IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2601.00395.html

Core-Periphery Dynamics in Market-Conditioned Financial Networks: A Conditional P-Threshold Mutual Information Approach

Author

Listed:
  • Kundan Mukhia
  • Imran Ansari
  • S R Luwang
  • Md Nurujjaman

Abstract

This study investigates how financial market structure reorganizes during the COVID-19 crash using a conditional p-threshold mutual information (MI) based Minimum Spanning Tree (MST) framework. We analyze nonlinear dependencies among the largest stocks from four diverse QUAD countries: the US, Japan, Australia, and India. Crashes are identified using the Hellinger distance and Hilbert spectrum; a crash occurs when HD = mu\_H + 2*sigma\_H, segmenting data into pre-crash, crash, and post-crash periods. Conditional p-threshold MI filters out common market effects and applies permutation-based significance testing. Resulting validated dependencies are used to construct MST networks for comparison across periods. Networks become more integrated during the crash, with shorter path lengths, higher centrality, and lower algebraic connectivity, indicating fragility. Core-periphery structure declines, with increased periphery vulnerability, and disassortative mixing facilitates shock transmission. Post-crash networks show only partial recovery. Aftershock analysis using the Gutenberg-Richter law indicates higher relative frequency of large volatility events following the crash. Results are consistent across all markets, highlighting the conditional p-threshold MI framework for capturing nonlinear interdependencies and systemic vulnerability.

Suggested Citation

  • Kundan Mukhia & Imran Ansari & S R Luwang & Md Nurujjaman, 2026. "Core-Periphery Dynamics in Market-Conditioned Financial Networks: A Conditional P-Threshold Mutual Information Approach," Papers 2601.00395, arXiv.org.
    • Handle: RePEc:arx:papers:2601.00395
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2601.00395
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Raj Kumar Pan & Sitabhra Sinha, 2007. "Collective behavior of stock price movements in an emerging market," Papers 0704.0773, arXiv.org, revised Nov 2007.
    2. Michel Marie Deza & Elena Deza, 2009. "Encyclopedia of Distances," Springer Books, in: Encyclopedia of Distances, chapter 1, pages 1-583, Springer.
    3. 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.
    4. Mukhia, Kundan & Rai, Anish & Luwang, S.R. & Nurujjaman, Md & Majhi, Sushovan & Hens, Chittaranjan, 2024. "Complex network analysis of cryptocurrency market during crashes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 653(C).
    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. M. Tumminello & T. Di Matteo & T. Aste & R. N. Mantegna, 2007. "Correlation based networks of equity returns sampled at different time horizons," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 55(2), pages 209-217, January.
    7. Huang, Wei-Qiang & Zhuang, Xin-Tian & Yao, Shuang, 2009. "A network analysis of the Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(14), pages 2956-2964.
    8. Pawe{l} Fiedor, 2014. "Mutual Information Rate-Based Networks in Financial Markets," Papers 1401.2548, arXiv.org.
    9. Dong, Zhiliang & An, Haizhong & Liu, Sen & Li, Zhengyang & Yuan, Meng, 2020. "Research on the time-varying network structure evolution of the stock indices of the BRICS countries based on fluctuation correlation," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 63-74.
    10. Anish Rai & Salam Rabindrajit Luwang & Md Nurujjaman & Chittaranjan Hens & Pratyay Kuila & Kanish Debnath, 2022. "Detection and Forecasting of Extreme event in Stock Price Triggered by Fundamental, Technical, and External Factors," Papers 2206.13860, arXiv.org, revised Apr 2023.
    11. Dror Y Kenett & Michele Tumminello & Asaf Madi & Gitit Gur-Gershgoren & Rosario N Mantegna & Eshel Ben-Jacob, 2010. "Dominating Clasp of the Financial Sector Revealed by Partial Correlation Analysis of the Stock Market," PLOS ONE, Public Library of Science, vol. 5(12), pages 1-14, December.
    12. Siokis, Fotios M., 2012. "Stock market dynamics: Before and after stock market crashes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1315-1322.
    13. X. F. Jiang & T. T. Chen & B. Zheng, 2014. "Structure of local interactions in complex financial dynamics," Papers 1406.0070, arXiv.org.
    14. Pawanesh, Pawanesh & Ansari, Imran & Sahni, Niteesh, 2025. "Exploring the core–periphery and community structure in the financial networks through random matrix theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 661(C).
    15. 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.
    16. Elena Deza & Michel Marie Deza, 2009. "Encyclopedia of Distances," Springer Books, Springer, number 978-3-642-00234-2, December.
    17. Wenyan Peng & Mingkai Wen & Xiongfei Jiang & Yan Li & Tingting Chen & Bo Zheng, 2024. "Global motion filtered nonlinear mutual information analysis: Enhancing dynamic portfolio strategies," PLOS ONE, Public Library of Science, vol. 19(7), pages 1-21, July.
    18. Caldarelli, Guido, 2007. "Scale-Free Networks: Complex Webs in Nature and Technology," OUP Catalogue, Oxford University Press, number 9780199211517.
    19. Burak Korkusuz & David G. McMillan & Dimos Kambouroudis, 2023. "Complex network analysis of volatility spillovers between global financial indicators and G20 stock markets," Empirical Economics, Springer, vol. 64(4), pages 1517-1537, April.
    20. Yang, Yue & Yang, Huijie, 2008. "Complex network-based time series analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(5), pages 1381-1386.
    21. Li, Yan & Jiang, Xiong-Fei & Tian, Yue & Li, Sai-Ping & Zheng, Bo, 2019. "Portfolio optimization based on network topology," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 671-681.
    22. 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.
    23. Kundan Mukhia & Anish Rai & SR Luwang & Md Nurujjaman & Sushovan Majhi & Chittaranjan Hens, 2024. "Complex network analysis of cryptocurrency market during crashes," Papers 2405.05642, arXiv.org.
    24. Lahmiri, Salim & Bekiros, Stelios, 2020. "Renyi entropy and mutual information measurement of market expectations and investor fear during the COVID-19 pandemic," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    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. Pawanesh, Pawanesh & Ansari, Imran & Sahni, Niteesh, 2025. "Exploring the core–periphery and community structure in the financial networks through random matrix theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 661(C).
    2. 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.
    3. 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.
    4. Nie, Chun-Xiao & Song, Fu-Tie, 2018. "Analyzing the stock market based on the structure of kNN network," Chaos, Solitons & Fractals, Elsevier, vol. 113(C), pages 148-159.
    5. Xue Guo & Hu Zhang & Tianhai Tian, 2018. "Development of stock correlation networks using mutual information and financial big data," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-16, April.
    6. Vishwas Kukreti & Hirdesh K. Pharasi & Priya Gupta & Sunil Kumar, 2020. "A perspective on correlation-based financial networks and entropy measures," Papers 2004.09448, arXiv.org.
    7. 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.
    8. 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.
    9. Bing Li, 2017. "Network Evolution of the Chinese Stock Market: A Study based on the CSI 300 Index," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 7(3), pages 1-5.
    10. Tao You & Paweł Fiedor & Artur Hołda, 2015. "Network Analysis of the Shanghai Stock Exchange Based on Partial Mutual Information," JRFM, MDPI, vol. 8(2), pages 1-19, June.
    11. Chun-Xiao Nie & Fu-Tie Song, 2021. "Entropy of Graphs in Financial Markets," Computational Economics, Springer;Society for Computational Economics, vol. 57(4), pages 1149-1166, April.
    12. 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).
    13. Nie, Chun-Xiao & Song, Fu-Tie, 2019. "Global Rényi index of the distance matrix," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 902-915.
    14. Li, Jianxuan & Shi, Yingying & Cao, Guangxi, 2018. "Topology structure based on detrended cross-correlation coefficient of exchange rate network of the belt and road countries," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 1140-1151.
    15. 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.
    16. Seyed Soheil Hosseini & Nick Wormald & Tianhai Tian, 2019. "A Weight-based Information Filtration Algorithm for Stock-Correlation Networks," Papers 1904.06007, arXiv.org.
    17. Nie, Chun-Xiao, 2022. "Analysis of critical events in the correlation dynamics of cryptocurrency market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).
    18. Kumar, Sushil & Kumar, Sunil & Kumar, Pawan, 2020. "Diffusion entropy analysis and random matrix analysis of the Indian stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 560(C).
    19. Guo, Xue & Li, Weibo & Zhang, Hu & Tian, Tianhai, 2022. "Multi-likelihood methods for developing relationship networks using stock market data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 585(C).
    20. V. A. Kalyagin & A. P. Koldanov & P. A. Koldanov & P. M. Pardalos, 2018. "Optimal decision for the market graph identification problem in a sign similarity network," Annals of Operations Research, Springer, vol. 266(1), pages 313-327, July.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:arx:papers:2601.00395. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.