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Immediate Causality Network of Stock Markets

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  • Li Zhou
  • Lu Qiu
  • Changgui Gu
  • Huijie Yang

Abstract

A financial system contains many elements networked by their relationships. Extensive works show that topological structure of the network stores rich information on evolutionary behaviors of the system such as early warning signals of collapses and/or crises. Existing works focus mainly on the network structure within a single stock market, while a collapse/crisis occurs in a macro-scale covering several or even all markets in the world. This mismatch of scale leads to unacceptable noise to the topological structure, and lack of information stored in relationships between different markets. In this work by using the transfer entropy we reconstruct the influential network between ten typical stock markets distributed in the world. Interesting findings include, before a financial crisis the connection strength reaches a maxima, which can act as an early warning signal of financial crises; The markets in America are mono-directionally and strongly influenced by that in Europe and act as the center; Some strongly linked pairs have also close correlations. The findings are helpful in understanding the evolution and modelling the dynamical process of the global financial system.

Suggested Citation

  • Li Zhou & Lu Qiu & Changgui Gu & Huijie Yang, 2018. "Immediate Causality Network of Stock Markets," Papers 1802.02699, arXiv.org.
  • Handle: RePEc:arx:papers:1802.02699
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    Cited by:

    1. Yan, Shuang & Gu, Changgui & Yang, Huijie, 2024. "Bridge successive states for a complex system with evolutionary matrix," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 637(C).
    2. M. Mija'il Mart'inez-Ramos & Parisa Majari & Andres R. Cruz-Hern'andez & Hirdesh K. Pharasi & Manan Vyas, 2024. "Coarse graining correlation matrices according to macrostructures: Financial markets as a paradigm," Papers 2402.05364, arXiv.org, revised Jun 2024.
    3. Qiu, Lu & Yang, Huijie, 2020. "Transfer entropy calculation for short time sequences with application to stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 559(C).
    4. Sasan Barak & Navid Parvini, 2023. "Transfer‐entropy‐based dynamic feature selection for evaluating Bitcoin price drivers," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(12), pages 1695-1726, December.
    5. Xie, Wen-Jie & Yong, Yang & Wei, Na & Yue, Peng & Zhou, Wei-Xing, 2021. "Identifying states of global financial market based on information flow network motifs," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).

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