IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0162362.html
   My bibliography  Save this article

Spatiotemporal Dynamics and Fitness Analysis of Global Oil Market: Based on Complex Network

Author

Listed:
  • Ruijin Du
  • Gaogao Dong
  • Lixin Tian
  • Minggang Wang
  • Guochang Fang
  • Shuai Shao

Abstract

We study the overall topological structure properties of global oil trade network, such as degree, strength, cumulative distribution, information entropy and weight clustering. The structural evolution of the network is investigated as well. We find the global oil import and export networks do not show typical scale-free distribution, but display disassortative property. Furthermore, based on the monthly data of oil import values during 2005.01–2014.12, by applying random matrix theory, we investigate the complex spatiotemporal dynamic from the country level and fitness evolution of the global oil market from a demand-side analysis. Abundant information about global oil market can be obtained from deviating eigenvalues. The result shows that the oil market has experienced five different periods, which is consistent with the evolution of country clusters. Moreover, we find the changing trend of fitness function agrees with that of gross domestic product (GDP), and suggest that the fitness evolution of oil market can be predicted by forecasting GDP values. To conclude, some suggestions are provided according to the results.

Suggested Citation

  • Ruijin Du & Gaogao Dong & Lixin Tian & Minggang Wang & Guochang Fang & Shuai Shao, 2016. "Spatiotemporal Dynamics and Fitness Analysis of Global Oil Market: Based on Complex Network," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-17, October.
    • Handle: RePEc:plo:pone00:0162362
    DOI: 10.1371/journal.pone.0162362
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0162362
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0162362&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0162362?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. Pukthuanthong, Kuntara & Roll, Richard, 2009. "Global market integration: An alternative measure and its application," Journal of Financial Economics, Elsevier, vol. 94(2), pages 214-232, November.
    2. M. Potters & J. P. Bouchaud & L. Laloux, 2005. "Financial Applications of Random Matrix Theory: Old Laces and New Pieces," Papers physics/0507111, arXiv.org.
    3. Billio, Monica & Getmansky, Mila & Lo, Andrew W. & Pelizzon, Loriana, 2012. "Econometric measures of connectedness and systemic risk in the finance and insurance sectors," Journal of Financial Economics, Elsevier, vol. 104(3), pages 535-559.
    4. An, Haizhong & Gao, Xiangyun & Fang, Wei & Ding, Yinghui & Zhong, Weiqiong, 2014. "Research on patterns in the fluctuation of the co-movement between crude oil futures and spot prices: A complex network approach," Applied Energy, Elsevier, vol. 136(C), pages 1067-1075.
    5. Zhang, Hai-Ying & Ji, Qiang & Fan, Ying, 2014. "Competition, transmission and pattern evolution: A network analysis of global oil trade," Energy Policy, Elsevier, vol. 73(C), pages 312-322.
    6. Gao, Cuixia & Sun, Mei & Shen, Bo, 2015. "Features and evolution of international fossil energy trade relationships: A weighted multilayer network analysis," Applied Energy, Elsevier, vol. 156(C), pages 542-554.
    7. Zhang, Hai-Ying & Ji, Qiang & Fan, Ying, 2013. "An evaluation framework for oil import security based on the supply chain with a case study focused on China," Energy Economics, Elsevier, vol. 38(C), pages 87-95.
    8. Münnix, Michael C. & Schäfer, Rudi & Guhr, Thomas, 2010. "Compensating asynchrony effects in the calculation of financial correlations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(4), pages 767-779.
    9. Lu, Weiwei & Su, Meirong & Zhang, Yan & Yang, Zhifeng & Chen, Bin & Liu, Gengyuan, 2014. "Assessment of energy security in China based on ecological network analysis: A perspective from the security of crude oil supply," Energy Policy, Elsevier, vol. 74(C), pages 406-413.
    10. Sunil Kumar & Nivedita Deo, 2012. "Correlation, Network and Multifractal Analysis of Global Financial Indices," Papers 1202.0409, arXiv.org.
    11. Plerou, V & Gopikrishnan, P & Rosenow, B & Amaral, L.A.N & Stanley, H.E, 2000. "A random matrix theory approach to financial cross-correlations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 287(3), pages 374-382.
    12. Zhong, Weiqiong & An, Haizhong & Gao, Xiangyun & Sun, Xiaoqi, 2014. "The evolution of communities in the international oil trade network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 42-52.
    13. An, Haizhong & Zhong, Weiqiong & Chen, Yurong & Li, Huajiao & Gao, Xiangyun, 2014. "Features and evolution of international crude oil trade relationships: A trading-based network analysis," Energy, Elsevier, vol. 74(C), pages 254-259.
    14. Zhang, Youguo, 2010. "Supply-side structural effect on carbon emissions in China," Energy Economics, Elsevier, vol. 32(1), pages 186-193, January.
    15. Michael C. Munnix & Rudi Schafer & Thomas Guhr, 2009. "Compensating asynchrony effects in the calculation of financial correlations," Papers 0910.2909, arXiv.org, revised Jul 2010.
    16. Bruno Giussani & George Hadjimatheou, 1991. "Modeling Regional House Prices In The United Kingdom," Papers in Regional Science, Wiley Blackwell, vol. 70(2), pages 201-219, April.
    17. Kim, Young Se & Rous, Jeffrey J., 2012. "House price convergence: Evidence from US state and metropolitan area panels," Journal of Housing Economics, Elsevier, vol. 21(2), pages 169-186.
    18. Bai, Min & Qin, Yafeng, 2015. "Commonality in liquidity in emerging markets: Another supply-side explanation," International Review of Economics & Finance, Elsevier, vol. 39(C), pages 90-106.
    19. Feher Kristen & Whelan James & Müller Samuel, 2011. "Assessing Modularity Using a Random Matrix Theory Approach," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-34, September.
    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. Zhifang He & Fangzhao Zhou, 2018. "Time-varying and asymmetric effects of the oil-specific demand shock on investor sentiment," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-18, August.
    2. Wang, Minggang & Zhao, Longfeng & Du, Ruijin & Wang, Chao & Chen, Lin & Tian, Lixin & Eugene Stanley, H., 2018. "A novel hybrid method of forecasting crude oil prices using complex network science and artificial intelligence algorithms," Applied Energy, Elsevier, vol. 220(C), pages 480-495.
    3. Zhang, Xin & Xie, Sheng & Vilela, André L.M. & Stanley, H. Eugene, 2019. "Inter-event time interval analysis of organizational-level activity: Venture capital market case," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 346-355.
    4. Du, Ruijin & Wang, Ya & Dong, Gaogao & Tian, Lixin & Liu, Yixiao & Wang, Minggang & Fang, Guochang, 2017. "A complex network perspective on interrelations and evolution features of international oil trade, 2002–2013," Applied Energy, Elsevier, vol. 196(C), pages 142-151.
    5. Du, Ruijin & Dong, Gaogao & Tian, Lixin & Wang, Yougui & Zhao, Longfeng & Zhang, Xin & Vilela, André L.M. & Stanley, H. Eugene, 2019. "Identifying the peak point of systemic risk in international crude oil importing trade," Energy, Elsevier, vol. 176(C), pages 281-291.

    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. Du, Ruijin & Dong, Gaogao & Tian, Lixin & Wang, Yougui & Zhao, Longfeng & Zhang, Xin & Vilela, André L.M. & Stanley, H. Eugene, 2019. "Identifying the peak point of systemic risk in international crude oil importing trade," Energy, Elsevier, vol. 176(C), pages 281-291.
    2. Du, Ruijin & Wang, Ya & Dong, Gaogao & Tian, Lixin & Liu, Yixiao & Wang, Minggang & Fang, Guochang, 2017. "A complex network perspective on interrelations and evolution features of international oil trade, 2002–2013," Applied Energy, Elsevier, vol. 196(C), pages 142-151.
    3. Xi, Xian & Zhou, Jinsheng & Gao, Xiangyun & Liu, Donghui & Zheng, Huiling & Sun, Qingru, 2019. "Impact of changes in crude oil trade network patterns on national economy," Energy Economics, Elsevier, vol. 84(C).
    4. Gao, Cuixia & Sun, Mei & Shen, Bo, 2015. "Features and evolution of international fossil energy trade relationships: A weighted multilayer network analysis," Applied Energy, Elsevier, vol. 156(C), pages 542-554.
    5. Li, Huajiao & An, Haizhong & Huang, Jiachen & Huang, Xuan & Mou, Songtao & Shi, Yanli, 2016. "The evolutionary stability of shareholders’ co-holding behavior for China’s listed energy companies based on associated maximal connected sub-graphs of derivative holding-based networks," Applied Energy, Elsevier, vol. 162(C), pages 1601-1607.
    6. Guan, Qing & An, Haizhong, 2017. "The exploration on the trade preferences of cooperation partners in four energy commodities’ international trade: Crude oil, coal, natural gas and photovoltaic," Applied Energy, Elsevier, vol. 203(C), pages 154-163.
    7. Li, Huajiao & An, Haizhong & Liu, Xueyong & Gao, Xiangyun & Fang, Wei & An, Feng, 2016. "Price fluctuation in the energy stock market based on fluctuation and co-fluctuation matrix transmission networks," Energy, Elsevier, vol. 117(P1), pages 73-83.
    8. Wang, Minggang & Chen, Ying & Tian, Lixin & Jiang, Shumin & Tian, Zihao & Du, Ruijin, 2016. "Fluctuation behavior analysis of international crude oil and gasoline price based on complex network perspective," Applied Energy, Elsevier, vol. 175(C), pages 109-127.
    9. Sun, Qingru & Gao, Xiangyun & Zhong, Weiqiong & Liu, Nairong, 2017. "The stability of the international oil trade network from short-term and long-term perspectives," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 345-356.
    10. Wang, Wenya & Fan, L.W. & Zhou, P., 2022. "Evolution of global fossil fuel trade dependencies," Energy, Elsevier, vol. 238(PC).
    11. Wen-Jie Xie & Na Wei & Wei-Xing Zhou, 2020. "Evolving efficiency and robustness of global oil trade networks," Papers 2004.05325, arXiv.org.
    12. Qing Guan & Haizhong An & Xiaoqing Hao & Xiaoliang Jia, 2016. "The Impact of Countries’ Roles on the International Photovoltaic Trade Pattern: The Complex Networks Analysis," Sustainability, MDPI, vol. 8(4), pages 1-16, March.
    13. Leonidas Sandoval Junior & Italo De Paula Franca, 2011. "Correlation of financial markets in times of crisis," Papers 1102.1339, arXiv.org, revised Mar 2011.
    14. Hao Meng & Wen-Jie Xie & Zhi-Qiang Jiang & Boris Podobnik & Wei-Xing Zhou & H. Eugene Stanley, 2013. "Systemic risk and spatiotemporal dynamics of the US housing market," Papers 1306.2831, arXiv.org.
    15. Hao, Xiaoqing, 2023. "Import competition and pressure in the international crude oil trade: A network analysis," Resources Policy, Elsevier, vol. 82(C).
    16. Li, Huajiao & Fang, Wei & An, Haizhong & Gao, Xiangyun & Yan, Lili, 2016. "Holding-based network of nations based on listed energy companies: An empirical study on two-mode affiliation network of two sets of actors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 449(C), pages 224-232.
    17. Li, Huajiao & An, Haizhong & Fang, Wei & Wang, Yue & Zhong, Weiqiong & Yan, Lili, 2017. "Global energy investment structure from the energy stock market perspective based on a Heterogeneous Complex Network Model," Applied Energy, Elsevier, vol. 194(C), pages 648-657.
    18. Peixiang Jiang & Chao Ding & Zhiliang Dong & Sen Liu & Yichi Zhang, 2022. "Research on the Trade Characteristics of Conventional Energy Network Countries: Based on the Trade Characteristics of Leading Countries," Sustainability, MDPI, vol. 14(24), pages 1-18, December.
    19. Hao, Xiaoqing & An, Haizhong & Qi, Hai & Gao, Xiangyun, 2016. "Evolution of the exergy flow network embodied in the global fossil energy trade: Based on complex network," Applied Energy, Elsevier, vol. 162(C), pages 1515-1522.
    20. Huan Chen & Lixin Tian & Minggang Wang & Zaili Zhen, 2017. "Analysis of the Dynamic Evolutionary Behavior of American Heating Oil Spot and Futures Price Fluctuation Networks," Sustainability, MDPI, vol. 9(4), pages 1-29, April.

    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:pone00:0162362. 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.