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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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    References listed on IDEAS

    as
    1. Ji, Qiang & Guo, Jian-Feng, 2015. "Oil price volatility and oil-related events: An Internet concern study perspective," Applied Energy, Elsevier, vol. 137(C), pages 256-264.
    2. Yao, Ting & Zhang, Yue-Jun & Ma, Chao-Qun, 2017. "How does investor attention affect international crude oil prices?," Applied Energy, Elsevier, vol. 205(C), pages 336-344.
    3. Jochen KRAUSE & Marc S. PAOLELLA, 2014. "A Fast, Accurate Method for Value at Risk and Expected Shortfall," Swiss Finance Institute Research Paper Series 14-40, Swiss Finance Institute.
    4. Dionne, Georges & Duchesne, Pierre & Pacurar, Maria, 2009. "Intraday Value at Risk (IVaR) using tick-by-tick data with application to the Toronto Stock Exchange," Journal of Empirical Finance, Elsevier, vol. 16(5), pages 777-792, December.
    5. Zhang, Yue-Jun & Zhang, Lu, 2015. "Interpreting the crude oil price movements: Evidence from the Markov regime switching model," Applied Energy, Elsevier, vol. 143(C), pages 96-109.
    6. Gencay, Ramazan & Selcuk, Faruk, 2004. "Extreme value theory and Value-at-Risk: Relative performance in emerging markets," International Journal of Forecasting, Elsevier, vol. 20(2), pages 287-303.
    7. Derek Bunn, Julien Chevallier, Yannick Le Pen, and Benoit Sevi, 2017. "Fundamental and Financial Influences on the Co-movement of Oil and Gas Prices," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2).
    8. Fong Chan, Kam & Gray, Philip, 2006. "Using extreme value theory to measure value-at-risk for daily electricity spot prices," International Journal of Forecasting, Elsevier, vol. 22(2), pages 283-300.
    9. Meng, Xiaochun & Taylor, James W., 2018. "An approximate long-memory range-based approach for value at risk estimation," International Journal of Forecasting, Elsevier, vol. 34(3), pages 377-388.
    10. Farshid Jamshidian & Yu Zhu, 1996. "Scenario Simulation: Theory and methodology (*)," Finance and Stochastics, Springer, vol. 1(1), pages 43-67.
    11. J. Baixauli & Susana Alvarez, 2012. "Implied Severity Density Estimation: An Extended Semiparametric Method to Compute Credit Value at Risk," Computational Economics, Springer;Society for Computational Economics, vol. 40(2), pages 115-129, August.
    12. Darryll Hendricks, 1996. "Evaluation of value-at-risk models using historical data," Proceedings 512, Federal Reserve Bank of Chicago.
    13. Jeremy Berkowitz & Peter Christoffersen & Denis Pelletier, 2011. "Evaluating Value-at-Risk Models with Desk-Level Data," Management Science, INFORMS, vol. 57(12), pages 2213-2227, December.
    14. Robert F. Engle & Simone Manganelli, 2004. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 367-381, October.
    15. Yu-Ying Tzeng & Paul M. Beaumont & Giray Ökten, 2018. "Time Series Simulation with Randomized Quasi-Monte Carlo Methods: An Application to Value at Risk and Expected Shortfall," Computational Economics, Springer;Society for Computational Economics, vol. 52(1), pages 55-77, June.
    16. Aboura, Sofiane & Chevallier, Julien, 2016. "Spikes and crashes in the oil market," Research in International Business and Finance, Elsevier, vol. 36(C), pages 615-623.
    17. Sadeghi, Mehdi & Shavvalpour, Saeed, 2006. "Energy risk management and value at risk modeling," Energy Policy, Elsevier, vol. 34(18), pages 3367-3373, December.
    18. Hung, Jui-Cheng & Lee, Ming-Chih & Liu, Hung-Chun, 2008. "Estimation of value-at-risk for energy commodities via fat-tailed GARCH models," Energy Economics, Elsevier, vol. 30(3), pages 1173-1191, May.
    19. Junhong Du & Zhiming Li & Lijun Wu, 2019. "Optimal Stop-Loss Reinsurance Under the VaR and CTE Risk Measures: Variable Transformation Method," Computational Economics, Springer;Society for Computational Economics, vol. 53(3), pages 1133-1151, March.
    20. Giovanni Barone‐Adesi & Kostas Giannopoulos & Les Vosper, 1999. "VaR without correlations for portfolios of derivative securities," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 19(5), pages 583-602, August.
    21. Cathy W.S. Chen & Richard Gerlach & Edward M. H. Lin & W. C. W. Lee, 2012. "Bayesian Forecasting for Financial Risk Management, Pre and Post the Global Financial Crisis," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 31(8), pages 661-687, December.
    22. McLaren, Nick & Shanbhogue, Rachana, 2011. "Using internet search data as economic indicators," Bank of England Quarterly Bulletin, Bank of England, vol. 51(2), pages 134-140.
    23. Zhi-Fu Mi & Yi-Ming Wei & Bao-Jun Tang & Rong-Gang Cong & Hao Yu & Hong Cao & Dabo Guan, 2017. "Risk assessment of oil price from static and dynamic modelling approaches," Applied Economics, Taylor & Francis Journals, vol. 49(9), pages 929-939, February.
    24. Fuertes, Ana-Maria & Olmo, Jose, 2013. "Optimally harnessing inter-day and intra-day information for daily value-at-risk prediction," International Journal of Forecasting, Elsevier, vol. 29(1), pages 28-42.
    25. Youssef, Manel & Belkacem, Lotfi & Mokni, Khaled, 2015. "Value-at-Risk estimation of energy commodities: A long-memory GARCH–EVT approach," Energy Economics, Elsevier, vol. 51(C), pages 99-110.
    26. Nieto, Maria Rosa & Ruiz, Esther, 2016. "Frontiers in VaR forecasting and backtesting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 475-501.
    27. Vosen, Simeon & Schmidt, Torsten, 2012. "A monthly consumption indicator for Germany based on Internet search query data," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 19(7), pages 683-687.
    28. Zhang, Xiao-Bing & Qin, Ping & Chen, Xiaolan, 2017. "Strategic oil stockpiling for energy security: The case of China and India," Energy Economics, Elsevier, vol. 61(C), pages 253-260.
    29. Zhang, Yue-Jun & Yao, Ting, 2016. "Interpreting the movement of oil prices: Driven by fundamentals or bubbles?," Economic Modelling, Elsevier, vol. 55(C), pages 226-240.
    30. Paul H. Kupiec, 1995. "Techniques for verifying the accuracy of risk measurement models," Finance and Economics Discussion Series 95-24, Board of Governors of the Federal Reserve System (U.S.).
    31. Fries, Christian P. & Nigbur, Tobias & Seeger, Norman, 2017. "Displaced relative changes in historical simulation: Application to risk measures of interest rates with phases of negative rates," Journal of Empirical Finance, Elsevier, vol. 42(C), pages 175-198.
    32. 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.
    33. Jochen Krause & Marc S. Paolella, 2014. "A Fast, Accurate Method for Value-at-Risk and Expected Shortfall," Econometrics, MDPI, vol. 2(2), pages 1-25, June.
    34. J. Baixauli-Soler & Eva Alfaro-Cid & Matilde Fernandez-Blanco, 2011. "Mean-VaR Portfolio Selection Under Real Constraints," Computational Economics, Springer;Society for Computational Economics, vol. 37(2), pages 113-131, February.
    35. David Cabedo, J. & Moya, Ismael, 2003. "Estimating oil price 'Value at Risk' using the historical simulation approach," Energy Economics, Elsevier, vol. 25(3), pages 239-253, May.
    36. Darryll Hendricks, 1996. "Evaluation of value-at-risk models using historical data," Economic Policy Review, Federal Reserve Bank of New York, vol. 2(Apr), pages 39-69.
    37. Ardia, David & Bluteau, Keven & Boudt, Kris & Catania, Leopoldo, 2018. "Forecasting risk with Markov-switching GARCH models:A large-scale performance study," International Journal of Forecasting, Elsevier, vol. 34(4), pages 733-747.
    38. Yue-Jun Zhang & Yao-Bin Wu, 2018. "The dynamic information spill-over effect of WTI crude oil prices on China’s traditional energy sectors," China Agricultural Economic Review, Emerald Group Publishing Limited, vol. 10(3), pages 516-534, July.
    39. Zhang, Yue-Jun & Wang, Jin-Li, 2019. "Do high-frequency stock market data help forecast crude oil prices? Evidence from the MIDAS models," Energy Economics, Elsevier, vol. 78(C), pages 192-201.
    40. Giannopoulos, Kostas & Tunaru, Radu, 2005. "Coherent risk measures under filtered historical simulation," Journal of Banking & Finance, Elsevier, vol. 29(4), pages 979-996, April.
    41. Karmakar, Madhusudan & Paul, Samit, 2019. "Intraday portfolio risk management using VaR and CVaR:A CGARCH-EVT-Copula approach," International Journal of Forecasting, Elsevier, vol. 35(2), pages 699-709.
    42. Panigirtzoglou, Nikolaos & Skiadopoulos, George, 2004. "A new approach to modeling the dynamics of implied distributions: Theory and evidence from the S&P 500 options," Journal of Banking & Finance, Elsevier, vol. 28(7), pages 1499-1520, July.
    43. Wang, Jue & Athanasopoulos, George & Hyndman, Rob J. & Wang, Shouyang, 2018. "Crude oil price forecasting based on internet concern using an extreme learning machine," International Journal of Forecasting, Elsevier, vol. 34(4), pages 665-677.
    44. Kaiser, Mark J. & Yu, Yunke, 2010. "The impact of Hurricanes Gustav and Ike on offshore oil and gas production in the Gulf of Mexico," Applied Energy, Elsevier, vol. 87(1), pages 284-297, January.
    45. Shangkun Deng & Akito Sakurai, 2014. "Crude Oil Spot Price Forecasting Based on Multiple Crude Oil Markets and Timeframes," Energies, MDPI, vol. 7(5), pages 1-19, April.
    46. Somboon Chuaykoblap & Parames Chutima & Achara Chandrachai & Natawut Nupairoj, 2017. "Expert-based text mining with Delphi method for crude oil price prediction," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 25(4), pages 545-563.
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    1. 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.
    2. 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.
    3. Jiangwei Liu & Xiaohong Huang, 2021. "Forecasting Crude Oil Price Using Event Extraction," Papers 2111.09111, arXiv.org.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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).

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