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Return and volatility spillovers among cryptocurrencies

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  • Koutmos, Dimitrios

Abstract

This paper measures interdependencies among 18 major cryptocurrencies and shows that (i) Bitcoin is the dominant contributor of return and volatility spillovers among all the sampled cryptocurrencies; (ii) return and volatility spillovers have risen steadily over time; (iii) there are ’spikes’ in spillovers during major news events regarding cryptocurrencies. These findings suggest growing interdependence among cryptocurrencies and, by extension, a higher degree of contagion risk. It may be the case that cryptocurrencies are becoming more integrated, albeit this makes for interesting future empirical testing. In addition, the time-varying nature of spillovers reveals a certain dimension of uncertainty regarding the future of these digital currencies.

Suggested Citation

  • Koutmos, Dimitrios, 2018. "Return and volatility spillovers among cryptocurrencies," Economics Letters, Elsevier, vol. 173(C), pages 122-127.
  • Handle: RePEc:eee:ecolet:v:173:y:2018:i:c:p:122-127
    DOI: 10.1016/j.econlet.2018.10.004
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. Gkillas, Konstantinos & Katsiampa, Paraskevi, 2018. "An application of extreme value theory to cryptocurrencies," Economics Letters, Elsevier, vol. 164(C), pages 109-111.
    4. Pavel Ciaian & Miroslava Rajcaniova & d’Artis Kancs, 2016. "The economics of BitCoin price formation," Applied Economics, Taylor & Francis Journals, vol. 48(19), pages 1799-1815, April.
    5. Garman, Mark B & Klass, Michael J, 1980. "On the Estimation of Security Price Volatilities from Historical Data," The Journal of Business, University of Chicago Press, vol. 53(1), pages 67-78, January.
    6. Urquhart, Andrew, 2017. "Price clustering in Bitcoin," Economics Letters, Elsevier, vol. 159(C), pages 145-148.
    7. Cheah, Eng-Tuck & Mishra, Tapas & Parhi, Mamata & Zhang, Zhuang, 2018. "Long Memory Interdependency and Inefficiency in Bitcoin Markets," Economics Letters, Elsevier, vol. 167(C), pages 18-25.
    8. Marie Briere & Kim Oosterlinck & Ariane Szafarz, 2015. "Virtual Currency, Tangible Return: Portfolio Diversification with Bitcoins," Post-Print CEB, ULB -- Universite Libre de Bruxelles, vol. 16(6), pages 365-373.
    9. Yilmaz, Kamil, 2010. "Return and volatility spillovers among the East Asian equity markets," Journal of Asian Economics, Elsevier, vol. 21(3), pages 304-313, June.
    10. Urquhart, Andrew, 2018. "What causes the attention of Bitcoin?," Economics Letters, Elsevier, vol. 166(C), pages 40-44.
    11. Gandal, Neil & Hamrick, JT & Moore, Tyler & Oberman, Tali, 2018. "Price manipulation in the Bitcoin ecosystem," Journal of Monetary Economics, Elsevier, vol. 95(C), pages 86-96.
    12. Brauneis, Alexander & Mestel, Roland, 2018. "Price discovery of cryptocurrencies: Bitcoin and beyond," Economics Letters, Elsevier, vol. 165(C), pages 58-61.
    13. Khuntia, Sashikanta & Pattanayak, J.K., 2018. "Adaptive market hypothesis and evolving predictability of bitcoin," Economics Letters, Elsevier, vol. 167(C), pages 26-28.
    14. Parkinson, Michael, 1980. "The Extreme Value Method for Estimating the Variance of the Rate of Return," The Journal of Business, University of Chicago Press, vol. 53(1), pages 61-65, January.
    15. Katsiampa, Paraskevi, 2017. "Volatility estimation for Bitcoin: A comparison of GARCH models," Economics Letters, Elsevier, vol. 158(C), pages 3-6.
    16. Pesaran, H. Hashem & Shin, Yongcheol, 1998. "Generalized impulse response analysis in linear multivariate models," Economics Letters, Elsevier, vol. 58(1), pages 17-29, January.
    17. Koutmos, Dimitrios, 2018. "Bitcoin returns and transaction activity," Economics Letters, Elsevier, vol. 167(C), pages 81-85.
    18. Koutmos, Dimitrios, 2018. "Liquidity uncertainty and Bitcoin’s market microstructure," Economics Letters, Elsevier, vol. 172(C), pages 97-101.
    19. Stephanie Lo & J. Christina Wang, 2014. "Bitcoin as money?," Current Policy Perspectives 14-4, Federal Reserve Bank of Boston.
    20. Dyhrberg, Anne H. & Foley, Sean & Svec, Jiri, 2018. "How investible is Bitcoin? Analyzing the liquidity and transaction costs of Bitcoin markets," Economics Letters, Elsevier, vol. 171(C), pages 140-143.
    21. Wei, Wang Chun, 2018. "Liquidity and market efficiency in cryptocurrencies," Economics Letters, Elsevier, vol. 168(C), pages 21-24.
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    More about this item

    Keywords

    Bitcoin; Cryptocurrencies; Spillovers; Variance decompositions; Vector autoregression;
    All these keywords.

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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