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Forecasting Oil Price Volatility in the Era of Big Data: A Text Mining for VaR Approach

Author

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  • Lu-Tao Zhao

    (School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China
    Center for Energy and Environmental Policy Research & School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China)

  • Li-Na Liu

    (School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China)

  • Zi-Jie Wang

    (School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China)

  • Ling-Yun He

    (Center for Energy and Environmental Policy Research & School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China
    School of Economics, JiNan University, Guangzhou 510632, China
    School of Economics and Management, Nanjing University of Information Science and Technology, Nanjing 210044, China)

Abstract

The rapid fluctuations in global crude oil prices are one of the important factors affecting both the sustainable development and the green transformation of the global economy. To accurately measure the risks of crude oil prices, in the context of big data, this study introduces the two-layer non-negative matrix factorization model, a kind of natural language processing, to extract the dynamic risk factors from online news and assign them as weighted factors to historical data. Finally, this study proposes a giant information history simulation (GIHS) method which is used to forecast the value-at-risk (VaR) of crude oil. In conclusion, this paper shows that considering the impact of dynamic risk factors from online news on the VaR can improve the accuracy of crude oil VaR measurement, providing an effective tool for analyzing crude oil price risks in oil market, providing risk management support for international oil market investors, and providing the country with a sense of risk analysis to achieve sustainable and green transformation.

Suggested Citation

  • Lu-Tao Zhao & Li-Na Liu & Zi-Jie Wang & Ling-Yun He, 2019. "Forecasting Oil Price Volatility in the Era of Big Data: A Text Mining for VaR Approach," Sustainability, MDPI, vol. 11(14), pages 1-20, July.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:14:p:3892-:d:249220
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    Cited by:

    1. 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.
    2. Matteo Bonato & Konstantinos Gkillas & Rangan Gupta & Christian Pierdzioch, 2020. "Investor Happiness and Predictability of the Realized Volatility of Oil Price," Sustainability, MDPI, vol. 12(10), pages 1-11, May.
    3. Casandra Okogwu & Mercy Odochi Agho & Mojisola Abimbola Adeyinka & Bukola A. Odulaja & Obinna Arize Ufoaro & Sodrudeen Abolore Ayodeji & Chibuike Daraojimba, 2023. "Adapting To Oil Price Volatility: A Strategic Review Of Supply Chain Responses Over Two Decades," International Journal of Research and Scientific Innovation, International Journal of Research and Scientific Innovation (IJRSI), vol. 10(10), pages 68-87, October.
    4. Marcus Vinicius Santos & Fernando Morgado-Dias & Thiago C. Silva, 2023. "Oil Sector and Sentiment Analysis—A Review," Energies, MDPI, vol. 16(12), pages 1-29, June.
    5. Jiangwei Liu & Xiaohong Huang, 2021. "Forecasting Crude Oil Price Using Event Extraction," Papers 2111.09111, arXiv.org.
    6. Pruethsan Sutthichaimethee & Sthianrapab Naluang, 2019. "The Efficiency of the Sustainable Development Policy for Energy Consumption under Environmental Law in Thailand: Adapting the SEM-VARIMAX Model," Energies, MDPI, vol. 12(16), pages 1-21, August.
    7. Shahriyar Mukhtarov & Sugra Humbatova & Mubariz Mammadli & Natig Gadim‒Oglu Hajiyev, 2021. "The Impact of Oil Price Shocks on National Income: Evidence from Azerbaijan," Energies, MDPI, vol. 14(6), pages 1-11, March.
    8. Hao, Jun & Feng, Qianqian & Yuan, Jiaxin & Sun, Xiaolei & Li, Jianping, 2022. "A dynamic ensemble learning with multi-objective optimization for oil prices prediction," Resources Policy, Elsevier, vol. 79(C).
    9. James Ming Chen & Mobeen Ur Rehman, 2021. "A Pattern New in Every Moment: The Temporal Clustering of Markets for Crude Oil, Refined Fuels, and Other Commodities," Energies, MDPI, vol. 14(19), pages 1-58, September.

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