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On the Market Efficiency and Liquidity of High-Frequency Cryptocurrencies in a Bull and Bear Market

Author

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  • Yuanyuan Zhang

    (School of Mathematics, University of Manchester, Manchester M13 9PL, UK)

  • Stephen Chan

    (Department of Mathematics and Statistics, American University of Sharjah, Sharjah, P.O. Box 26666, UAE)

  • Jeffrey Chu

    (Department of Statistics, Universidad Carlos III de Madrid, 28903 Getafe, Spain)

  • Hana Sulieman

    (Department of Mathematics and Statistics, American University of Sharjah, Sharjah, P.O. Box 26666, UAE)

Abstract

The market for cryptocurrencies has experienced extremely turbulent conditions in recent times, and we can clearly identify strong bull and bear market phenomena over the past year. In this paper, we utilise algorithms for detecting turnings points to identify both bull and bear phases in high-frequency markets for the three largest cryptocurrencies of Bitcoin, Ethereum, and Litecoin. We also examine the market efficiency and liquidity of the selected cryptocurrencies during these periods using high-frequency data. Our findings show that the hourly returns of the three cryptocurrencies during a bull market indicate market efficiency when using the detrended-fluctuation-analysis (DFA) method to analyse the Hurst exponent with a rolling window. However, when conditions turn and there is a bear-market period, we see signs of a more inefficient market. Furthermore, our results indicated differences between the cryptocurrencies in terms of their liquidity during the two market states. Moving from a bull to a bear market, Ethereum and Litecoin appear to become more illiquid, as opposed to Bitcoin, which appears to become more liquid. The motivation to study the high-frequency cryptocurrency market came from the increasing availability of higher-frequency cryptocurrency-pricing data. However, it also comes from a movement towards higher-frequency trading of cryptocurrency. In addition, the efficiency of cryptocurrency markets relates not only to whether prices are predictable and arbitrage opportunities exist, but, more widely, to topics such as testing the profitability of trading strategies and determining the maturity of cryptocurrency markets.

Suggested Citation

  • Yuanyuan Zhang & Stephen Chan & Jeffrey Chu & Hana Sulieman, 2020. "On the Market Efficiency and Liquidity of High-Frequency Cryptocurrencies in a Bull and Bear Market," JRFM, MDPI, vol. 13(1), pages 1-14, January.
    • Handle: RePEc:gam:jjrfmx:v:13:y:2020:i:1:p:8-:d:304875
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    Cited by:

    1. Andrey Shternshis & Stefano Marmi, 2023. "Price predictability at ultra-high frequency: Entropy-based randomness test," Papers 2312.16637, arXiv.org, revised Dec 2023.
    2. Ben Nouir, Jihed & Ben Haj Hamida, Hayet, 2023. "How do economic policy uncertainty and geopolitical risk drive Bitcoin volatility?," Research in International Business and Finance, Elsevier, vol. 64(C).
    3. Smales, L.A., 2022. "Investor attention in cryptocurrency markets," International Review of Financial Analysis, Elsevier, vol. 79(C).
    4. Chan, Stephen & Chu, Jeffrey & Zhang, Yuanyuan & Nadarajah, Saralees, 2022. "An extreme value analysis of the tail relationships between returns and volumes for high frequency cryptocurrencies," Research in International Business and Finance, Elsevier, vol. 59(C).
    5. Nagy, Balint Zsolt & Benedek, Botond, 2021. "Higher co-moments and adjusted Sharpe ratios for cryptocurrencies," Finance Research Letters, Elsevier, vol. 39(C).
    6. Dong, Bingbing & Jiang, Lei & Liu, Jinyu & Zhu, Yifeng, 2022. "Liquidity in the cryptocurrency market and commonalities across anomalies," International Review of Financial Analysis, Elsevier, vol. 81(C).
    7. Stephen Chan & Jeffrey Chu & Yuanyuan Zhang & Saralees Nadarajah, 2020. "Blockchain and Cryptocurrencies," JRFM, MDPI, vol. 13(10), pages 1-3, September.
    8. Saralees Nadarajah & Emmanuel Afuecheta & Stephen Chan, 2021. "Dependence between bitcoin and African currencies," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(4), pages 1203-1218, August.
    9. 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).
    10. Helder Sebastião & Pedro Godinho, 2021. "Forecasting and trading cryptocurrencies with machine learning under changing market conditions," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-30, December.
    11. 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.

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