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Core–periphery organization of the cryptocurrency market inferred by the modularity operator

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  • Polovnikov, Kirill
  • Kazakov, Vlad
  • Syntulsky, Sergey

Abstract

Modularity matrix has long been used for inferring modular structure of stochastic networks of different scale-free nature. In this paper we show efficiency of the modularity to detect the core–periphery organization on the example of the cryptocurrency correlation-based network. The cryptocurrencies exemplify assets with dual macroeconomical background sharing properties of currency and stock markets with a non-obvious topological organization. We demonstrate that the modularity operator applied to a daily correlation-based network rules out community structure of the cryptocurrency market, simultaneously revealing stratification into a core and a periphery. Classification of tokens into two groups is shown to be day-dependent, however, stable tokens with statistically significant participation ratio can be easily identified. To approve the core–periphery organization of the stable assets, we compute the centrality measure of the two groups and show that it is considerably less for the periphery than for the core. Embedding of a subgraph of the stable tokens into the Euclidean space demonstrates clear spatial core–shell segregation. Furthermore, we show that the degree distribution of the minimal spanning tree has a distinctive power-law tail with exponent γ≈−2.6 which makes the cryptomarket an archetypal example of the scale-free network. Economical reasoning suggests that the revealed topological motif is in the full agreement with the outliers hypothesis. The core is driven by traditionally liquid and highly capitalized tokens, resembling blockchain and payment systems, while the periphery is marked by the stable tokens with little exposure to the market. We report that the very center of the core is populated by tokens with strong financial usage, while main drivers of the market (such as ETH or XRP) turn out to locate in the middle layers. This is an clear evidence of speculative processes underlying formation and evolution of the market.

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  • Polovnikov, Kirill & Kazakov, Vlad & Syntulsky, Sergey, 2020. "Core–periphery organization of the cryptocurrency market inferred by the modularity operator," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
  • Handle: RePEc:eee:phsmap:v:540:y:2020:i:c:s0378437119317364
    DOI: 10.1016/j.physa.2019.123075
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    as
    1. Wiliński, M. & Sienkiewicz, A. & Gubiec, T. & Kutner, R. & Struzik, Z.R., 2013. "Structural and topological phase transitions on the German Stock Exchange," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(23), pages 5963-5973.
    2. S. Redner, 1998. "How popular is your paper? An empirical study of the citation distribution," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 4(2), pages 131-134, July.
    3. Mantegna,Rosario N. & Stanley,H. Eugene, 2007. "Introduction to Econophysics," Cambridge Books, Cambridge University Press, number 9780521039871, September.
    4. 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.
    5. Sebastien Valeyre & Denis S Grebenkov & Sofiane Aboura, 2019. "Emergence of correlations between securities at short time scales," Post-Print hal-02343888, HAL.
    6. Piccardi, Carlo & Calatroni, Lisa & Bertoni, Fabio, 2010. "Communities in Italian corporate networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(22), pages 5247-5258.
    7. Réka Albert & Hawoong Jeong & Albert-László Barabási, 1999. "Diameter of the World-Wide Web," Nature, Nature, vol. 401(6749), pages 130-131, September.
    8. Laurent Laloux & Pierre Cizeau & Marc Potters & Jean-Philippe Bouchaud, 2000. "Random Matrix Theory And Financial Correlations," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 3(03), pages 391-397.
    9. Valeyre, Sebastien & Grebenkov, Denis S. & Aboura, Sofiane, 2019. "Emergence of correlations between securities at short time scales," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 526(C).
    10. Jaroslaw Kwapien & Sylwia Gworek & Stanislaw Drozdz & Andrzej Gorski, 2009. "Analysis of a network structure of the foreign currency exchange market," Papers 0906.0480, arXiv.org.
    11. Stanis{l}aw Dro.zd.z & Robert Gk{e}barowski & Ludovico Minati & Pawe{l} O'swik{e}cimka & Marcin Wk{a}torek, 2018. "Bitcoin market route to maturity? Evidence from return fluctuations, temporal correlations and multiscaling effects," Papers 1804.05916, arXiv.org, revised Jul 2018.
    12. Jarosław Kwapień & Sylwia Gworek & Stanisław Drożdż & Andrzej Górski, 2009. "Analysis of a network structure of the foreign currency exchange market," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 4(1), pages 55-72, June.
    13. M. Wili'nski & A. Sienkiewicz & T. Gubiec & R. Kutner & Z. R. Struzik, 2013. "Structural and topological phase transitions on the German Stock Exchange," Papers 1301.2530, arXiv.org, revised Jul 2013.
    14. Drożdż, S & Grümmer, F & Górski, A.Z & Ruf, F & Speth, J, 2000. "Dynamics of competition between collectivity and noise in the stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 287(3), pages 440-449.
    15. Stanis{l}aw Dro.zd.z & Ludovico Minati & Pawe{l} O'swik{e}cimka & Marek Stanuszek & Marcin Wk{a}torek, 2019. "Signatures of crypto-currency market decoupling from the Forex," Papers 1906.07834, arXiv.org, revised Jul 2019.
    16. Galazka, Marek, 2011. "Characteristics of the Polish Stock Market correlations," International Review of Financial Analysis, Elsevier, vol. 20(1), pages 1-5, January.
    17. Raffaele Corrado & Maurizio Zollo, 2006. "Small worlds evolving: governance reforms, privatizations, and ownership networks in Italy," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 15(2), pages 319-352, April.
    18. Dong-Ming Song & Michele Tumminello & Wei-Xing Zhou & Rosario N. Mantegna, 2011. "Evolution of worldwide stock markets, correlation structure and correlation based graphs," Papers 1103.5555, arXiv.org.
    19. H. Jeong & B. Tombor & R. Albert & Z. N. Oltvai & A.-L. Barabási, 2000. "The large-scale organization of metabolic networks," Nature, Nature, vol. 407(6804), pages 651-654, October.
    20. Jacopo Grilli & Tim Rogers & Stefano Allesina, 2016. "Modularity and stability in ecological communities," Nature Communications, Nature, vol. 7(1), pages 1-10, November.
    21. N. Vandewalle & F. Brisbois & X. Tordoir, 2001. "Non-random topology of stock markets," Quantitative Finance, Taylor & Francis Journals, vol. 1(3), pages 372-374, March.
    22. Andrea Lancichinetti & Filippo Radicchi & José J Ramasco & Santo Fortunato, 2011. "Finding Statistically Significant Communities in Networks," PLOS ONE, Public Library of Science, vol. 6(4), pages 1-18, April.
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    Cited by:

    1. Jaros{l}aw Kwapie'n & Marcin Wk{a}torek & Stanis{l}aw Dro.zd.z, 2021. "Cryptocurrency Market Consolidation in 2020--2021," Papers 2112.06552, arXiv.org.
    2. Stanis{l}aw Dro.zd.z & Jaros{l}aw Kwapie'n & Pawe{l} O'swik{e}cimka & Tomasz Stanisz & Marcin Wk{a}torek, 2020. "Complexity in economic and social systems: cryptocurrency market at around COVID-19," Papers 2009.10030, arXiv.org.
    3. Marcin Wk{a}torek & Stanis{l}aw Dro.zd.z & Jaros{l}aw Kwapie'n & Ludovico Minati & Pawe{l} O'swik{e}cimka & Marek Stanuszek, 2020. "Multiscale characteristics of the emerging global cryptocurrency market," Papers 2010.15403, arXiv.org, revised Mar 2021.
    4. Ziqiao Ao & Lin William Cong & Gergely Horvath & Luyao Zhang, 2022. "Is decentralized finance actually decentralized? A social network analysis of the Aave protocol on the Ethereum blockchain," Papers 2206.08401, arXiv.org, revised Nov 2023.

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