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On the evolution of cryptocurrency market efficiency

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  • Akihiko Noda

Abstract

This study examines whether the efficiency of cryptocurrency markets (Bitcoin and Ethereum) evolve over time based on the adaptive market hypothesis (AMH). In particular, we measure the degree of market efficiency using a generalized least squares-based time-varying model that does not depend on sample size, unlike previous studies that used conventional methods. The empirical results show that (1) the degree of market efficiency varies with time in the markets, (2) Bitcoin’s market efficiency level is higher than that of Ethereum over most periods, and (3) a market with high market liquidity has been evolving. We conclude that the results support the AMH for the most established cryptocurrency market.

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  • Akihiko Noda, 2021. "On the evolution of cryptocurrency market efficiency," Applied Economics Letters, Taylor & Francis Journals, vol. 28(6), pages 433-439, March.
    • Handle: RePEc:taf:apeclt:v:28:y:2021:i:6:p:433-439
    DOI: 10.1080/13504851.2020.1758617
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    2. Ata Assaf & Luis Alberiko Gil-Alana & Khaled Mokni, 2022. "True or spurious long memory in the cryptocurrency markets: evidence from a multivariate test and other Whittle estimation methods," Empirical Economics, Springer, vol. 63(3), pages 1543-1570, September.
    3. Ayoub Ammy-Driss & Matthieu Garcin, 2021. "Efficiency of the financial markets during the COVID-19 crisis: time-varying parameters of fractional stable dynamics," Working Papers hal-02903655, HAL.
    4. Virginie Terraza & Aslı Boru İpek & Mohammad Mahdi Rounaghi, 2024. "The nexus between the volatility of Bitcoin, gold, and American stock markets during the COVID-19 pandemic: evidence from VAR-DCC-EGARCH and ANN models," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-34, December.
    5. Ayoub Ammy-Driss & Matthieu Garcin, 2020. "Efficiency of the financial markets during the COVID-19 crisis: time-varying parameters of fractional stable dynamics," Papers 2007.10727, arXiv.org, revised Nov 2021.
    6. Fernandes, Leonardo H.S. & Bouri, Elie & Silva, José W.L. & Bejan, Lucian & de Araujo, Fernando H.A., 2022. "The resilience of cryptocurrency market efficiency to COVID-19 shock," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    7. Shanaev, Savva & Ghimire, Binam, 2022. "A generalised seasonality test and applications for cryptocurrency and stock market seasonality," The Quarterly Review of Economics and Finance, Elsevier, vol. 86(C), pages 172-185.
    8. Carmen López-Martín & Sonia Benito Muela & Raquel Arguedas, 2021. "Efficiency in cryptocurrency markets: new evidence," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 11(3), pages 403-431, September.
    9. Qadan, Mahmoud & Aharon, David Y. & Eichel, Ron, 2022. "Seasonal and Calendar Effects and the Price Efficiency of Cryptocurrencies," Finance Research Letters, Elsevier, vol. 46(PA).
    10. Florentina Șoiman & Jean-Guillaume Dumas & Sonia Jimenez-Garces, 2022. "The return of (I)DeFiX [Le rendement de (I)DeFiX]," Working Papers hal-03625891, HAL.
    11. Assaf, Ata & Mokni, Khaled & Yousaf, Imran & Bhandari, Avishek, 2023. "Long memory in the high frequency cryptocurrency markets using fractal connectivity analysis: The impact of COVID-19," Research in International Business and Finance, Elsevier, vol. 64(C).
    12. Oktay Ozkan, 2020. "Time-varying return predictability and adaptive markets hypothesis: Evidence on MIST countries from a novel wild bootstrap likelihood ratio approach," Bogazici Journal, Review of Social, Economic and Administrative Studies, Bogazici University, Department of Economics, vol. 34(2), pages 101-113.
    13. Majid Mirzaee Ghazani & Mohammad Ali Jafari, 2021. "Cryptocurrencies, gold, and WTI crude oil market efficiency: a dynamic analysis based on the adaptive market hypothesis," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-26, December.
    14. Shrestha, Keshab & Naysary, Babak & Philip, Sheena Sara Suresh, 2023. "Fintech market efficiency: A multifractal detrended fluctuation analysis," Finance Research Letters, Elsevier, vol. 54(C).
    15. Ammy-Driss, Ayoub & Garcin, Matthieu, 2023. "Efficiency of the financial markets during the COVID-19 crisis: Time-varying parameters of fractional stable dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    16. Manahov, Viktor & Urquhart, Andrew, 2021. "The efficiency of Bitcoin: A strongly typed genetic programming approach to smart electronic Bitcoin markets," International Review of Financial Analysis, Elsevier, vol. 73(C).
    17. Onur Özdemir, 2022. "Cue the volatility spillover in the cryptocurrency markets during the COVID-19 pandemic: evidence from DCC-GARCH and wavelet analysis," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-38, December.
    18. Florentina c{S}oiman & Guillaume Dumas & Sonia Jimenez-Garces, 2022. "The return of (I)DeFiX," Papers 2204.00251, arXiv.org.

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