy Economics, Elsevier, vol. 133(C).
    12. Fang, Tianhui & Zheng, Chunling & Wang, Donghua, 2023. "Forecasting the crude oil prices with an EMD-ISBM-FNN model," Energy, Elsevier, vol. 263(PA).
    13. Cheng, Fangzheng & Li, Tian & Wei, Yi-ming & Fan, Tijun, 2019. "The VEC-NAR model for short-term forecasting of oil prices," Energy Economics, Elsevier, vol. 78(C), pages 656-667.
    14. Shaolong Suna & Dan Bi & Ju-e Guo & Shouyang Wang, 2020. "Seasonal and Trend Forecasting of Tourist Arrivals: An Adaptive Multiscale Ensemble Learning Approach," Papers 2002.08021, arXiv.org, revised Mar 2020.
    15. Nademi, Arash & Nademi, Younes, 2018. "Forecasting crude oil prices by a semiparametric Markov switching model: OPEC, WTI, and Brent cases," Energy Economics, Elsevier, vol. 74(C), pages 757-766.
    16. Xu, Guangyue & Schwarz, Peter & Yang, Hualiu, 2019. "Determining China's CO2 emissions peak with a dynamic nonlinear artificial neural network approach and scenario analysis," Energy Policy, Elsevier, vol. 128(C), pages 752-762.
    17. Lang, Korbinian & Auer, Benjamin R., 2020. "The economic and financial properties of crude oil: A review," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lang, Korbinian & Auer, Benjamin R., 2020. "The economic and financial properties of crude oil: A review," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    2. Marcos Álvarez-Díaz, 2020. "Is it possible to accurately forecast the evolution of Brent crude oil prices? An answer based on parametric and nonparametric forecasting methods," Empirical Economics, Springer, vol. 59(3), pages 1285-1305, September.
    3. Ghaffari, Ali & Zare, Samaneh, 2009. "A novel algorithm for prediction of crude oil price variation based on soft computing," Energy Economics, Elsevier, vol. 31(4), pages 531-536, July.
    4. Zhang, Jin-Liang & Zhang, Yue-Jun & Zhang, Lu, 2015. "A novel hybrid method for crude oil price forecasting," Energy Economics, Elsevier, vol. 49(C), pages 649-659.
    5. Francesca Rondina, 2017. "The Impact of Oil Price Changes in a New Keynesian Model of the U.S. Economy," Working Papers 1709E, University of Ottawa, Department of Economics.
    6. Munechika Katayama, 2013. "Declining Effects of Oil Price Shocks," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 45(6), pages 977-1016, September.
    7. Conny Olovsson, 2019. "Oil prices in a general equilibrium model with precautionary demand for oil," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 32, pages 1-17, April.
    8. Pierdzioch, Christian & Kamps, Christophe, 2002. "Monetary Policy Rules and Oil Price Shocks," Kiel Working Papers 1090, Kiel Institute for the World Economy (IfW Kiel).
    9. Roman E. Romero, 2008. "Monetary Policy in Oil-Producing Economies," Working Papers 1053, Princeton University, Department of Economics, Center for Economic Policy Studies..
    10. Manel Hamdi & Chaker Aloui, 2015. "Forecasting Crude Oil Price Using Artificial Neural Networks: A Literature Survey," Economics Bulletin, AccessEcon, vol. 35(2), pages 1339-1359.
    11. Luõs Aguiar-Conraria & Yi Wen, 2007. "Understanding the Large Negative Impact of Oil Shocks," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(4), pages 925-944, June.
    12. Xiong, Tao & Bao, Yukun & Hu, Zhongyi, 2013. "Beyond one-step-ahead forecasting: Evaluation of alternative multi-step-ahead forecasting models for crude oil prices," Energy Economics, Elsevier, vol. 40(C), pages 405-415.
    13. Roman E. Romero, 2008. "Monetary Policy in Oil-Producing Economies," Working Papers 1053, Princeton University, Department of Economics, Center for Economic Policy Studies..
    14. Lin, Ling & Jiang, Yong & Xiao, Helu & Zhou, Zhongbao, 2020. "Crude oil price forecasting based on a novel hybrid long memory GARCH-M and wavelet analysis model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 543(C).
    15. Huynh Bao Tan, 2017. "Monetary policy and energy price shocks," The B.E. Journal of Macroeconomics, De Gruyter, vol. 17(2), pages 1-27, June.
    16. Aguiar-Conraria, Luís & Wen, Yi, 2008. "A Note On Oil Dependence And Economic Instability," Macroeconomic Dynamics, Cambridge University Press, vol. 12(5), pages 717-723, November.
    17. Lutz Kilian, 2008. "The Economic Effects of Energy Price Shocks," Journal of Economic Literature, American Economic Association, vol. 46(4), pages 871-909, December.
    18. Sugra Ingilab Humbatova & Natig Qadim-Oglu Hajiyev, 2019. "Oil Factor in Economic Development," Energies, MDPI, vol. 12(8), pages 1-40, April.
    19. Luís Francisco Aguiar-Conraria & Yi Wen, 2005. "Understanding the Impact of Oil Shocks," NIPE Working Papers 2/2005, NIPE - Universidade do Minho.
    20. Raheem, Ibrahim & Olabisi, Nafisat, 2019. "What is new? The role of asymmetry and breaks in oil price–output growth volatility nexus," MPRA Paper 105361, University Library of Munich, Germany.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:enepol:v:68:y:2014:i:c:p:371-382. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/enpol .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.