Designing a Oil Market Model and Comparing Crude Oil Price Forecasts

Salek, Navid; Khorsandi, Morteza

Volume 13, Issue 47 (5-2022) jemr 2022, 13(47): 73-114 | Back to browse issues page

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Salek N, Khorsandi M. Designing a Oil Market Model and Comparing Crude Oil Price Forecasts. jemr 2022; 13 (47) :73-114
URL: http://jemr.khu.ac.ir/article-1-2282-en.html

Designing a Oil Market Model and Comparing Crude Oil Price Forecasts

Navid Salek ^*

¹, Morteza Khorsandi²

1- AllamEh Tabatabaei University , saleknavid@yahoo.com
2- AllamEh Tabatabaei University

Abstract: (3311 Views)

The price of crude oil is one of the factors affecting economic indicators. Therefore, the prediction of oil prices and the accuracy of the applied methods have always been discussed by economists. In this study, the effect of all effective variables on the supply and demand of crude oil based on McAvoy's competitive theory is investigated, and the supply and demand are estimated using the system of simultaneous equations and conventional statistical methods. Then, using algebraic operations and the assumption of equality of oil supply and demand in the long term, the long-term potential of oil supply and demand is extracted with respect to each of the variables in the model. Based on the results, the world's gross domestic product (GDP) has the greatest impact on oil prices with a demand potential of 0.6039, and the world's military and security tensions have the least impact with a demand potential of –0.0110. After estimating the model, the prediction accuracy of three combined mothod is compared with conventional and single-variable methods of neural network and ARIMA. These three combined methods are: (a) neural network and system of simultaneous equations, (b) ARIMA and system of simultaneous equations, (c) neural network and ARIMA and system of simultaneous equations. The results showed that the combined method of ARIMA and simultaneous equation system provides better reslts for 5-year forecasts while the combined method of neural network and ARIMA and simultaneous equation system shows better results for 10-year forecasts.

Keywords: Crude Oil Price, Price Prediction, Neural Network, ARIMA, System of Simultaneous Equations

Full-Text [PDF 809 kb] (1379 Downloads)

Type of Study: Applicable | Subject: انرژی، منابع و محیط زیست
Received: 2022/11/23 | Accepted: 2023/04/4 | Published: 2023/05/13

References

1. Abasi Nami H (2021). Forecasting Crude Oil Prices Volatility and Value at Risk: Single and Switching Regime GARCH Models, Quarterly Energy Economics Review, 17 (68): 141-174 (In Persian)

2. Abrishami, H. Behradmehr, N. & seifi, T (2013). Forecasting of Crude Oil Price by Using Wavelet Transform, Non-Linear and Linear Models, Quarterly Journal of Applied Economics Studiesin Iran, 2(7): 41-62 (In Persian)

3. Abunoori, AA & khodadad, N (2012). Comparing the performance of ARIMA regression models and neural network with genetic algorithm (GMDH) in predicting the price of crude oil in Iran, Journal of Financial Engineering and Securities Management, 3(11): 43-62 (In Persian)

4. Ayazi, A. Amiri, M. Fartukzadeh, HR & Azar, A (2020). Strategic analysis of international oil market suppliers based on graph model, Scientific quarterly of interdisciplinary studies on strategic knowledge, 10(39): 179-206 (In Persian)

5. Baumeister, Christiane. & Lutz, Kilian. (2016). Forty Years of Oil Price Fluctuations: Why the Price of Oil May Still Surprise Us. Translated by Mehrdad Rahmani and Ali Faridzad (2019). Trend Quarterly, 25 (83,84):131-168 (In Persian) [DOI:10.2139/ssrn.2734052]

6. Bordbar N, Heidari E (2017). The Effect of World Oil Price Fluctuations on the Return of the Energy Intensive Industries Stock in Iran, Journal of Economic Modeling Research, 8 (27):177-205 (In Persian) [DOI:10.29252/jemr.7.27.177]

7. Deng, C. Ma, L & Zeng, T (2021). Crude Oil Price Forecast Based on Deep Transfer Learning: Shanghai Crude Oil as an Example, MDPI, 13(24): 1-13. [DOI:10.3390/su132413770]

8. Ebrahimi, M. Hajimirzayi, MA. & Mohammadkhani, S (2011). Estimating Iran's crude oil supply pattern, Quarterly Energy Economics Review, 8(29): 113-137 (In Persian)

9. Emami meibodi A, memarzadeh A, amadeh H, ghasemi nejad A (2013). Comparing the performance of GARCH model and gravitational search algorithm (GSA) in modeling and forecasting of spot oil price of Iran, Journal of Economic Modeling Research, 4 (14):1-23 (In Persian)

10. Gojarati, D (1991). Basics of econometrics, Translated by Abrishami H (2008), Tehran: Tehran University Publications, The second volume

11. Gupta, N & Nigam, Sh (2020). Crude Oil Price Prediction using Artificial Neural Network, Procedia Computer Science, 170: 642-647 [DOI:10.1016/j.procs.2020.03.136]

12. Hajikaram, E & darabi, R (2017). Brent Crude Oil Daily Price Forecast by Combining Principal Components Analysis and Support Vector Regression methods, Iranian Energy Economics Research; 7(25):41-60 (In Persian)

13. HajiLari Semnani, B & Khalili, S (2018). Estimation of OPEC crude oil price using binomial tree, time series and artificial neural networks, Journal of Mineral Resources Engineering,3(3): 31-41 (In Persian)

14. Jammazi, R. & Aloui, C (2012). Crude Oil Price Forecasting: Experimental Evidence from Wavelet Decomposition and Neural Network Modeling, Energy Economics, 34(3):828-841 [DOI:10.1016/j.eneco.2011.07.018]

15. Li, X. He, K. Lai, K & Zou Y (2014). Forecasting Crude Oil Price With Multiscale Denoising Ensemble Model, Mathematic Problems in Engineering, (4): 1-9. [DOI:10.1155/2014/716571]

16. Menhaj, MB (1998). Basics of neural networks (computational intelligence), Tehran: Publication of Dr Hesabi (In Persian)

17. Mohammadi, H. & Lixian, Su (2010). International Evidence on Crude Oil Price Dynamics: Applications of ARIMA-GARCH Models, Energy Economics, 32(5): 1001-1008. [DOI:10.1016/j.eneco.2010.04.009]

18. Runfang, Y., Jiangze, D., Xiaoto, L. (2019). Improved Forecast Ability of Oil Market Volatility Based on combined Markov Switching and GARCHclass Model, Procedia Computer Science, 122: 415-422. [DOI:10.1016/j.procs.2017.11.388]

19. Souri, A (2017). Econometrics (Volume 1), Tehran: Cultural publication, Sixth edition (In Persian)

20. Sadeghi, H. Zolfaghari, M & Elhaminezhd, M (2011). Comparison of Neural Networks and ARIMA in Modeling and Forecasting of Short Run Pricing of the OPEC Crude Oil Basket (With Focus on Comparative Expectations), Quarterly Energy Economics Review, 8(28): 25-47 (In Persian)

21. Takroosta A, Mohajeri P, Mohammadi T, Shakeri A, Ghasemi A (2019). An Analysis of Oil Prices Considering the Political Risk of OPEC, Journal of Economic Modeling Research; 10 (37):105-138 (In Persian) [DOI:10.29252/jemr.10.37.105]

22. Wang, M. Tian, L. & Zhou, P (2018). A novel approach for oil price forecasting based on data fluctuation network, Energy Economic, 71:201-212 [DOI:10.1016/j.eneco.2018.02.021]

23. Yadegari H, Mohammadi T, Amadeh H, Qasemi A, Mostafaei H (2022). Brent crude oil Price Forecast with Hybrid Model of Nonlinear Grey Model and Linear Arima Waste Correction, Quarterly Energy Economics Review, 18(72): 1-25 (In Persian)

24. Yu Lean, Dai Wei, Tang Ling (2016). A novel decomposition ensemble model with extended extreme learning machine for crude oil price forecasting. Engineering Applications of Artificial Intelligence, , 47: 110-121 [DOI:10.1016/j.engappai.2015.04.016]

25. Zhang, Y., Yao, T., He, L., Ripple, R. (2019). Volatility forecasting of crude oil market: Can the regime switching GARCH model beat the single-regime GARCH models, International Review of Economics & Finance, 59: 302- 317. [DOI:10.1016/j.iref.2018.09.006]

26. Zhang, K & Hong, M (2022). Forecasting crude oil price using LSTM neural networks, Data Science in Finance and Economics, 2(3):163-180. [DOI:10.3934/DSFE.2022008]

27. Zou, Yingchao & Chen, Yanhui (2016). Multi-step-ahead Crude Oil Price Forecasting based on Grey Wave Forecasting Method, Procedia Computer Science, 91:1050 - 1056. [DOI:10.1016/j.procs.2016.07.147]