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Analysis of cryptocurrency connectedness based on network to transaction volume ratios

Author

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  • Hafner, Christian M.

    (Université catholique de Louvain, LIDAM/ISBA, Belgium)

  • Majeri , Sabrine

Abstract

Network to Transaction (NVT) ratio is a measure that describes the relationship between transaction volume and market capitalization, and that may serve as an indicator for the valuation of a cryptocurrency. We build a connectedness network connecting 39 cryptocurrencies based on mutual contributions to the variances of forecast errors for NVT ratios. We find that NVT connectedness is not related to market capitalization, as we have large and small cryptocurrencies by market cap that propagate large NVT shocks (e.g. Litecoin, Dogecoin, Bitcoin Cash(bch), OMG Network and Decentraland). The largest transmitter of NVT shocks is OMG Network, which receives little public attention. Cryptocurrencies relying on proof of stake as a consensus mechanism are the smallest receivers of NVT spillovers from other cryptocurrencies. These assets are also the least interconnected, which makes them attractive from a risk diversification point of view. This complements the energy efficiency of PoS compared with proof of work.

Suggested Citation

  • Hafner, Christian M. & Majeri , Sabrine, 2022. "Analysis of cryptocurrency connectedness based on network to transaction volume ratios," LIDAM Reprints ISBA 2022033, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    • Handle: RePEc:aiz:louvar:2022033
    DOI: https://doi.org/10.1007/s42521-022-00054-w
    Note: In: Digital Finance, 2022, vol. 4, p. 187-216
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    as
    1. Fry, John & Cheah, Eng-Tuck, 2016. "Negative bubbles and shocks in cryptocurrency markets," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 343-352.
    2. Wolfgang Karl Härdle & Campbell R Harvey & Raphael C G Reule, 2020. "Understanding Cryptocurrencies," Journal of Financial Econometrics, Oxford University Press, vol. 18(2), pages 181-208.
    3. Diebold, Francis X. & Yilmaz, Kamil, 2012. "Better to give than to receive: Predictive directional measurement of volatility spillovers," International Journal of Forecasting, Elsevier, vol. 28(1), pages 57-66.
    4. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    5. Francis X. Diebold & Kamil Yilmaz, 2009. "Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets," Economic Journal, Royal Economic Society, vol. 119(534), pages 158-171, January.
    6. Christian M Hafner, 2020. "Testing for Bubbles in Cryptocurrencies with Time-Varying Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 18(2), pages 233-249.
    7. Peter C. B. Phillips & Yangru Wu & Jun Yu, 2011. "EXPLOSIVE BEHAVIOR IN THE 1990s NASDAQ: WHEN DID EXUBERANCE ESCALATE ASSET VALUES?," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 52(1), pages 201-226, February.
    8. Hafner, Christian M. & Herwartz, Helmut & Maxand, Simone, 2022. "Identification of structural multivariate GARCH models," Journal of Econometrics, Elsevier, vol. 227(1), pages 212-227.
    9. Diebold, Francis X. & Yılmaz, Kamil, 2014. "On the network topology of variance decompositions: Measuring the connectedness of financial firms," Journal of Econometrics, Elsevier, vol. 182(1), pages 119-134.
    10. Nasekin, Sergey & Chen, Cathy Yi-Hsuan, 2018. "Deep learning-based cryptocurrency sentiment construction," IRTG 1792 Discussion Papers 2018-066, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    11. Wolfgang Karl Hardle & Campbell R. Harvey & Raphael C. G. Reule, 2020. "Editorial: Understanding Cryptocurrencies," Papers 2007.14702, arXiv.org.
    12. Yi, Shuyue & Xu, Zishuang & Wang, Gang-Jin, 2018. "Volatility connectedness in the cryptocurrency market: Is Bitcoin a dominant cryptocurrency?," International Review of Financial Analysis, Elsevier, vol. 60(C), pages 98-114.
    13. Simon Trimborn & Mingyang Li & Wolfgang Karl Härdle, 2020. "Investing with Cryptocurrencies—a Liquidity Constrained Investment Approach," Journal of Financial Econometrics, Oxford University Press, vol. 18(2), pages 280-306.
    14. Pesaran, H. Hashem & Shin, Yongcheol, 1998. "Generalized impulse response analysis in linear multivariate models," Economics Letters, Elsevier, vol. 58(1), pages 17-29, January.
    15. Elendner, Hermann & Trimborn, Simon & Ong, Bobby & Lee, Teik Ming, 2016. "The cross-section of crypto-currencies as financial assets: An overview," SFB 649 Discussion Papers 2016-038, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    16. Bouri, Elie & Molnár, Peter & Azzi, Georges & Roubaud, David & Hagfors, Lars Ivar, 2017. "On the hedge and safe haven properties of Bitcoin: Is it really more than a diversifier?," Finance Research Letters, Elsevier, vol. 20(C), pages 192-198.
    17. Cathy Yi-Hsuan Chen & Christian M. Hafner, 2019. "Sentiment-Induced Bubbles in the Cryptocurrency Market," JRFM, MDPI, vol. 12(2), pages 1-12, April.
    18. Corbet, Shaen & Lucey, Brian & Yarovaya, Larisa, 2018. "Datestamping the Bitcoin and Ethereum bubbles," Finance Research Letters, Elsevier, vol. 26(C), pages 81-88.
    19. Cheah, Eng-Tuck & Fry, John, 2015. "Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin," Economics Letters, Elsevier, vol. 130(C), pages 32-36.
    20. Adrian (Wai-Kong) Cheung & Eduardo Roca & Jen-Je Su, 2015. "Crypto-currency bubbles: an application of the Phillips-Shi-Yu (2013) methodology on Mt. Gox bitcoin prices," Applied Economics, Taylor & Francis Journals, vol. 47(23), pages 2348-2358, May.
    21. Frode Kjærland & Aras Khazal & Erlend A. Krogstad & Frans B. G. Nordstrøm & Are Oust, 2018. "An Analysis of Bitcoin’s Price Dynamics," JRFM, MDPI, vol. 11(4), pages 1-18, October.
    22. Corbet, Shaen & Meegan, Andrew & Larkin, Charles & Lucey, Brian & Yarovaya, Larisa, 2018. "Exploring the dynamic relationships between cryptocurrencies and other financial assets," Economics Letters, Elsevier, vol. 165(C), pages 28-34.
    23. Fahad Saleh & Wei Jiang, 2021. "Blockchain without Waste: Proof-of-Stake [Proof of Work vs Proof of Stake]," The Review of Financial Studies, Society for Financial Studies, vol. 34(3), pages 1156-1190.
    24. Koop, Gary & Pesaran, M. Hashem & Potter, Simon M., 1996. "Impulse response analysis in nonlinear multivariate models," Journal of Econometrics, Elsevier, vol. 74(1), pages 119-147, September.
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    More about this item

    Keywords

    Vector Autoregressions ; ICA ; LASSO ; Networks ; Cryptocurrencies;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • Z11 - Other Special Topics - - Cultural Economics - - - Economics of the Arts and Literature

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