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An Auxiliary Index for Reducing Brent Crude Investment Risk—Evaluating the Price Relationships between Brent Crude and Commodities

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  • Yu-Wei Chen

    (Department of Information and Finance Management, National Taipei University of Technology, Taipei 10608, Taiwan)

  • Chui-Yu Chiu

    (Department of Industrial Engineering and Management, National Taipei University of Technology, Taipei 10608, Taiwan)

  • Mu-Chun Hsiao

    (Graduate Institute of Information and Finance Management, National Taipei University of Technology, Taipei 10608, Taiwan)

Abstract

Examining the price relationships of Brent Crude with 78 global commodities, our study shows that the spot price of a certain commodity, New York Harbor No. 2 Heating Oil Spot Price FOB, can serve as an auxiliary forecasting index of the rise and fall of the monthly Brent Crude oil price. With an innovative view for evaluating the price relationship and prediction based on simple, practical measurement, our findings provide a helpful auxiliary index tool for investors and analysts by offering a high success rate (82.98%) and predicting the rise and fall of the monthly Brent Crude oil price three weeks in advance.

Suggested Citation

  • Yu-Wei Chen & Chui-Yu Chiu & Mu-Chun Hsiao, 2021. "An Auxiliary Index for Reducing Brent Crude Investment Risk—Evaluating the Price Relationships between Brent Crude and Commodities," Sustainability, MDPI, vol. 13(9), pages 1-45, April.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:9:p:5050-:d:547153
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    1. Anthony E. Bopp & Scott Sitzer, 1987. "Are petroleum futures prices good predictors of cash value?," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 7(6), pages 705-719, December.
    2. Chang, Chun-Ping & Lee, Chien-Chiang, 2015. "Do oil spot and futures prices move together?," Energy Economics, Elsevier, vol. 50(C), pages 379-390.
    3. Monika Roman & Aleksandra Górecka & Joanna Domagała, 2020. "The Linkages between Crude Oil and Food Prices," Energies, MDPI, vol. 13(24), pages 1-18, December.
    4. Zavaleta, Armando & Walls, W.D. & Rusco, Frank W., 2015. "Refining for export and the convergence of petroleum product prices," Energy Economics, Elsevier, vol. 47(C), pages 206-214.
    5. Xuluo Yin & Jiangang Peng & Tian Tang, 2018. "Improving the Forecasting Accuracy of Crude Oil Prices," Sustainability, MDPI, vol. 10(2), pages 1-9, February.
    6. Lu-Tao Zhao & Shun-Gang Wang & Zhi-Gang Zhang, 2020. "Oil Price Forecasting Using a Time-Varying Approach," Energies, MDPI, vol. 13(6), pages 1-16, March.
    7. Alvarez-Ramirez, Jose & Alvarez, Jesus & Rodriguez, Eduardo, 2008. "Short-term predictability of crude oil markets: A detrended fluctuation analysis approach," Energy Economics, Elsevier, vol. 30(5), pages 2645-2656, September.
    8. Saeed Moshiri & Faezeh Foroutan, 2006. "Forecasting Nonlinear Crude Oil Futures Prices," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 81-96.
    9. Athanasia Dimitriadou & Periklis Gogas & Theophilos Papadimitriou & Vasilios Plakandaras, 2018. "Oil Market Efficiency under a Machine Learning Perspective," Forecasting, MDPI, vol. 1(1), pages 1-12, October.
    10. Duc Hong Vo & Tan Ngoc Vu & Anh The Vo & Michael McAleer, 2019. "Modeling the Relationship between Crude Oil and Agricultural Commodity Prices," Energies, MDPI, vol. 12(7), pages 1-41, April.
    11. Cen, Zhongpei & Wang, Jun, 2019. "Crude oil price prediction model with long short term memory deep learning based on prior knowledge data transfer," Energy, Elsevier, vol. 169(C), pages 160-171.
    12. Li, Jinchao & Zhu, Shaowen & Wu, Qianqian, 2019. "Monthly crude oil spot price forecasting using variational mode decomposition," Energy Economics, Elsevier, vol. 83(C), pages 240-253.
    13. 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.
    14. Guhathakurta, Kousik & Dash, Saumya Ranjan & Maitra, Debasish, 2020. "Period specific volatility spillover based connectedness between oil and other commodity prices and their portfolio implications," Energy Economics, Elsevier, vol. 85(C).
    15. Asche, Frank & Gjolberg, Ole & Volker, Teresa, 2003. "Price relationships in the petroleum market: an analysis of crude oil and refined product prices," Energy Economics, Elsevier, vol. 25(3), pages 289-301, May.
    16. Tao Yin & Yiming Wang, 2019. "Predicting the Price of WTI Crude Oil Using ANN and Chaos," Sustainability, MDPI, vol. 11(21), pages 1-14, October.
    17. Arfaoui, Mongi, 2018. "On the spot-futures relationship in crude-refined petroleum prices: New evidence from an ARDL bounds testing approach," Journal of Commodity Markets, Elsevier, vol. 11(C), pages 48-58.
    18. Lanza, Alessandro & Manera, Matteo & Giovannini, Massimo, 2005. "Modeling and forecasting cointegrated relationships among heavy oil and product prices," Energy Economics, Elsevier, vol. 27(6), pages 831-848, November.
    19. Taghizadeh-Hesary, Farhad & Rasoulinezhad, Ehsan & Yoshino, Naoyuki, 2019. "Energy and Food Security: Linkages through Price Volatility," Energy Policy, Elsevier, vol. 128(C), pages 796-806.
    20. Morana, Claudio, 2001. "A semiparametric approach to short-term oil price forecasting," Energy Economics, Elsevier, vol. 23(3), pages 325-338, May.
    21. Kaijian He & Rui Zha & Jun Wu & Kin Keung Lai, 2016. "Multivariate EMD-Based Modeling and Forecasting of Crude Oil Price," Sustainability, MDPI, vol. 8(4), pages 1-11, April.
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    2. Maneejuk, Paravee & Kaewtathip, Nuttaphong & Jaipong, Peemmawat & Yamaka, Woraphon, 2022. "The transition of the global financial markets' connectedness during the COVID-19 pandemic," The North American Journal of Economics and Finance, Elsevier, vol. 63(C).

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