IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i14p3892-d249220.html
   My bibliography  Save this article

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

Author

Listed:
  • 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, Open Access Journal, vol. 11(14), pages 1-20, July.
  • Handle: RePEc:gam:jsusta:v:11:y:2019:i:14:p:3892-:d:249220
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/14/3892/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/11/14/3892/
    Download Restriction: no

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.).
    5. 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.
    6. 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.
    7. 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.
    8. 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).
    9. 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.
    10. 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.
    11. Farshid Jamshidian & Yu Zhu, 1996. "Scenario Simulation: Theory and methodology (*)," Finance and Stochastics, Springer, vol. 1(1), pages 43-67.
    12. Darryll Hendricks, 1996. "Evaluation of value-at-risk models using historical data," Proceedings 512, Federal Reserve Bank of Chicago.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. Jochen Krause & Marc S. Paolella, 2014. "A Fast, Accurate Method for Value-at-Risk and Expected Shortfall," Econometrics, MDPI, Open Access Journal, vol. 2(2), pages 1-25, June.
    20. 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.
    21. 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.
    22. Sadeghi, Mehdi & Shavvalpour, Saeed, 2006. "Energy risk management and value at risk modeling," Energy Policy, Elsevier, vol. 34(18), pages 3367-3373, December.
    23. 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.
    24. 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.
    25. 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.
    26. 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.
    27. 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.
    28. 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.
    29. 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.
    30. Giannopoulos, Kostas & Tunaru, Radu, 2005. "Coherent risk measures under filtered historical simulation," Journal of Banking & Finance, Elsevier, vol. 29(4), pages 979-996, April.
    31. 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.
    32. Nieto, Maria Rosa & Ruiz, Esther, 2016. "Frontiers in VaR forecasting and backtesting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 475-501.
    33. Vosen, Simeon & Schmidt, Torsten, 2012. "A monthly consumption indicator for Germany based on Internet search query data," EconStor Open Access Articles, ZBW - Leibniz Information Centre for Economics, pages 683-687.
    34. 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.
    35. 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.
    36. 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.
    37. 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.
    38. 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.
    39. Shangkun Deng & Akito Sakurai, 2014. "Crude Oil Spot Price Forecasting Based on Multiple Crude Oil Markets and Timeframes," Energies, MDPI, Open Access Journal, vol. 7(5), pages 1-19, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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, Open Access Journal, vol. 12(16), pages 1-21, August.

    More about this item

    Keywords

    oil price volatility; risk identification; VaR; big data; natural language processing; two-layer non-negative matrix factorization;

    JEL classification:

    • Q - Agricultural and Natural Resource Economics; Environmental and Ecological Economics
    • Q0 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General
    • Q2 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation
    • Q3 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Nonrenewable Resources and Conservation
    • Q5 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics
    • Q56 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environment and Development; Environment and Trade; Sustainability; Environmental Accounts and Accounting; Environmental Equity; Population Growth
    • O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:11:y:2019:i:14:p:3892-:d:249220. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (XML Conversion Team). General contact details of provider: https://www.mdpi.com/ .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.