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Convergence in cryptocurrency prices? the role of market microstructure

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  • Apergis, Nicholas
  • Koutmos, Dimitrios
  • Payne, James E.

Abstract

Do we observe convergence between cryptocurrencies over time? This study explores this question with eight major cryptocurrencies in circulation and posits a framework to evaluate whether shifts in their market microstructures drive convergence. Three main findings emerge. First, convergence occurs between cryptocurrencies with distinct technological functions and classifications. Second, market microstructure behavior drives convergence. Third, estimated transition paths show tighter convergence for half of our sampled cryptocurrencies during the time when the Chicago Board of Exchange (CBOE) introduced bitcoin futures contracts.

Suggested Citation

  • Apergis, Nicholas & Koutmos, Dimitrios & Payne, James E., 2021. "Convergence in cryptocurrency prices? the role of market microstructure," Finance Research Letters, Elsevier, vol. 40(C).
    • Handle: RePEc:eee:finlet:v:40:y:2021:i:c:s1544612319314114
    DOI: 10.1016/j.frl.2020.101685
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    References listed on IDEAS

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    1. Sifat, Imtiaz Mohammad & Mohamad, Azhar & Mohamed Shariff, Mohammad Syazwan Bin, 2019. "Lead-Lag relationship between Bitcoin and Ethereum: Evidence from hourly and daily data," Research in International Business and Finance, Elsevier, vol. 50(C), pages 306-321.
    2. Omane-Adjepong, Maurice & Alagidede, Imhotep Paul, 2019. "Multiresolution analysis and spillovers of major cryptocurrency markets," Research in International Business and Finance, Elsevier, vol. 49(C), pages 191-206.
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    Citations

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    Cited by:

    1. Wang, Fanyi & Ma, Wanying & Mirza, Nawazish & Altuntaş, Mehmet, 2023. "Green financing, financial uncertainty, geopolitical risk, and oil prices volatility," Resources Policy, Elsevier, vol. 83(C).
    2. Gunay, Samet & Goodell, John W. & Muhammed, Shahnawaz & Kirimhan, Destan, 2023. "Frequency connectedness between FinTech, NFT and DeFi: Considering linkages to investor sentiment," International Review of Financial Analysis, Elsevier, vol. 90(C).
    3. Giannellis, Nikolaos, 2022. "Cryptocurrency market connectedness in Covid-19 days and the role of Twitter: Evidence from a smooth transition regression model," Research in International Business and Finance, Elsevier, vol. 63(C).
    4. Mohamad, Azhar & Stavroyiannis, Stavros, 2022. "Do birds of a feather flock together? Evidence from time-varying herding behaviour of bitcoin and foreign exchange majors during Covid-19," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 80(C).
    5. Jeremy Eng-Tuck Cheah & Thong Dao & Haozhe Su, 2024. "Measuring cryptocurrency moment convergence using distance analysis," Annals of Operations Research, Springer, vol. 332(1), pages 533-577, January.
    6. Wang, Qishu, 2023. "Herding behavior and the dynamics of ESG performance in the European banking industry," Finance Research Letters, Elsevier, vol. 58(PD).
    7. Apergis, Nicholas, 2023. "Realized higher-order moments spillovers across cryptocurrencies," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 85(C).

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    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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