IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v482y2017icp65-73.html
   My bibliography  Save this article

Financial networks based on Granger causality: A case study

Author

Listed:
  • Papana, Angeliki
  • Kyrtsou, Catherine
  • Kugiumtzis, Dimitris
  • Diks, Cees

Abstract

Connectivity analysis is performed on a long financial record of 21 international stock indices employing a linear and a nonlinear causality measure, the conditional Granger causality index (CGCI) and the partial mutual information on mixed embedding (PMIME), respectively. Both measures aim to specify the direction of the interrelationships among the international stock indexes and portray the links of the resulting networks, by the presence of direct couplings between variables exploiting all available information. However, their differences are assessed due to the presence of nonlinearity. The weighted networks formed with respect to the causality measures are transformed to binary ones using a significance test. The financial networks are formed on sliding windows in order to examine the network characteristics and trace changes in the connectivity structure. Subsequently, two statistical network quantities are calculated; the average degree and the average shortest path length. The empirical findings reveal interesting time-varying properties of the constructed network, which are clearly dependent on the nature of the financial cycle.

Suggested Citation

  • Papana, Angeliki & Kyrtsou, Catherine & Kugiumtzis, Dimitris & Diks, Cees, 2017. "Financial networks based on Granger causality: A case study," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 65-73.
    • Handle: RePEc:eee:phsmap:v:482:y:2017:i:c:p:65-73
    DOI: 10.1016/j.physa.2017.04.046
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437117303503
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2017.04.046?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Papadopoulos, G. & Kugiumtzis, D., 2015. "Estimation of connectivity measures in gappy time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 387-398.
    2. Stefano Schiavo & Javier Reyes & Giorgio Fagiolo, 2010. "International trade and financial integration: a weighted network analysis," Quantitative Finance, Taylor & Francis Journals, vol. 10(4), pages 389-399.
    3. Lyócsa, Štefan & Výrost, Tomáš & Baumöhl, Eduard, 2012. "Stock market networks: The dynamic conditional correlation approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(16), pages 4147-4158.
    4. 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.
    5. R. Mantegna, 1999. "Hierarchical structure in financial markets," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 11(1), pages 193-197, September.
    6. Kevin F. Ryan & David E. A. Giles, 1998. "Testing for Unit Roots With Missing Observations," Department Discussion Papers 9802, Department of Economics, University of Victoria.
    7. Nobi, Ashadun & Maeng, Seong Eun & Ha, Gyeong Gyun & Lee, Jae Woo, 2014. "Effects of global financial crisis on network structure in a local stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 407(C), pages 135-143.
    8. Tse, Chi K. & Liu, Jing & Lau, Francis C.M., 2010. "A network perspective of the stock market," Journal of Empirical Finance, Elsevier, vol. 17(4), pages 659-667, September.
    9. Yao, Can-Zhong & Lin, Ji-Nan & Lin, Qing-Wen & Zheng, Xu-Zhou & Liu, Xiao-Feng, 2016. "A study of causality structure and dynamics in industrial electricity consumption based on Granger network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 297-320.
    10. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    11. Johansen, Soren, 1991. "Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models," Econometrica, Econometric Society, vol. 59(6), pages 1551-1580, November.
    12. Heiberger, Raphael H., 2014. "Stock network stability in times of crisis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 393(C), pages 376-381.
    13. Yao, Can-Zhong & Lin, Qing-Wen & Lin, Ji-Nan, 2016. "A study of industrial electricity consumption based on partial Granger causality network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 629-646.
    14. Wang, Gang-Jin & Xie, Chi, 2015. "Correlation structure and dynamics of international real estate securities markets: A network perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 424(C), pages 176-193.
    15. Djauhari, Maman Abdurachman & Gan, Siew Lee, 2015. "Optimality problem of network topology in stocks market analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 108-114.
    16. Kazemilari, Mansooreh & Djauhari, Maman Abdurachman, 2015. "Correlation network analysis for multi-dimensional data in stocks market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 429(C), pages 62-75.
    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. Gautier Marti & Frank Nielsen & Miko{l}aj Bi'nkowski & Philippe Donnat, 2017. "A review of two decades of correlations, hierarchies, networks and clustering in financial markets," Papers 1703.00485, arXiv.org, revised Nov 2020.
    2. Huang, Wei-Qiang & Zhuang, Xin-Tian & Yao, Shuang & Uryasev, Stan, 2016. "A financial network perspective of financial institutions’ systemic risk contributions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 183-196.
    3. Výrost, Tomáš & Lyócsa, Štefan & Baumöhl, Eduard, 2015. "Granger causality stock market networks: Temporal proximity and preferential attachment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 427(C), pages 262-276.
    4. Haiming Long & Ji Zhang & Nengyu Tang, 2017. "Does network topology influence systemic risk contribution? A perspective from the industry indices in Chinese stock market," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-19, July.
    5. Coletti, Paolo, 2016. "Comparing minimum spanning trees of the Italian stock market using returns and volumes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 463(C), pages 246-261.
    6. Chen, Yanhua & Li, Youwei & Pantelous, Athanasios A. & Stanley, H. Eugene, 2022. "Short-run disequilibrium adjustment and long-run equilibrium in the international stock markets: A network-based approach," International Review of Financial Analysis, Elsevier, vol. 79(C).
    7. Chuangxia Huang & Xian Zhao & Renli Su & Xiaoguang Yang & Xin Yang, 2022. "Dynamic network topology and market performance: A case of the Chinese stock market," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 1962-1978, April.
    8. Brida, Juan Gabriel & Matesanz, David & Seijas, Maria Nela, 2016. "Network analysis of returns and volume trading in stock markets: The Euro Stoxx case," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 751-764.
    9. Vidal-Tomás, David, 2021. "Transitions in the cryptocurrency market during the COVID-19 pandemic: A network analysis," Finance Research Letters, Elsevier, vol. 43(C).
    10. Su, Qingqing & Tu, Lilan & Wang, Xianjia & Rong, Hang, 2022. "Construction and robustness of directed-weighted financial stock networks via meso-scales," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 605(C).
    11. Lyócsa, Štefan & Výrost, Tomáš & Baumöhl, Eduard, 2019. "Return spillovers around the globe: A network approach," Economic Modelling, Elsevier, vol. 77(C), pages 133-146.
    12. He, Chengying & Wen, Zhang & Huang, Ke & Ji, Xiaoqin, 2022. "Sudden shock and stock market network structure characteristics: A comparison of past crisis events," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
    13. Zhang, Weiping & Zhuang, Xintian, 2019. "The stability of Chinese stock network and its mechanism," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 748-761.
    14. Ling, Yu-Xiu & Xie, Chi & Wang, Gang-Jin, 2022. "Interconnectedness between convertible bonds and underlying stocks in the Chinese capital market: A multilayer network perspective," Emerging Markets Review, Elsevier, vol. 52(C).
    15. Yong Tang & Jason Jie Xiong & Zi-Yang Jia & Yi-Cheng Zhang, 2018. "Complexities in Financial Network Topological Dynamics: Modeling of Emerging and Developed Stock Markets," Complexity, Hindawi, vol. 2018, pages 1-31, November.
    16. Weibo Li & Wei Liu & Lei Wu & Xue Guo, 2021. "Risk spillover networks in financial system based on information theory," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-20, June.
    17. Bu, Hui & Tang, Wenjin & Wu, Junjie, 2019. "Time-varying comovement and changes of comovement structure in the Chinese stock market: A causal network method," Economic Modelling, Elsevier, vol. 81(C), pages 181-204.
    18. Nie, Chun-Xiao & Song, Fu-Tie, 2018. "Constructing financial network based on PMFG and threshold method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 495(C), pages 104-113.
    19. Hosseini, Seyed Soheil & Wormald, Nick & Tian, Tianhai, 2021. "A Weight-based Information Filtration Algorithm for Stock-correlation Networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 563(C).
    20. Brida, Juan Gabriel & Gómez, David Matesanz & Seijas, Maria Nela, 2017. "Debt and growth: A non-parametric approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 883-894.

    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:eee:phsmap:v:482:y:2017:i:c:p:65-73. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.