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Analyzing volatility patterns in the Chinese stock market using partial mutual information-based distances

Author

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  • Arash Sioofy Khoojine

    (Yibin University)

  • Ziyun Feng

    (Yibin University)

  • Mahboubeh Shadabfar

    (Nanjing University of Science and Technology)

  • Negar Sioofy Khoojine

    (Middle East Technical University)

Abstract

This study examines the dynamic range of financial networks in the Chinese stock market between 2019 and 2021. It provides an objective assessment of the network’s characteristics and scalability. The research time-frame is divided into three segments, reflecting the fluctuations of the financial market, including stable, volatile, and follow-up periods. To establish correlations among companies, the study employs the partial mutual information distance (PMID) method, followed by the construction of three minimum spanning tree (MST) networks for each period. Given the non-linear nature of financial phenomena, PMID is found to be more appropriate than linear methods in the study of financial markets. Additionally, the power law is observed in all three networks. This study is organized hierarchically into levels of nodes, clusters, and global indicators, providing a comprehensive perspective on network behavior and adaptation. Three-level indicators are calculated for each of the three networks, and the findings display a noteworthy variation between the volatile network and the other two networks. During stable and follow-up periods, a node-level analysis has indicated strong interconnectedness among companies. In contrast, during volatility, there are dynamic fluctuations in network dynamics. Cluster-level analysis reveals that firms become more essential connectors and actively engaged, with increased centrality. A global analysis shows that companies are more likely to form partnerships with counterparts possessing similar degrees during times of market volatility compared to periods of stability or follow-up periods. To assess the resilience of the constructed networks, we employed Markov chain analysis and examined the maximal connected component (MCC); the study findings suggest that the network is more susceptible to volatility in the observed second period, while demonstrating greater resilience in the follow-up period indicating recovery of financial markets. Graphic abstract

Suggested Citation

  • Arash Sioofy Khoojine & Ziyun Feng & Mahboubeh Shadabfar & Negar Sioofy Khoojine, 2023. "Analyzing volatility patterns in the Chinese stock market using partial mutual information-based distances," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 96(12), pages 1-21, December.
    • Handle: RePEc:spr:eurphb:v:96:y:2023:i:12:d:10.1140_epjb_s10051-023-00628-6
    DOI: 10.1140/epjb/s10051-023-00628-6
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    References listed on IDEAS

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    1. Huang, Chuangxia & Wen, Shigang & Li, Mengge & Wen, Fenghua & Yang, Xin, 2021. "An empirical evaluation of the influential nodes for stock market network: Chinese A-shares case," Finance Research Letters, Elsevier, vol. 38(C).
    2. Tian Qiu & Bo Zheng & Guang Chen, 2010. "Adaptive financial networks with static and dynamic thresholds," Papers 1002.3432, arXiv.org.
    3. Afees A. Salisu & Riza Demirer & Rangan Gupta, 2023. "Technological Shocks and Stock Market Volatility Over a Century: A GARCH-MIDAS Approach," Working Papers 202308, University of Pretoria, Department of Economics.
    4. Zhimei Lei & Kuo-Jui Wu & Li Cui & Ming K Lim, 2018. "A Hybrid Approach to Explore the Risk Dependency Structure among Agribusiness Firms," Sustainability, MDPI, vol. 10(2), pages 1-17, February.
    5. Longsheng Cheng & Mahboubeh Shadabfar & Arash Sioofy Khoojine, 2023. "A State-of-the-Art Review of Probabilistic Portfolio Management for Future Stock Markets," Mathematics, MDPI, vol. 11(5), pages 1-34, February.
    6. repec:mje:mjejnl:v:12:y:2017:i:3:p:161-174 is not listed on IDEAS
    7. Lei Li & Kun Qin & Desheng Wu, 2023. "A Hybrid Approach for the Assessment of Risk Spillover to ESG Investment in Financial Networks," Sustainability, MDPI, vol. 15(7), pages 1-16, April.
    8. Boginski, Vladimir & Butenko, Sergiy & Pardalos, Panos M., 2005. "Statistical analysis of financial networks," Computational Statistics & Data Analysis, Elsevier, vol. 48(2), pages 431-443, February.
    9. Irena Vodenska & Alexander P. Becker & Di Zhou & Dror Y. Kenett & H. Eugene Stanley & Shlomo Havlin, 2016. "Community Analysis of Global Financial Markets," Risks, MDPI, vol. 4(2), pages 1-15, May.
    10. Xinxin Xu & Sheng Ma & Ziqiang Zeng, 2019. "Complex network analysis of bilateral international investment under de-globalization: Structural properties and evolution," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-16, April.
    11. Mansooreh Kazemilari & Ali Mohamadi, 2018. "Topological Network Analysis Based on Dissimilarity Measure of Multivariate Time Series Evolution in the Subprime Crisis," IJFS, MDPI, vol. 6(2), pages 1-16, May.
    12. Fenghua Pan & Chun Yang & He Wang & Dariusz Wójcik, 2020. "Linking global financial networks with regional development: a case study of Linyi, China," Regional Studies, Taylor & Francis Journals, vol. 54(2), pages 187-197, February.
    13. Chu, J. & Nadarajah, S., 2017. "A statistical analysis of UK financial networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 445-459.
    14. Román Ferrer & Rafael Benítez & Vicente J. Bolós, 2021. "Interdependence between Green Financial Instruments and Major Conventional Assets: A Wavelet-Based Network Analysis," Mathematics, MDPI, vol. 9(8), pages 1-20, April.
    15. Igor Kravchuk, 2017. "Interconnectedness and Contagion Effects in International Financial Instruments Markets," Montenegrin Journal of Economics, Economic Laboratory for Transition Research (ELIT), vol. 13(3), pages 161-174.
    16. Yan, Jie & Möhrlen, Corinna & Göçmen, Tuhfe & Kelly, Mark & Wessel, Arne & Giebel, Gregor, 2022. "Uncovering wind power forecasting uncertainty sources and their propagation through the whole modelling chain," Renewable and Sustainable Energy Reviews, Elsevier, vol. 165(C).
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