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Volatility persistence in cryptocurrency markets under structural breaks

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  • Abakah, Emmanuel Joel Aikins
  • Gil-Alana, Luis Alberiko
  • Madigu, Godfrey
  • Romero-Rojo, Fatima

Abstract

This paper deals with the analysis of volatility persistence in 12 main cryptocurrencies (Bitcoin, Bitshare, Bytecoin, Dash, Ether, Litecoin, Monero, Nem, Ripple, Siacoin, Stellar and Tether) taking into account the possibility of structural breaks. Using fractional integration methods, the results indicate that both absolute and squared returns display long memory features, with orders of integration confirming the long memory hypothesis. However, after accounting for structural breaks, we find a reduction in the degree of persistence in the cryptocurrency market. The evidence of persistence in volatility imply that market participants who want to make gains across trading scales need to factor the persistence properties of cryptocurrencies in their valuation and forecasting models since that will help improve long-term volatility market forecasts and optimal hedging decisions.

Suggested Citation

  • Abakah, Emmanuel Joel Aikins & Gil-Alana, Luis Alberiko & Madigu, Godfrey & Romero-Rojo, Fatima, 2020. "Volatility persistence in cryptocurrency markets under structural breaks," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 680-691.
    • Handle: RePEc:eee:reveco:v:69:y:2020:i:c:p:680-691
    DOI: 10.1016/j.iref.2020.06.035
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    17. Guglielmo Maria Caporale & José Javier de Dios Mazariegos & Luis A. Gil-Alana, 2024. "Long-Run Linkages Between us Stock Prices and Cryptocurrencies: A Fractional Cointegration Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 64(6), pages 3543-3553, December.
    18. Kejia Yan & Huqin Yan & Rakesh Gupta, 2022. "Are GARCH and DCC Values of 10 Cryptocurrencies Affected by COVID-19?," JRFM, MDPI, vol. 15(3), pages 1-25, March.
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    20. Alessio Brini & Jimmie Lenz, 2024. "A Comparison of Cryptocurrency Volatility-benchmarking New and Mature Asset Classes," Papers 2404.04962, arXiv.org.
    21. Hemendra Gupta & Rashmi Chaudhary, 2022. "An Empirical Study of Volatility in Cryptocurrency Market," JRFM, MDPI, vol. 15(11), pages 1-14, November.
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    More about this item

    Keywords

    Cryptocurrencies; Volatility; Long memory; Fractional integration;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G20 - Financial Economics - - Financial Institutions and Services - - - General

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