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Exploring the Interconnectedness of Cryptocurrencies using Correlation Networks

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  • Andrew Burnie

Abstract

Correlation networks were used to detect characteristics which, although fixed over time, have an important influence on the evolution of prices over time. Potentially important features were identified using the websites and whitepapers of cryptocurrencies with the largest userbases. These were assessed using two datasets to enhance robustness: one with fourteen cryptocurrencies beginning from 9 November 2017, and a subset with nine cryptocurrencies starting 9 September 2016, both ending 6 March 2018. Separately analysing the subset of cryptocurrencies raised the number of data points from 115 to 537, and improved robustness to changes in relationships over time. Excluding USD Tether, the results showed a positive association between different cryptocurrencies that was statistically significant. Robust, strong positive associations were observed for six cryptocurrencies where one was a fork of the other; Bitcoin / Bitcoin Cash was an exception. There was evidence for the existence of a group of cryptocurrencies particularly associated with Cardano, and a separate group correlated with Ethereum. The data was not consistent with a token's functionality or creation mechanism being the dominant determinants of the evolution of prices over time but did suggest that factors other than speculation contributed to the price.

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  • Andrew Burnie, 2018. "Exploring the Interconnectedness of Cryptocurrencies using Correlation Networks," Papers 1806.06632, arXiv.org.
    • Handle: RePEc:arx:papers:1806.06632
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    File URL: http://arxiv.org/pdf/1806.06632
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    References listed on IDEAS

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    1. Young Bin Kim & Jun Gi Kim & Wook Kim & Jae Ho Im & Tae Hyeong Kim & Shin Jin Kang & Chang Hun Kim, 2016. "Predicting Fluctuations in Cryptocurrency Transactions Based on User Comments and Replies," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-17, August.
    2. Jamal Bouoiyour & Refk Selmi, 2015. "What Does Bitcoin Look Like?," Annals of Economics and Finance, Society for AEF, vol. 16(2), pages 449-492, November.
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    Cited by:

    1. Bullmann, Dirk & Klemm, Jonas & Pinna, Andrea, 2019. "In search for stability in crypto-assets: are stablecoins the solution?," Occasional Paper Series 230, European Central Bank.
    2. Danai Likitratcharoen & Pan Chudasring & Chakrin Pinmanee & Karawan Wiwattanalamphong, 2023. "The Efficiency of Value-at-Risk Models during Extreme Market Stress in Cryptocurrencies," Sustainability, MDPI, vol. 15(5), pages 1-21, March.
    3. André Meyer & Lennart Ante, 2020. "Effects of initial coin offering characteristics on cross-listing returns," Digital Finance, Springer, vol. 2(3), pages 259-283, December.
    4. Fakhfekh, Mohamed & Bejaoui, Azza & Bariviera, Aurelio F. & Jeribi, Ahmed, 2024. "Dependence structure between NFT, DeFi and cryptocurrencies in turbulent times: An Archimax copula approach," The North American Journal of Economics and Finance, Elsevier, vol. 70(C).
    5. Kajtazi, Anton & Moro, Andrea, 2019. "The role of bitcoin in well diversified portfolios: A comparative global study," International Review of Financial Analysis, Elsevier, vol. 61(C), pages 143-157.
    6. Chika Anastesia Anisiuba & Obiamaka P. Egbo & Felix C. Alio & Chuka Ifediora & Ebele C. Igwemeka & C. O. Odidi & Hillary Chijindu Ezeaku, 2021. "Analysis of Cryptocurrency Dynamics in the Emerging Market Economies: Does Reinforcement or Substitution Effect Prevail?," SAGE Open, , vol. 11(1), pages 21582440211, March.
    7. María de la O González & Francisco Jareño & Frank S. Skinner, 2020. "Nonlinear Autoregressive Distributed Lag Approach: An Application on the Connectedness between Bitcoin Returns and the Other Ten Most Relevant Cryptocurrency Returns," Mathematics, MDPI, vol. 8(5), pages 1-22, May.
    8. Bergsli, Lykke Øverland & Lind, Andrea Falk & Molnár, Peter & Polasik, Michał, 2022. "Forecasting volatility of Bitcoin," Research in International Business and Finance, Elsevier, vol. 59(C).
    9. Mayer, Fabian & Bofinger, Peter, 2023. "Cryptocurrency competition: An empirical test of Hayek's vision of private monies," W.E.P. - Würzburg Economic Papers 103, University of Würzburg, Department of Economics.
    10. Lennart Ante & André Meyer, 2021. "Cross-listings of blockchain-based tokens issued through initial coin offerings: Do liquidity and specific cryptocurrency exchanges matter?," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 44(2), pages 957-980, December.
    11. Rubaiyat Ahsan Bhuiyan & Afzol Husain & Changyong Zhang, 2023. "Diversification evidence of bitcoin and gold from wavelet analysis," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-36, December.
    12. Walid Bakry & Audil Rashid & Somar Al-Mohamad & Nasser El-Kanj, 2021. "Bitcoin and Portfolio Diversification: A Portfolio Optimization Approach," JRFM, MDPI, vol. 14(7), pages 1-24, June.

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