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Testing Stylized Facts of Bitcoin Limit Order Books

Author

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  • Matthias Schnaubelt

    (Department of Statistics and Econometrics, University of Erlangen-Nürnberg, Lange Gasse 20, 90403 Nürnberg, Germany)

  • Jonas Rende

    (Department of Statistics and Econometrics, University of Erlangen-Nürnberg, Lange Gasse 20, 90403 Nürnberg, Germany)

  • Christopher Krauss

    (Department of Statistics and Econometrics, University of Erlangen-Nürnberg, Lange Gasse 20, 90403 Nürnberg, Germany)

Abstract

The majority of electronic markets worldwide employ limit order books, and the recently emerging exchanges for cryptocurrencies pose no exception. With this work, we empirically analyze whether commonly observed empirical properties from established limit order exchanges transfer to the cryptocurrency domain. Based on the literature, we establish a structured methodological framework to conduct analyses in a systematic and comprehensive way. We then present results from a unique and extensive limit order data set acquired from major cryptocurrency exchanges for the currency pair Bitcoin to US Dollar. We recover many observations from mature markets, such as a symmetry between the average ask and the average bid side of the order book, autocorrelation in returns on the smallest time scales only, volatility clustering and the timing of large trades. We also observe some idiosyncrasies: The distributions of trade size and limit order prices deviate from commonly observed patterns. Also, we find limit order books to be relatively shallow and liquidity costs to be relatively high when compared to established markets.

Suggested Citation

  • Matthias Schnaubelt & Jonas Rende & Christopher Krauss, 2019. "Testing Stylized Facts of Bitcoin Limit Order Books," JRFM, MDPI, vol. 12(1), pages 1-30, February.
  • Handle: RePEc:gam:jjrfmx:v:12:y:2019:i:1:p:25-:d:203633
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    References listed on IDEAS

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    Cited by:

    1. Peter Fratrič & Giovanni Sileno & Sander Klous & Tom Engers, 2022. "Manipulation of the Bitcoin market: an agent-based study," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-29, December.
    2. Shigeyuki Hamori, 2020. "Recent Advancements in Section “Financial Technology and Innovation”," JRFM, MDPI, vol. 13(12), pages 1-2, December.
    3. Alla A. Petukhina & Raphael C. G. Reule & Wolfgang Karl Härdle, 2021. "Rise of the machines? Intraday high-frequency trading patterns of cryptocurrencies," The European Journal of Finance, Taylor & Francis Journals, vol. 27(1-2), pages 8-30, January.
    4. Schnaubelt, Matthias, 2022. "Deep reinforcement learning for the optimal placement of cryptocurrency limit orders," European Journal of Operational Research, Elsevier, vol. 296(3), pages 993-1006.
    5. Lin William Cong & Xi Li & Ke Tang & Yang Yang, 2023. "Crypto Wash Trading," Management Science, INFORMS, vol. 69(11), pages 6427-6454, November.
    6. Schnaubelt, Matthias, 2020. "Deep reinforcement learning for the optimal placement of cryptocurrency limit orders," FAU Discussion Papers in Economics 05/2020, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    7. Christian M. Hafner, 2020. "Alternative Assets and Cryptocurrencies," JRFM, MDPI, vol. 13(1), pages 1-3, January.
    8. Jeon, Yoontae & Samarbakhsh, Laleh & Hewitt, Kenji, 2021. "Fragmentation in the Bitcoin market: Evidence from multiple coexisting order books," Finance Research Letters, Elsevier, vol. 39(C).

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