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COVID-19, cryptocurrencies bubbles and digital market efficiency: sensitivity and similarity analysis

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  • Montasser, Ghassen El
  • Charfeddine, Lanouar
  • Benhamed, Adel

Abstract

This paper compares the degree of cryptocurrency market efficiency during the pre- and post COVID-19 pandemic with the bubble and non-bubble periods of cryptocurrency markets. Furthermore, it examines and clusters eighteen cryptocurrencies by exploring their market efficiency similarity. Comparing the cryptocurrency bubble periods with the COVID-19 pandemic, the results indicate that this pandemic has the highest impact on cryptocurrency market efficiency. Interestingly, using the dynamic time warping clustering approach, we found evidence on the presence of three clusters that essentially represent mining coins, non-mining coins and token categorizations .

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  • Montasser, Ghassen El & Charfeddine, Lanouar & Benhamed, Adel, 2022. "COVID-19, cryptocurrencies bubbles and digital market efficiency: sensitivity and similarity analysis," Finance Research Letters, Elsevier, vol. 46(PA).
    • Handle: RePEc:eee:finlet:v:46:y:2022:i:pa:s1544612321003652
    DOI: 10.1016/j.frl.2021.102362
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Efstathios Polyzos & Ghulame Rubbaniy & Mieszko Mazur, 2024. "Efficient Market Hypothesis on the blockchain: A social‐media‐based index for cryptocurrency efficiency," The Financial Review, Eastern Finance Association, vol. 59(3), pages 807-829, August.
    2. Bouteska, Ahmed & Abedin, Mohammad Zoynul & Hajek, Petr & Yuan, Kunpeng, 2024. "Cryptocurrency price forecasting – A comparative analysis of ensemble learning and deep learning methods," International Review of Financial Analysis, Elsevier, vol. 92(C).
    3. Zhang, Pengcheng & Xu, Kunpeng & Qi, Jiayin, 2023. "The impact of regulation on cryptocurrency market volatility in the context of the COVID-19 pandemic — evidence from China," Economic Analysis and Policy, Elsevier, vol. 80(C), pages 222-246.
    4. Shimeng Shi & Jia Zhai & Yingying Wu, 2024. "Informational inefficiency on bitcoin futures," The European Journal of Finance, Taylor & Francis Journals, vol. 30(6), pages 642-667, April.
    5. Shafiqah Azman & Dharini Pathmanathan & Aerambamoorthy Thavaneswaran, 2022. "Forecasting the Volatility of Cryptocurrencies in the Presence of COVID-19 with the State Space Model and Kalman Filter," Mathematics, MDPI, vol. 10(17), pages 1-15, September.
    6. Assaf, Ata & Demir, Ender & Ersan, Oguz, 2024. "Detecting and date-stamping bubbles in fan tokens," International Review of Economics & Finance, Elsevier, vol. 92(C), pages 98-113.

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