IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0268996.html
   My bibliography  Save this article

An analysis of crude oil prices in the last decade (2011-2020): With deep learning approach

Author

Listed:
  • Abhibasu Sen
  • Karabi Dutta Choudhury
  • Tapan Kumar Datta

Abstract

Crude Oil is one of the most important commodities in this world. We have studied the effects of Crude Oil inventories on crude oil prices over the last ten years (2011 to 2020). We tried to figure out how the Crude Oil price variance responds to inventory announcements. We then introduced several other financial instruments to study the relation of these instruments with Crude Oil variation. To undertake this task, we took the help of several mathematical tools including machine learning tools such as Long Short Term Memory(LSTM) methods, etc. The previous researches in this area primarily focussed on statistical methods such as GARCH (1,1) etc. (Bu (2014)). Various researches on the price of crude oil have been undertaken with the help of LSTM. But the variation of crude oil price has not yet been studied. In this research, we studied the variance of crude oil prices with the help of LSTM. This research will be beneficial for the options traders who would like to get benefit from the variance of the underlying instrument.

Suggested Citation

  • Abhibasu Sen & Karabi Dutta Choudhury & Tapan Kumar Datta, 2023. "An analysis of crude oil prices in the last decade (2011-2020): With deep learning approach," PLOS ONE, Public Library of Science, vol. 18(3), pages 1-32, March.
    • Handle: RePEc:plo:pone00:0268996
    DOI: 10.1371/journal.pone.0268996
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0268996
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0268996&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0268996?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Urolagin, Siddhaling & Sharma, Nikhil & Datta, Tapan Kumar, 2021. "A combined architecture of multivariate LSTM with Mahalanobis and Z-Score transformations for oil price forecasting," Energy, Elsevier, vol. 231(C).
    2. Cao, Jian & Li, Zhi & Li, Jian, 2019. "Financial time series forecasting model based on CEEMDAN and LSTM," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 519(C), pages 127-139.
    3. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Clara Vega, 2003. "Micro Effects of Macro Announcements: Real-Time Price Discovery in Foreign Exchange," American Economic Review, American Economic Association, vol. 93(1), pages 38-62, March.
    4. Wang, Yu Shan & Chueh, Yen Ling, 2013. "Dynamic transmission effects between the interest rate, the US dollar, and gold and crude oil prices," Economic Modelling, Elsevier, vol. 30(C), pages 792-798.
    5. Miao, Hong & Ramchander, Sanjay & Wang, Tianyang & Yang, Dongxiao, 2017. "Influential factors in crude oil price forecasting," Energy Economics, Elsevier, vol. 68(C), pages 77-88.
    6. Wei, Yu & Wang, Yudong & Huang, Dengshi, 2010. "Forecasting crude oil market volatility: Further evidence using GARCH-class models," Energy Economics, Elsevier, vol. 32(6), pages 1477-1484, November.
    7. Narayan, Paresh Kumar & Thuraisamy, Kannan S. & Wagner, Niklas F., 2017. "How do bond, equity and commodity cycles interact?," Finance Research Letters, Elsevier, vol. 21(C), pages 151-156.
    8. Hongbing Ouyang & Xiaolu Wei & Qiufeng Wu, 2019. "Agricultural commodity futures prices prediction via long- and short-term time series network," Journal of Applied Economics, Taylor & Francis Journals, vol. 22(1), pages 468-483, January.
    9. Bu, Hui, 2014. "Effect of inventory announcements on crude oil price volatility," Energy Economics, Elsevier, vol. 46(C), pages 485-494.
    10. Kumar, Satish, 2017. "On the nonlinear relation between crude oil and gold," Resources Policy, Elsevier, vol. 51(C), pages 219-224.
    11. Ma, Feng & Liao, Yin & Zhang, Yaojie & Cao, Yang, 2019. "Harnessing jump component for crude oil volatility forecasting in the presence of extreme shocks," Journal of Empirical Finance, Elsevier, vol. 52(C), pages 40-55.
    12. Coleman, Les, 2012. "Explaining crude oil prices using fundamental measures," Energy Policy, Elsevier, vol. 40(C), pages 318-324.
    13. Ding, Shusheng & Zhang, Yongmin, 2020. "Cross market predictions for commodity prices," Economic Modelling, Elsevier, vol. 91(C), pages 455-462.
    14. Geman, Hélyette & Kharoubi, Cécile, 2008. "WTI crude oil Futures in portfolio diversification: The time-to-maturity effect," Journal of Banking & Finance, Elsevier, vol. 32(12), pages 2553-2559, December.
    15. Chen, Rongda & Xu, Jianjun, 2019. "Forecasting volatility and correlation between oil and gold prices using a novel multivariate GAS model," Energy Economics, Elsevier, vol. 78(C), pages 379-391.
    16. Filippidis, Michail & Filis, George & Kizys, Renatas, 2020. "Oil price shocks and EMU sovereign yield spreads," Energy Economics, Elsevier, vol. 86(C).
    17. Gargano, Antonio & Timmermann, Allan, 2014. "Forecasting commodity price indexes using macroeconomic and financial predictors," International Journal of Forecasting, Elsevier, vol. 30(3), pages 825-843.
    18. Yu, Lean & Zhao, Yang & Tang, Ling, 2014. "A compressed sensing based AI learning paradigm for crude oil price forecasting," Energy Economics, Elsevier, vol. 46(C), pages 236-245.
    19. Abdollahi, Hooman, 2020. "A novel hybrid model for forecasting crude oil price based on time series decomposition," Applied Energy, Elsevier, vol. 267(C).
    20. Siddhivinayak Kulkarni & Imad Haidar, 2009. "Forecasting Model for Crude Oil Price Using Artificial Neural Networks and Commodity Futures Prices," Papers 0906.4838, arXiv.org.
    21. Abhibasu Sen & Karabi Dutta Choudhury, 2020. "On the co-movement of crude, gold prices and stock index in the Indian market," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 7(03), pages 1-26, September.
    22. Sun, Shaolong & Sun, Yuying & Wang, Shouyang & Wei, Yunjie, 2018. "Interval decomposition ensemble approach for crude oil price forecasting," Energy Economics, Elsevier, vol. 76(C), pages 274-287.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wu, Chunying & Wang, Jianzhou & Hao, Yan, 2022. "Deterministic and uncertainty crude oil price forecasting based on outlier detection and modified multi-objective optimization algorithm," Resources Policy, Elsevier, vol. 77(C).
    2. Krzysztof Drachal, 2018. "Determining Time-Varying Drivers of Spot Oil Price in a Dynamic Model Averaging Framework," Energies, MDPI, vol. 11(5), pages 1-24, May.
    3. Wang, Lu & Ma, Feng & Liu, Jing & Yang, Lin, 2020. "Forecasting stock price volatility: New evidence from the GARCH-MIDAS model," International Journal of Forecasting, Elsevier, vol. 36(2), pages 684-694.
    4. Lang, Korbinian & Auer, Benjamin R., 2020. "The economic and financial properties of crude oil: A review," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    5. Liu, Jing & Ma, Feng & Tang, Yingkai & Zhang, Yaojie, 2019. "Geopolitical risk and oil volatility: A new insight," Energy Economics, Elsevier, vol. 84(C).
    6. Butler, Sunil & Kokoszka, Piotr & Miao, Hong & Shang, Han Lin, 2021. "Neural network prediction of crude oil futures using B-splines," Energy Economics, Elsevier, vol. 94(C).
    7. Gustavo Carvalho Santos & Flavio Barboza & Antônio Cláudio Paschoarelli Veiga & Mateus Ferreira Silva, 2021. "Forecasting Brazilian Ethanol Spot Prices Using LSTM," Energies, MDPI, vol. 14(23), pages 1-15, November.
    8. Gkillas, Konstantinos & Manickavasagam, Jeevananthan & Visalakshmi, S., 2022. "Effects of fundamentals, geopolitical risk and expectations factors on crude oil prices," Resources Policy, Elsevier, vol. 78(C).
    9. Chen, Haixin & Liu, Yancheng & Li, Xiangjie & Gu, Xiang & Fan, Kun, 2024. "Oil market regulatory: An ensembled model for prediction," Finance Research Letters, Elsevier, vol. 67(PA).
    10. Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian, 2020. "Forecasting realized oil-price volatility: The role of financial stress and asymmetric loss," Journal of International Money and Finance, Elsevier, vol. 104(C).
    11. Wen, Danyan & Liu, Li & Wang, Yudong & Zhang, Yaojie, 2022. "Forecasting crude oil market returns: Enhanced moving average technical indicators," Resources Policy, Elsevier, vol. 76(C).
    12. Olivier Rousse & Benoît Sévi, 2017. "Informed Trading in Oil-Futures Market," Working Papers hal-01460186, HAL.
    13. Liang, Chao & Tang, Linchun & Li, Yan & Wei, Yu, 2020. "Which sentiment index is more informative to forecast stock market volatility? Evidence from China," International Review of Financial Analysis, Elsevier, vol. 71(C).
    14. Cai, Yifei & Mignon, Valérie & Saadaoui, Jamel, 2022. "Not all political relation shocks are alike: Assessing the impacts of US–China tensions on the oil market," Energy Economics, Elsevier, vol. 114(C).
    15. He, Mengxi & Zhang, Yaojie & Wen, Danyan & Wang, Yudong, 2021. "Forecasting crude oil prices: A scaled PCA approach," Energy Economics, Elsevier, vol. 97(C).
    16. Yue-Jun Zhang & Shu-Hui Li, 2019. "The impact of investor sentiment on crude oil market risks: evidence from the wavelet approach," Quantitative Finance, Taylor & Francis Journals, vol. 19(8), pages 1357-1371, August.
    17. Su, Chi Wei & Song, Xin Yue & Qin, Meng & Lobonţ, Oana-Ramona, 2024. "Is copper a safe haven for oil?," Resources Policy, Elsevier, vol. 91(C).
    18. Wang, Xinya & Lucey, Brian & Huang, Shupei, 2022. "Can gold hedge against oil price movements: Evidence from GARCH-EVT wavelet modeling," Journal of Commodity Markets, Elsevier, vol. 27(C).
    19. Drachal, Krzysztof, 2016. "Forecasting spot oil price in a dynamic model averaging framework — Have the determinants changed over time?," Energy Economics, Elsevier, vol. 60(C), pages 35-46.
    20. Stanislav Anatolyev & Sergei Seleznev & Veronika Selezneva, 2021. "How does the financial market update beliefs about the real economy? Evidence from the oil market," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(7), pages 938-961, November.

    More about this item

    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:plo:pone00:0268996. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.