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Dynamics of network structure in cryptocurrency markets during abrupt changes in Bitcoin price

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  • Jaroonchokanan, Nawee
  • Sinha, Amit
  • Suwanna, Sujin

Abstract

Network modeling is a powerful approach to study agent interactions that can provide insights into dynamics and behaviors of complex systems. Cryptocurrency market stability can be assessed by analyzing changes in network structures, where cryptocurrencies serve as nodes in the network, and the network’s weights of connectivity represent the strength of their relationships. This study examines the roles of weighting methods — correlation, mutual information, and Fisher information distance (FID) — in constructing cryptocurrency networks, and how they perform when abrupt changes occur in the Bitcoin price. Each weighting method offers unique insights into cryptocurrency relationships. Results show that sudden Bitcoin price shifts impact the cryptocurrency network’s structures, including characteristic path length, hubs, and minimum spanning trees, providing insights into market stability and clustering behaviors. Additionally, the Granger causality test reveals that the Bitcoin’s returns drive the cryptocurrency network’s connectivity and structure changes. However, the converse is not true, suggesting that the collective behaviors of cryptocurrencies are strongly influenced by the BTC price movement, but not vice versa. The study highlights weighting methods as valuable tools for network analysis and portfolio management using minimum spanning trees.

Suggested Citation

  • Jaroonchokanan, Nawee & Sinha, Amit & Suwanna, Sujin, 2025. "Dynamics of network structure in cryptocurrency markets during abrupt changes in Bitcoin price," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 661(C).
    • Handle: RePEc:eee:phsmap:v:661:y:2025:i:c:s0378437125000561
    DOI: 10.1016/j.physa.2025.130404
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    as
    1. Zhang, Dayong & Hu, Min & Ji, Qiang, 2020. "Financial markets under the global pandemic of COVID-19," Finance Research Letters, Elsevier, vol. 36(C).
    2. Jae Woo Lee & Ashadun Nobi, 2018. "State and Network Structures of Stock Markets Around the Global Financial Crisis," Computational Economics, Springer;Society for Computational Economics, vol. 51(2), pages 195-210, February.
    3. 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.
    4. Antonakakis, Nikolaos & Chatziantoniou, Ioannis & Gabauer, David, 2019. "Cryptocurrency market contagion: Market uncertainty, market complexity, and dynamic portfolios," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 61(C), pages 37-51.
    5. Jong-Min Kim & Chanho Cho & Chulhee Jun, 2022. "Forecasting the Price of the Cryptocurrency Using Linear and Nonlinear Error Correction Model," JRFM, MDPI, vol. 15(2), pages 1-10, February.
    6. Chen, Wei & Qu, Shuai & Jiang, Manrui & Jiang, Cheng, 2021. "The construction of multilayer stock network model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    7. Stosic, Darko & Stosic, Dusan & Ludermir, Teresa B. & Stosic, Tatijana, 2018. "Collective behavior of cryptocurrency price changes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 507(C), pages 499-509.
    8. Matkovskyy, Roman & Jalan, Akanksha, 2019. "From financial markets to Bitcoin markets: A fresh look at the contagion effect," Finance Research Letters, Elsevier, vol. 31(C), pages 93-97.
    9. Song, Jung Yoon & Chang, Woojin & Song, Jae Wook, 2019. "Cluster analysis on the structure of the cryptocurrency market via Bitcoin–Ethereum filtering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 527(C).
    10. Ozili, Peterson K, 2022. "Decentralized finance research and developments around the World," MPRA Paper 114338, University Library of Munich, Germany.
    11. Jiaqi Liang & Linjing Li & Daniel Zeng, 2018. "Evolutionary dynamics of cryptocurrency transaction networks: An empirical study," Papers 1808.08585, arXiv.org.
    12. Bouri, Elie & Roubaud, David & Shahzad, Syed Jawad Hussain, 2020. "Do Bitcoin and other cryptocurrencies jump together?," The Quarterly Review of Economics and Finance, Elsevier, vol. 76(C), pages 396-409.
    13. Stosic, Darko & Stosic, Dusan & Ludermir, Teresa B. & Stosic, Tatijana, 2019. "Exploring disorder and complexity in the cryptocurrency space," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 548-556.
    14. Galati, Luca & Webb, Alexander & Webb, Robert I., 2024. "Financial contagion in cryptocurrency exchanges: Evidence from the FTT collapse," Finance Research Letters, Elsevier, vol. 67(PA).
    15. Oleg Shirokikh & Grigory Pastukhov & Vladimir Boginski & Sergiy Butenko, 2013. "Computational study of the US stock market evolution: a rank correlation-based network model," Computational Management Science, Springer, vol. 10(2), pages 81-103, June.
    16. Hiemstra, Craig & Jones, Jonathan D, 1994. "Testing for Linear and Nonlinear Granger Causality in the Stock Price-Volume Relation," Journal of Finance, American Finance Association, vol. 49(5), pages 1639-1664, December.
    17. Jae Woo Lee & Ashadun Nobi, 2018. "State and Network Structures of Stock Markets around the Global Financial Crisis," Papers 1806.04363, arXiv.org.
    18. Mathieu Jacomy & Tommaso Venturini & Sebastien Heymann & Mathieu Bastian, 2014. "ForceAtlas2, a Continuous Graph Layout Algorithm for Handy Network Visualization Designed for the Gephi Software," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-12, June.
    19. Smales, L.A., 2022. "Investor attention in cryptocurrency markets," International Review of Financial Analysis, Elsevier, vol. 79(C).
    20. Jiang, Zhi-Qiang & Zhou, Wei-Xing, 2010. "Complex stock trading network among investors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(21), pages 4929-4941.
    21. Vidal-Tomás, David, 2021. "Transitions in the cryptocurrency market during the COVID-19 pandemic: A network analysis," Finance Research Letters, Elsevier, vol. 43(C).
    22. A. Q. Barbi & G. A. Prataviera, 2017. "Nonlinear dependencies on Brazilian equity network from mutual information minimum spanning trees," Papers 1711.06185, arXiv.org, revised May 2019.
    23. Dionisio, Andreia & Menezes, Rui & Mendes, Diana A., 2004. "Mutual information: a measure of dependency for nonlinear time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 344(1), pages 326-329.
    24. Bentian Li & Dechang Pi, 2018. "Analysis of global stock index data during crisis period via complex network approach," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-16, July.
    25. Jiaqi Liang & Linjing Li & Daniel Zeng, 2018. "Evolutionary dynamics of cryptocurrency transaction networks: An empirical study," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-18, August.
    26. Xiao-Qian Sun & Xue-Qi Cheng & Hua-Wei Shen & Zhao-Yang Wang, 2011. "Distinguishing manipulated stocks via trading network analysis," Papers 1110.2260, arXiv.org.
    27. Mi Yeon Hong & Ji Won Yoon, 2022. "The impact of COVID-19 on cryptocurrency markets: A network analysis based on mutual information," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-24, February.
    28. D'aniel Kondor & M'arton P'osfai & Istv'an Csabai & G'abor Vattay, 2013. "Do the rich get richer? An empirical analysis of the BitCoin transaction network," Papers 1308.3892, arXiv.org, revised Mar 2014.
    29. Panpan Zhu & Xing Zhang & You Wu & Hao Zheng & Yinpeng Zhang, 2021. "Investor attention and cryptocurrency: Evidence from the Bitcoin market," PLOS ONE, Public Library of Science, vol. 16(2), pages 1-28, February.
    30. Sun, Xiao-Qian & Cheng, Xue-Qi & Shen, Hua-Wei & Wang, Zhao-Yang, 2011. "Distinguishing manipulated stocks via trading network analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(20), pages 3427-3434.
    31. Younes Berouaga & Cherif El Msiyah & Jaouad Madkour, 2023. "Portfolio Optimization Using Minimum Spanning Tree Model in the Moroccan Stock Exchange Market," IJFS, MDPI, vol. 11(2), pages 1-20, March.
    32. Khashanah, Khaldoun & Yang, Hanchao, 2016. "Evolutionary systemic risk: Fisher information flow metric in financial network dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 445(C), pages 318-327.
    33. Ashadun Nobi & Sungmin Lee & Doo Hwan Kim & Jae Woo Lee, 2014. "Correlation and Network Topologies in Global and Local Stock Indices," Papers 1402.1552, arXiv.org.
    34. Assaf, Ata & Charif, Husni & Demir, Ender, 2022. "Information sharing among cryptocurrencies: Evidence from mutual information and approximate entropy during COVID-19," Finance Research Letters, Elsevier, vol. 47(PA).
    35. David Hartman & Jaroslav Hlinka, 2018. "Nonlinearity in stock networks," Papers 1804.10264, arXiv.org, revised Jun 2018.
    36. López-Martín, Carmen & Arguedas-Sanz, Raquel & Muela, Sonia Benito, 2022. "A cryptocurrency empirical study focused on evaluating their distribution functions," International Review of Economics & Finance, Elsevier, vol. 79(C), pages 387-407.
    37. Cees Diks & Marcin Wolski, 2016. "Nonlinear Granger Causality: Guidelines for Multivariate Analysis," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1333-1351, November.
    38. 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.
    39. Barbi, A.Q. & Prataviera, G.A., 2019. "Nonlinear dependencies on Brazilian equity network from mutual information minimum spanning trees," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 876-885.
    40. Agosto, Arianna & Cerchiello, Paola & Pagnottoni, Paolo, 2022. "Sentiment, Google queries and explosivity in the cryptocurrency market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 605(C).
    41. Marco Bardoscia & Paolo Barucca & Stefano Battiston & Fabio Caccioli & Giulio Cimini & Diego Garlaschelli & Fabio Saracco & Tiziano Squartini & Guido Caldarelli, 2021. "The Physics of Financial Networks," Papers 2103.05623, arXiv.org.
    42. Dániel Kondor & Márton Pósfai & István Csabai & Gábor Vattay, 2014. "Do the Rich Get Richer? An Empirical Analysis of the Bitcoin Transaction Network," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-10, February.
    43. Jaros{l}aw Kwapie'n & Marcin Wk{a}torek & Marija Bezbradica & Martin Crane & Tai Tan Mai & Stanis{l}aw Dro.zd.z, 2022. "Analysis of inter-transaction time fluctuations in the cryptocurrency market," Papers 2206.07831, arXiv.org, revised Sep 2022.
    44. Baumgartner, Tim & Güttler, André, 2022. "Bitcoin flash crash on May 19, 2021: What did really happen on Binance?," IWH Discussion Papers 25/2022, Halle Institute for Economic Research (IWH).
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