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MODELING OF WORLD CRUDE OIL PRICE BASED ON PULSE FUNCTION INTERVENTION ANALYSIS APPROACH

Netha Aliffia orcid  -  Department of Mathematics, Faculty of Science and Technology, Universitas Airlangga, Jl. Dr. Ir. H. Soekarno, Mulyorejo, Kec. Mulyorejo, Kota Surabaya, Jawa Timur 60115, Indonesia
*Sediono Sediono  -  Department of Mathematics, Faculty of Science and Technology, Universitas Airlangga, Jl. Dr. Ir. H. Soekarno, Mulyorejo, Kec. Mulyorejo, Kota Surabaya, Jawa Timur 60115, Indonesia
Suliyanto Suliyanto  -  Department of Mathematics, Faculty of Science and Technology, Universitas Airlangga, Jl. Dr. Ir. H. Soekarno, Mulyorejo, Kec. Mulyorejo, Kota Surabaya, Jawa Timur 60115, Indonesia
M. Fariz Fadillah Mardianto  -  Department of Mathematics, Faculty of Science and Technology, Universitas Airlangga, Jl. Dr. Ir. H. Soekarno, Mulyorejo, Kec. Mulyorejo, Kota Surabaya, Jawa Timur 60115, Indonesia
Dita Amelia  -  Department of Mathematics, Faculty of Science and Technology, Universitas Airlangga, Jl. Dr. Ir. H. Soekarno, Mulyorejo, Kec. Mulyorejo, Kota Surabaya, Jawa Timur 60115, Indonesia
Open Access Copyright (c) 2023 MEDIA STATISTIKA under http://creativecommons.org/licenses/by-nc-sa/4.0.

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Abstract

Crude oil has important role in global economy, including Indonesia with considerable dependence on crude oil energy consumption. The increase in crude oil prices can be triggered by several factors, one of which is geopolitical conflict that occurred due to Russia's invasion of Ukraine on February 24, 2022. As the result, world crude oil prices rose above US$100 per barrel for the first time since 2014. Therefore, this study uses pulse function intervention analysis approach to evaluate the impact of certain events in predicting data over the next few periods. The pulse function is used because the intervention occurs at the moment t only. The data used starts from June 8, 2020 to September 19, 2022 on weekly basis with the proportion of training and testing data is 90:10. The best intervention model obtained is ARIMA (3,2,0) with b=0, s=1, r=2, and intervention point at T=91. The prediction results for the next 12 periods obtained MAPE value of 2.8982% and MSE of 10.2687. This study is expected to help reduce risks due to uncertainty in world crude oil prices and in line with the goals of the Sustainable Development Goals (SDGs) to ensure access to reliable, sustainable, and affordable energy.

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Data Sets of World Crude Oil Price
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Keywords: Crude Oil Price; Intervention Analysis; Pulse Function; Russia-Ukraine Geopolitical Conflict

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  1. Ahmar, A. S. (2020). Forecast Error Calculation with Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). JINAV: Journal of Information and Visualization, 1(2), 94-96
  2. Azlan, A., Yusof, Y., & Mohsin, M. F. M. (2020). Univariate Financial Time Series Prediction using Clonal Selection Algorithm. International Journal on Advanced Science, Engineering and Information Technology, 10(1), 151-156
  3. Baek, J. & Yoon, J. H. (2022). Do Macroeconomic Activities Respond Differently to Oil Price Shocks? New Evidence from Indonesia. Economic Analysis and Policy, 76, 852-862. https://doi.org/10.1016/j.eap.2022.09.023
  4. Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2016). Time Series Analysis Forecasting and Control. Fifth Edition. New Jersey: John Wiley & Sons, Inc
  5. Central Bureau of Statistics. (2022). Exports in June 2022 reached US$26.09 Billion & Imports in June 2022 Reached US$21.00 Billion, https://www.bps.go.id/pressrelease/2022/07/15/1923/ekspor-juni-2022-mencapai-us-26-09-miliar--naik-21-30-persen-dibanding-mei-2022---impor-juni-2022-senilai-us-21-00-miliar--naik-12-87-persen-dibanding-mei-2022.html
  6. Cody, R. (2018). An Introduction to SAS University Edition. North Carolina: SAS Institute
  7. Cryer, J. D. & Chan, K. S. (2008). Time Series Analysis: with applications in R. New York: Springer
  8. Finley, M., & Krane, J. (2022). Reroute, Reduce, or Replace? How The Oil Market Might Cope with a Loss of Russian Exports After the Invasion of Ukraine. Baker Institute for Public Policy, Rice University. https://doi.org/10.25613/5DX9-P121
  9. Haque, M. I., & Shaik, A. R. (2021). Predicting Crude Oil Prices During a Pandemic: A Comparison of ARIMA and GARCHModels. Montenegrin Journal of Economics, 17(1), 197-207. DOI: 10.14254/1800-5845/2021.17-1.15
  10. He, X. J. (2018). Crude Oil Prices Forecasting: Time Series vs. SVR Models. Journal of International Technology and Information Management, 27(2), 25-42
  11. Joo, K., Suh, J. H., Lee, D., & Ahn, K. (2020). Impact of the Global Financial Crisis on the Crude Oil Market, Energy Strategy Reviews, 30, 100516, https://doi.org/10.1016/j.esr.2020.100516
  12. Liadze, I., Macchiarelli, C., Mortimer-Lee, P., & Juanino, P. S. (2022). The Economic Costs of the Russia-Ukraine Conflict. London: National Institute of Economic and Social Research
  13. Liang, C., Wei, Y., Li, X., Zhang, X., & Xhang, Y. (2020). Uncertainty and Crude Oil Market Volatility: New Evidence. Applied Economics, 52(27), 2945–2959, DOI: 10.1080/00036846.2019.1696943
  14. Mardianto, M. F. F., Ariyanto, R. A., Andriawan, R., & Husada, D. A. (2021). Contribution Analysis of “Suroboyo Bus” in Waste Management Based on Two Form of Complete Fourier Series Estimator. Jurnal Matematika MANTIK, 7(1), 86-95. https://doi.org/10.15642/mantik.2021.7.1.86-95
  15. Mehta, W. A., Sukmawaty, Y., & Khairullah. (2021). Rainfall Prediction Climatological Station of Banjarbaru using ARIMA Kalman Filter. Journal of Physics: Conference Series, 2106(1), 012003. IOP Publishing
  16. Min, J. C., Lim, C., & Kung, H. H. (2011). Intervention Analysis of SARS on Japanese Tourism Demand for Taiwan. Quality & Quantity, 45(1), 91-102. DOI: 10.1007/s11135-010-9338-4
  17. Ministry of Energy and Mineral Resources Republic of Indonesia. (2022). February ICP Set at USD95.72 per Barrel. https://www.esdm.go.id/en/media-center/news-archives/february-icp-set-at-usd9572-per-barrel
  18. Murari, A., Peluso, E., Cianfrani, F., Gaudio, P., & Lungaroni, M. (2019). On the Use of Entropy to Improve Model Selection Criteria. Entropy, 21(4), 394. https://doi.org/10.3390/e21040394
  19. Namazian, A., Ghodsi, M., & Nawaser, K. (2018). Prediction of Temperature Variations using Artificial Neural Networks and ARIMA Model. International Journal of Industrial and Systems Engineering, 30(1), 60-77
  20. Purwa, T., Nafngiyana, U., & Suhartono. (2020). Comparison of ARIMA, Transfer Function and VAR Models for Forecasting CPI, Stock Prices, and Indonesian Exchange Rate: Accuracy Vs. Explainability. Media Statistika, 13(1), 1-12. DOI: 10.14710/medstat.13.1.1-12
  21. Trimono, T., Sonhaji, A., & Mukhaiyar, U. (2020). Forecasting Farmer Exchange Rate in Central Java Province Using Vector Integrated Moving Average. Media Statistika, 13(2), 182-193. DOI: 10.14710/medstat.13.2.182-193
  22. Wang, K. H., Su, C. W., & Umar, M. (2021). Geopolitical Risk and Crude Oil Security: A Chinese Perspective. Energy, 219, 119555
  23. Wei, W.W.S. (2006). Time Series Analysis Univariate and Multivariate Methods. 2nd Edition. New York: Addison Wesley
  24. Wiri, L., & Tuaneh, G. L. (2019). Modelling the Nigeria Crude Oil Prices using ARIMA, Pre-intervention and Post-intervention Model. Asian Journal of Probability and Statistics, 3(1), 1-12. DOI: 10.9734/AJPAS/2019/v3i130083
  25. Yahoo Finance. (2022). Crude Oil Historical Data. https://finance.yahoo.com/quote/CL%3DF/history?period1=1593561600&period2=1664409600&interval=1wk&filter=history&frequency=1wk&includeAdjustedClose=true. Do Macroeconomic Activities Respond Differently to Oil Price Shocks? New Evidence from Indonesia. Economic Analysis and Policy

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