IDEAS home Printed from https://ideas.repec.org/a/spr/eurphb/v92y2019i8d10.1140_epjb_e2019-100132-6.html
   My bibliography  Save this article

Influence of petroleum and gas trade on EU economies from the reduced Google matrix analysis of UN COMTRADE data

Author

Listed:
  • Célestin Coquidé

    (Institut UTINAM, OSU THETA, Université de Bourgogne Franche-Comté, CNRS)

  • Leonardo Ermann

    (Departamento de Física Teórica, GIyA, CNEA)

  • José Lages

    (Institut UTINAM, OSU THETA, Université de Bourgogne Franche-Comté, CNRS)

  • Dima L. Shepelyansky

    (Laboratoire de Physique Théorique, IRSAMC, Université de Toulouse, CNRS, UPS)

Abstract

Using the United Nations COMTRADE database (United Nations Commodity Trade Statistics Database, http://comtrade.un.org/db/ (accessed January 2019)) we apply the reduced Google matrix (REGOMAX) algorithm to analyze the multiproduct world trade in years 2004–2016. Our approach allows determining the trade balance sensitivity of a group of countries to a specific product price increase from a specific exporting country taking into account all direct and indirect trade pathways via all world countries exchanging 61 UN COMTRADE identified trade products. On the basis of this approach we present the influence of trade in petroleum and gas products from Russia, USA, Saudi Arabia and Norway determining the sensitivity of each EU country. We show that the REGOMAX approach provides a new and more detailed analysis of trade influence propagation comparing to the usual approach based on export and import flows. Graphical abstract

Suggested Citation

  • Célestin Coquidé & Leonardo Ermann & José Lages & Dima L. Shepelyansky, 2019. "Influence of petroleum and gas trade on EU economies from the reduced Google matrix analysis of UN COMTRADE data," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 92(8), pages 1-14, August.
    • Handle: RePEc:spr:eurphb:v:92:y:2019:i:8:d:10.1140_epjb_e2019-100132-6
    DOI: 10.1140/epjb/e2019-100132-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1140/epjb/e2019-100132-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1140/epjb/e2019-100132-6?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
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Bouchaud,Jean-Philippe & Potters,Marc, 2003. "Theory of Financial Risk and Derivative Pricing," Cambridge Books, Cambridge University Press, number 9780521819169, 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. C'elestin Coquid'e & Jos'e Lages & Dima L. Shepelyansky, 2024. "Opinion formation in the world trade network," Papers 2401.02378, arXiv.org, revised Feb 2024.
    2. Célestin Coquidé & José Lages & Dima Shepelyansky, 2024. "Opinion Formation in the World Trade Network," Post-Print hal-04461784, HAL.
    3. C'elestin Coquid'e & Jos'e Lages & Dima L. Shepelyansky, 2020. "Crisis contagion in the world trade network," Papers 2002.07100, arXiv.org.
    4. Célestin Coquidé & José Lages & Dima Shepelyansky, 2020. "Interdependence of sectors of economic activities for world countries from the reduced Google matrix analysis of WTO data," Post-Print hal-02132487, HAL.
    5. Célestin Coquidé & José Lages & Leonardo Ermann & Dima Shepelyansky, 2022. "COVID-19 impact on the international trade," Post-Print hal-03536528, HAL.
    6. Frahm, Klaus M. & Shepelyansky, Dima L., 2020. "Google matrix analysis of bi-functional SIGNOR network of protein–protein interactions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 559(C).
    7. C'elestin Coquid'e & Jos'e Lages & Dima L. Shepelyansky, 2023. "Prospects of BRICS currency dominance in international trade," Papers 2305.00585, arXiv.org.
    8. Guillaume Rollin & José Lages & Dima L Shepelyansky, 2019. "Wikipedia network analysis of cancer interactions and world influence," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-26, September.

    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. Assaf Almog & Ferry Besamusca & Mel MacMahon & Diego Garlaschelli, 2015. "Mesoscopic Community Structure of Financial Markets Revealed by Price and Sign Fluctuations," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-16, July.
    2. Sebastiano Michele Zema & Giorgio Fagiolo & Tiziano Squartini & Diego Garlaschelli, 2021. "Mesoscopic Structure of the Stock Market and Portfolio Optimization," Papers 2112.06544, arXiv.org.
    3. S. Reimann, 2007. "Price dynamics from a simple multiplicative random process model," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 56(4), pages 381-394, April.
    4. Nicolas Langrené & Geoffrey Lee & Zili Zhu, 2016. "Switching To Nonaffine Stochastic Volatility: A Closed-Form Expansion For The Inverse Gamma Model," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 19(05), pages 1-37, August.
    5. Paulo Ferreira & Éder J.A.L. Pereira & Hernane B.B. Pereira, 2020. "From Big Data to Econophysics and Its Use to Explain Complex Phenomena," JRFM, MDPI, vol. 13(7), pages 1-10, July.
    6. V. Alfi & L. Pietronero & A. Zaccaria, 2008. "Minimal Agent Based Model For The Origin And Self-Organization Of Stylized Facts In Financial Markets," Papers 0807.1888, arXiv.org.
    7. Slanina, František, 2010. "A contribution to the systematics of stochastic volatility models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(16), pages 3230-3239.
    8. Guevara Hidalgo, Esteban, 2017. "Bin size independence in intra-day seasonalities for relative prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 722-732.
    9. F. Wang & P. Weber & K. Yamasaki & S. Havlin & H. E. Stanley, 2007. "Statistical regularities in the return intervals of volatility," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 55(2), pages 123-133, January.
    10. Selçuk, Faruk & Gençay, Ramazan, 2006. "Intraday dynamics of stock market returns and volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 367(C), pages 375-387.
    11. Pištěk, Miroslav & Slanina, František, 2011. "Diversity of scales makes an advantage: The case of the Minority Game," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(13), pages 2549-2561.
    12. Muhammad Zeeshan Younas, 2020. "How Did Risk Management Methods Change After The 2007 Sub-Prime Mortgage Crisis In The United Kingdom?," Bulletin of Business and Economics (BBE), Research Foundation for Humanity (RFH), vol. 9(1), pages 22-31, March.
    13. E. Bacry & S. Delattre & M. Hoffmann & J. F. Muzy, 2013. "Modelling microstructure noise with mutually exciting point processes," Quantitative Finance, Taylor & Francis Journals, vol. 13(1), pages 65-77, January.
    14. G. Livan & S. Alfarano & E. Scalas, 2011. "The fine structure of spectral properties for random correlation matrices: an application to financial markets," Papers 1102.4076, arXiv.org.
    15. Cornelis A. Los & Rossitsa M. Yalamova, 2004. "Multi-Fractal Spectral Analysis of the 1987 Stock Market Crash," Finance 0409050, University Library of Munich, Germany.
    16. Conlon, T. & Ruskin, H.J. & Crane, M., 2007. "Random matrix theory and fund of funds portfolio optimisation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 382(2), pages 565-576.
    17. Denis S. Grebenkov & Jeremy Serror, 2014. "Optimal Allocation of Trend Following Strategies," Papers 1410.8409, arXiv.org.
    18. Jin Zhang & Yi Xiang, 2008. "The implied volatility smirk," Quantitative Finance, Taylor & Francis Journals, vol. 8(3), pages 263-284.
    19. Ouyang, F.Y. & Zheng, B. & Jiang, X.F., 2014. "Spatial and temporal structures of four financial markets in Greater China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 402(C), pages 236-244.
    20. Danilo Delpini & Giacomo Bormetti, 2012. "Stochastic Volatility with Heterogeneous Time Scales," Papers 1206.0026, arXiv.org, revised Apr 2013.

    More about this item

    Keywords

    Statistical and Nonlinear Physics;

    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:spr:eurphb:v:92:y:2019:i:8:d:10.1140_epjb_e2019-100132-6. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.