{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T13:46:01Z","timestamp":1785764761032,"version":"3.56.0"},"reference-count":73,"publisher":"AIP Publishing","issue":"8","content-domain":{"domain":["pubs.aip.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,8,1]]},"abstract":"<jats:p>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) network framework. We analyze nonlinear dependencies among the largest stocks from four economically diverse QUAD countries: the United States, Japan, Australia, and India. The crash period is identified using the Hellinger distance and further characterized by the Hilbert spectrum. A crash is defined when the Hellinger distance exceeds the threshold, enabling the segmentation of the data into pre-crash, crash, and post-crash periods. To isolate direct stock-level dependencies, the conditional p-threshold MI approach filters out common market effects and applies permutation-based significance testing. The resulting statistically validated dependencies are used to construct MST networks, allowing consistent comparison across market periods. The network analysis reveals common crisis-related dynamics across all markets. During the crash, networks become more integrated, with shorter path lengths and higher centrality, while algebraic connectivity declines, indicating increased structural fragility. A clear reorganization of the core\u2013periphery structure is observed, with declining core concentration and increasing periphery fragility, supported by disassortative mixing that facilitates shock transmission. In the post-crash period, network topology shows partial and non-uniform recovery, suggesting persistent structural effects. This interpretation is further supported by an aftershock analysis based on the Gutenberg\u2013Richter law, which indicates a higher relative frequency of large volatility events following the crash. The consistency of these findings across all four markets highlights the effectiveness of the conditional p-threshold MI framework for capturing nonlinear interdependencies and systemic vulnerability in financial markets.<\/jats:p>","DOI":"10.1063\/5.0320572","type":"journal-article","created":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T12:55:14Z","timestamp":1785761714000},"update-policy":"https:\/\/doi.org\/10.1063\/aip-crossmark-policy-page","source":"Crossref","is-referenced-by-count":0,"title":["Core\u2013periphery dynamics in market-conditioned financial networks: A conditional p-threshold mutual information approach"],"prefix":"10.1063","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-5887-0047","authenticated-orcid":false,"given":"Kundan","family":"Mukhia","sequence":"first","affiliation":[{"name":"Department of Physics, National Institute of Technology Sikkim 1 , Sikkim 737139,","place":["India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-2481-0015","authenticated-orcid":false,"given":"Imran","family":"Ansari","sequence":"additional","affiliation":[{"name":"Department of Management Studies, Indian Institute of Science 2 , Bengaluru,","place":["India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-7210-1862","authenticated-orcid":false,"given":"Salam","family":"Rabindrajit Luwang","sequence":"additional","affiliation":[{"name":"Department of Physics, National Institute of Technology Sikkim 1 , Sikkim 737139,","place":["India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2543-3411","authenticated-orcid":false,"given":"Md.","family":"Nurujjaman","sequence":"additional","affiliation":[{"name":"Department of Physics, National Institute of Technology Sikkim 1 , Sikkim 737139,","place":["India"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"317","published-online":{"date-parts":[[2026,8,3]]},"reference":[{"key":"2026080308550944100_c1","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1007\/s100510050929","article-title":"Hierarchical structure in financial markets","volume":"11","year":"1999","journal-title":"Eur. Phys. J. B"},{"key":"2026080308550944100_c2","doi-asserted-by":"publisher","first-page":"1381","DOI":"10.1016\/j.physa.2007.10.055","article-title":"Complex network-based time series analysis","volume":"387","year":"2008","journal-title":"Physica A"},{"key":"2026080308550944100_c3","doi-asserted-by":"publisher","first-page":"130095","DOI":"10.1016\/j.physa.2024.130095","article-title":"Complex network analysis of cryptocurrency market during crashes","volume":"653","year":"2024","journal-title":"Physica A"},{"key":"2026080308550944100_c4","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1140\/epjb\/e2006-00414-4","article-title":"Correlation based networks of equity returns sampled at different time horizons","volume":"55","year":"2007","journal-title":"Eur. Phys. J. B"},{"key":"2026080308550944100_c5","unstructured":"I.\n            Ansari\n           and P.Pawanesh, \u201cA comprehensive review of core-periphery and community detection paradigms,\u201d arXiv:2511.17657 (2025)."},{"key":"2026080308550944100_c6","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1016\/j.jempfin.2010.04.008","article-title":"A network perspective of the stock market","volume":"17","year":"2010","journal-title":"J. Empir. Finance"},{"key":"2026080308550944100_c7","doi-asserted-by":"publisher","first-page":"10421","DOI":"10.1073\/pnas.0500298102","article-title":"A tool for filtering information in complex systems","volume":"102","year":"2005","journal-title":"Proc. Natl. Acad. Sci. U.S.A."},{"key":"2026080308550944100_c8","doi-asserted-by":"publisher","first-page":"431","DOI":"10.1016\/j.csda.2004.02.004","article-title":"Statistical analysis of financial networks","volume":"48","year":"2005","journal-title":"Comput. Stat. Data Anal."},{"key":"2026080308550944100_c9","doi-asserted-by":"publisher","first-page":"376","DOI":"10.1016\/j.physa.2013.08.053","article-title":"Stock network stability in times of crisis","volume":"393","year":"2014","journal-title":"Physica A"},{"key":"2026080308550944100_c10","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1238\/Physica.Topical.106a00048","article-title":"Asset trees and asset graphs in financial markets","volume":"T106","year":"2003","journal-title":"Phys. Scr."},{"key":"2026080308550944100_c11","doi-asserted-by":"publisher","first-page":"3171","DOI":"10.1016\/j.cor.2005.01.027","article-title":"Mining market data: A network approach","volume":"33","year":"2006","journal-title":"Comput. Oper. Res."},{"key":"2026080308550944100_c12","doi-asserted-by":"publisher","first-page":"130403","DOI":"10.1016\/j.physa.2025.130403","article-title":"Exploring the core\u2013periphery and community structure in the financial networks through random matrix theory","volume":"661","year":"2025","journal-title":"Physica A"},{"key":"2026080308550944100_c13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-025-20777-1","article-title":"A novel portfolio construction strategy based on the core-periphery profile of stocks","volume":"15","year":"2025","journal-title":"Sci. Rep."},{"key":"2026080308550944100_c14","doi-asserted-by":"publisher","first-page":"1091","DOI":"10.1016\/j.physa.2019.04.128","article-title":"Network analysis of the Chinese stock market during the turbulence of 2015\u20132016 using log-returns, volumes and mutual information","volume":"523","year":"2019","journal-title":"Physica A"},{"key":"2026080308550944100_c15","doi-asserted-by":"publisher","first-page":"671","DOI":"10.1016\/j.physa.2018.10.014","article-title":"Portfolio optimization based on network topology","volume":"515","year":"2019","journal-title":"Physica A"},{"key":"2026080308550944100_c16","doi-asserted-by":"publisher","first-page":"e0303707","DOI":"10.1371\/journal.pone.0303707","article-title":"Global motion filtered nonlinear mutual information analysis: Enhancing dynamic portfolio strategies","volume":"19","year":"2024","journal-title":"PLoS One"},{"key":"2026080308550944100_c17","doi-asserted-by":"publisher","first-page":"e15032","DOI":"10.1371\/journal.pone.0015032","article-title":"Dominating clasp of the financial sector revealed by partial correlation analysis of the stock market","volume":"5","year":"2010","journal-title":"PLoS One"},{"key":"2026080308550944100_c18","doi-asserted-by":"publisher","first-page":"1665","DOI":"10.1038\/srep01665","article-title":"Spread of risk across financial markets: Better to invest in the peripheries","volume":"3","year":"2013","journal-title":"Sci. Rep."},{"key":"2026080308550944100_c19","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/0261-5606(94)00001-H","article-title":"Is the correlation in international equity returns constant: 1960\u20131990?","volume":"14","year":"1995","journal-title":"J. Int. Money Finance"},{"key":"2026080308550944100_c20","doi-asserted-by":"publisher","first-page":"035106","DOI":"10.1103\/PhysRevE.64.035106","article-title":"Quantifying and interpreting collective behavior in financial markets","volume":"64","year":"2001","journal-title":"Phys. Rev. E"},{"key":"2026080308550944100_c21","doi-asserted-by":"publisher","first-page":"046116","DOI":"10.1103\/PhysRevE.76.046116","article-title":"Collective behavior of stock price movements in an emerging market","volume":"76","year":"2007","journal-title":"Phys. Rev. E"},{"key":"2026080308550944100_c22","doi-asserted-by":"publisher","first-page":"021006","DOI":"10.1103\/PhysRevX.5.021006","article-title":"Community detection for correlation matrices","volume":"5","year":"2015","journal-title":"Phys. Rev. X"},{"key":"2026080308550944100_c23","doi-asserted-by":"publisher","first-page":"5321","DOI":"10.1038\/srep05321","article-title":"Structure of local interactions in complex financial dynamics","volume":"4","year":"2014","journal-title":"Sci. Rep."},{"key":"2026080308550944100_c24"},{"key":"2026080308550944100_c25","doi-asserted-by":"publisher","first-page":"052801","DOI":"10.1103\/PhysRevE.89.052801","article-title":"Networks in financial markets based on the mutual information rate","volume":"89","year":"2014","journal-title":"Phys. Rev. E"},{"key":"2026080308550944100_c26","doi-asserted-by":"publisher","first-page":"e0221910","DOI":"10.1371\/journal.pone.0221910","article-title":"Mutual information based stock networks and portfolio selection for intraday traders using high frequency data: An Indian market case study","volume":"14","year":"2019","journal-title":"PLoS One"},{"key":"2026080308550944100_c27","doi-asserted-by":"publisher","first-page":"1517","DOI":"10.1007\/s00181-022-02290-w","article-title":"Complex network analysis of volatility spillovers between global financial indicators and G20 stock markets","volume":"64","year":"2023","journal-title":"Empir. Econ."},{"key":"2026080308550944100_c28","doi-asserted-by":"publisher","first-page":"221","DOI":"10.25294\/auiibfd.827498","article-title":"Frequency connectedness and network analysis in equity markets: Evidence from G-7 countries","volume":"20","year":"2020","journal-title":"Akdeniz \u0130\u0130BF Dergisi"},{"key":"2026080308550944100_c29"},{"key":"2026080308550944100_c30","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.iref.2020.04.008","article-title":"Research on the time-varying network structure evolution of the stock indices of the BRICS countries based on fluctuation correlation","volume":"69","year":"2020","journal-title":"Int. Rev. Econ. Finance"},{"key":"2026080308550944100_c31","doi-asserted-by":"publisher","first-page":"2956","DOI":"10.1016\/j.physa.2009.03.028","article-title":"A network analysis of the Chinese stock market","volume":"388","year":"2009","journal-title":"Physica A"},{"key":"2026080308550944100_c32"},{"key":"2026080308550944100_c33","doi-asserted-by":"publisher","first-page":"013118","DOI":"10.1063\/5.0176892","article-title":"High-frequency stock market order transitions during the US\u2013China trade war 2018: A discrete-time Markov chain analysis","volume":"34","year":"2024","journal-title":"Chaos"},{"key":"2026080308550944100_c34"},{"key":"2026080308550944100_c35","first-page":"184","article-title":"The validity of Capital Asset Pricing Model (CAPM) and Arbitrage Pricing Theory (APT) in predicting the return of stocks in Indonesia stock exchange","volume":"1","year":"2015","journal-title":"Am. J. Econ., Finance Manag."},{"key":"2026080308550944100_c36"},{"key":"2026080308550944100_c37","doi-asserted-by":"publisher","first-page":"419","DOI":"10.1111\/j.1751-5823.2002.tb00178.x","article-title":"On choosing and bounding probability metrics","volume":"70","year":"2002","journal-title":"Int. Stat. Rev."},{"key":"2026080308550944100_c38","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1049\/iet-smt.2015.0045","article-title":"Change detection in a distillation column using non-linear auto-regressive moving average with exogenous input model and Hellinger distance","volume":"10","year":"2016","journal-title":"IET Sci., Meas. Technol."},{"key":"2026080308550944100_c39"},{"key":"2026080308550944100_c40"},{"key":"2026080308550944100_c41","doi-asserted-by":"publisher","first-page":"903","DOI":"10.1098\/rspa.1998.0193","article-title":"The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis","volume":"454","year":"1998","journal-title":"Proc. R. Soc. Lond. Ser. A"},{"key":"2026080308550944100_c42","doi-asserted-by":"publisher","first-page":"113716","DOI":"10.1016\/j.chaos.2023.113716","article-title":"Detection and forecasting of extreme events in stock price triggered by fundamental, technical, and external factors","volume":"173","year":"2023","journal-title":"Chaos Soliton. Fract."},{"key":"2026080308550944100_c43"},{"key":"2026080308550944100_c44","doi-asserted-by":"publisher","first-page":"41379","DOI":"10.1038\/srep41379","article-title":"Topological characteristics of the Hong Kong stock market: A test-based p-threshold approach to understanding network complexity","volume":"7","year":"2017","journal-title":"Sci. Rep."},{"key":"2026080308550944100_c45","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/0304-405X(93)90023-5","article-title":"Common risk factors in the returns on stocks and bonds","volume":"33","year":"1993","journal-title":"J. Financ. Econ."},{"key":"2026080308550944100_c46","doi-asserted-by":"publisher","first-page":"e0195941","DOI":"10.1371\/journal.pone.0195941","article-title":"Development of stock correlation networks using mutual information and financial big data","volume":"13","year":"2018","journal-title":"PLoS One"},{"key":"2026080308550944100_c47","doi-asserted-by":"publisher","first-page":"110084","DOI":"10.1016\/j.chaos.2020.110084","article-title":"Renyi entropy and mutual information measurement of market expectations and investor fear during the COVID-19 pandemic","volume":"139","year":"2020","journal-title":"Chaos Soliton. Fract."},{"key":"2026080308550944100_c48","doi-asserted-by":"publisher","first-page":"066138","DOI":"10.1103\/PhysRevE.69.066138","article-title":"Estimating mutual information","volume":"69","year":"2004","journal-title":"Phys. Rev. E"},{"key":"2026080308550944100_c49","doi-asserted-by":"publisher","first-page":"1302","DOI":"10.1016\/j.patrec.2012.02.022","article-title":"Low bias histogram-based estimation of mutual information for feature selection","volume":"33","year":"2012","journal-title":"Pattern Recognit. Lett."},{"key":"2026080308550944100_c50","doi-asserted-by":"publisher","first-page":"S231","DOI":"10.1093\/bioinformatics\/18.suppl_2.S231","article-title":"The mutual information: Detecting and evaluating dependencies between variables","volume":"18","year":"2002","journal-title":"Bioinformatics"},{"key":"2026080308550944100_c51","doi-asserted-by":"publisher","first-page":"918","DOI":"10.1016\/j.compeleceng.2013.02.010","article-title":"A new histogram-based estimation technique of entropy and mutual information using mean squared error minimization","volume":"39","year":"2013","journal-title":"Comput. Electr. Eng."},{"key":"2026080308550944100_c52","volume-title":"Permutation, Parametric and Bootstrap Tests of Hypotheses","year":"2005"},{"key":"2026080308550944100_c53","doi-asserted-by":"publisher","first-page":"1105","DOI":"10.5194\/isprsarchives-XL-8-1105-2014","article-title":"Development of GIS tool for the solution of minimum spanning tree problem using prim\u2019s algorithm","volume":"XL-8","year":"2014","journal-title":"Int. Arch. Photogramm., Remote Sens. Spat. Inf. Sci."},{"key":"2026080308550944100_c54","doi-asserted-by":"publisher","first-page":"0263","DOI":"10.30598\/barekengvol17iss1pp0263-0274","article-title":"Modified weight matrix using prim\u2019s algorithm in minimum spanning tree (MST) approach for GSTAR (1;1) model","volume":"17","year":"2023","journal-title":"BAREKENG: J. Ilmu Mat. Terapan"},{"key":"2026080308550944100_c55","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1016\/0378-8733(79)90002-9","article-title":"Centrality in social networks: II. Experimental results","volume":"2","year":"1979","journal-title":"Soc. Netw."},{"key":"2026080308550944100_c56","volume-title":"Scale-Free Networks: Complex Webs in Nature and Technology","year":"2007"},{"key":"2026080308550944100_c57","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1016\/0378-8733(91)90018-O","article-title":"Simultaneous group and individual centralities","volume":"13","year":"1991","journal-title":"Soc. Netw."},{"key":"2026080308550944100_c58","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1103\/RevModPhys.74.47","article-title":"Statistical mechanics of complex networks","volume":"74","year":"2002","journal-title":"Rev. Mod. Phys."},{"key":"2026080308550944100_c59","doi-asserted-by":"publisher","first-page":"198701","DOI":"10.1103\/PhysRevLett.87.198701","article-title":"Efficient behavior of small-world networks","volume":"87","year":"2001","journal-title":"Phys. Rev. Lett."},{"key":"2026080308550944100_c60","doi-asserted-by":"publisher","first-page":"026126","DOI":"10.1103\/PhysRevE.67.026126","article-title":"Mixing patterns in networks","volume":"67","year":"2003","journal-title":"Phys. Rev. E"},{"key":"2026080308550944100_c61","doi-asserted-by":"publisher","first-page":"298","DOI":"10.21136\/CMJ.1973.101168","article-title":"Algebraic connectivity of graphs","volume":"23","year":"1973","journal-title":"Czechoslovak Math. J."},{"key":"2026080308550944100_c62","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1016\/S0378-8733(99)00019-2","article-title":"Models of core\/periphery structures","volume":"21","year":"2000","journal-title":"Soc. Netw."},{"key":"2026080308550944100_c63","doi-asserted-by":"publisher","first-page":"034311","DOI":"10.1103\/s9cx-cftv","article-title":"Uncovering the hidden core-periphery structure in hyperbolic networks","volume":"112","year":"2025","journal-title":"Phys. Rev. E"},{"key":"2026080308550944100_c64","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1785\/BSSA0320030163","article-title":"Earthquake magnitude, intensity, energy, and acceleration","volume":"32","year":"1942","journal-title":"Bull. Seismol. Soc. Am."},{"key":"2026080308550944100_c65","doi-asserted-by":"publisher","first-page":"1315","DOI":"10.1016\/j.physa.2011.08.068","article-title":"Stock market dynamics: Before and after stock market crashes","volume":"391","year":"2012","journal-title":"Physica A"},{"key":"2026080308550944100_c66","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1016\/j.physa.2003.10.060","article-title":"Financial earthquakes, aftershocks and scaling in emerging stock markets","volume":"333","year":"2004","journal-title":"Physica A"},{"key":"2026080308550944100_c67"},{"key":"2026080308550944100_c68","doi-asserted-by":"publisher","first-page":"053115","DOI":"10.1063\/5.0046704","article-title":"Characteristics of 2020 stock market crash: The COVID-19 induced extreme event","volume":"31","year":"2021","journal-title":"Chaos"},{"key":"2026080308550944100_c69","doi-asserted-by":"publisher","first-page":"113125","DOI":"10.1063\/5.0184267","article-title":"Linear and nonlinear causality in financial markets","volume":"34","year":"2024","journal-title":"Chaos"},{"key":"2026080308550944100_c70","doi-asserted-by":"publisher","first-page":"383\u2013397","DOI":"10.47974\/JSMS-1550","volume":"29","year":"2026","journal-title":"J. Stat. Manage. Syst."},{"key":"2026080308550944100_c71","doi-asserted-by":"publisher","first-page":"131","DOI":"10.15388\/omee.2021.12.51","article-title":"Indian stock market during the COVID-19 pandemic: Vulnerable or resilient? Sectoral analysis","volume":"12","year":"2021","journal-title":"Organ. Mark. Emerg. Econ."},{"key":"2026080308550944100_c72","doi-asserted-by":"publisher","first-page":"112138","DOI":"10.1016\/j.chaos.2022.112138","article-title":"Empirical study and model simulation of global stock market dynamics during COVID-19","volume":"159","year":"2022","journal-title":"Chaos Soliton. Fract."},{"key":"2026080308550944100_c73","doi-asserted-by":"publisher","first-page":"066111","DOI":"10.1103\/PhysRevE.70.066111","article-title":"Finding community structure in very large networks","volume":"70","year":"2004","journal-title":"Phys. Rev. E"}],"container-title":["Chaos: An Interdisciplinary Journal of Nonlinear Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/pubs.aip.org\/aip\/cha\/article-pdf\/doi\/10.1063\/5.0320572\/21092859\/083101_1_5.0320572.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/pubs.aip.org\/aip\/cha\/article-pdf\/doi\/10.1063\/5.0320572\/21092859\/083101_1_5.0320572.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T12:55:16Z","timestamp":1785761716000},"score":1,"resource":{"primary":{"URL":"https:\/\/pubs.aip.org\/cha\/article\/36\/8\/083101\/3400119\/Core-periphery-dynamics-in-market-conditioned"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,1]]},"references-count":73,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2026,8,1]]}},"URL":"https:\/\/doi.org\/10.1063\/5.0320572","relation":{},"ISSN":["1054-1500","1089-7682"],"issn-type":[{"value":"1054-1500","type":"print"},{"value":"1089-7682","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2026,8]]},"published":{"date-parts":[[2026,8,1]]},"article-number":"083101"}}