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A Million Metaorder Analysis of Market Impact on the Bitcoin

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  • Jonathan Donier
  • Julius Bonart

Abstract

We present a thorough empirical analysis of market impact on the Bitcoin/USD exchange market using a complete dataset that allows us to reconstruct more than one million metaorders. We empirically confirm the "square-root law'' for market impact, which holds on four decades in spite of the quasi-absence of statistical arbitrage and market marking strategies. We show that the square-root impact holds during the whole trajectory of a metaorder and not only for the final execution price. We also attempt to decompose the order flow into an "informed'' and "uninformed'' component, the latter leading to an almost complete long-term decay of impact. This study sheds light on the hypotheses and predictions of several market impact models recently proposed in the literature and promotes heterogeneous agent models as promising candidates to explain price impact on the Bitcoin market -- and, we believe, on other markets as well.

Suggested Citation

  • Jonathan Donier & Julius Bonart, 2014. "A Million Metaorder Analysis of Market Impact on the Bitcoin," Papers 1412.4503, arXiv.org, revised Sep 2015.
    • Handle: RePEc:arx:papers:1412.4503
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Louis Saddier & Matteo Marsili, 2023. "A Bayesian theory of market impact," Papers 2303.08867, arXiv.org, revised Feb 2024.
    2. Paul Jusselin & Mathieu Rosenbaum, 2020. "No‐arbitrage implies power‐law market impact and rough volatility," Mathematical Finance, Wiley Blackwell, vol. 30(4), pages 1309-1336, October.
    3. Emilio Said, 2022. "Market Impact: Empirical Evidence, Theory and Practice," Papers 2205.07385, arXiv.org.
    4. Fabrizio Lillo, 2021. "Order flow and price formation," Papers 2105.00521, arXiv.org.
    5. Emilio Said, 2022. "Market Impact: Empirical Evidence, Theory and Practice," Working Papers hal-03668669, HAL.
    6. Saggese, Pietro & Belmonte, Alessandro & Dimitri, Nicola & Facchini, Angelo & Böhme, Rainer, 2023. "Arbitrageurs in the Bitcoin ecosystem: Evidence from user-level trading patterns in the Mt. Gox exchange platform," Journal of Economic Behavior & Organization, Elsevier, vol. 213(C), pages 251-270.
    7. Christopher J. Cho & Timothy J. Norman & Manuel Nunes, 2023. "PRIME: A Price-Reverting Impact Model of a cryptocurrency Exchange," Papers 2305.07559, arXiv.org.
    8. Steven Haryanto & Athor Subroto & Maria Ulpah, 2020. "Disposition effect and herding behavior in the cryptocurrency market," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 47(1), pages 115-132, March.
    9. Fr'ed'eric Bucci & Iacopo Mastromatteo & Michael Benzaquen & Jean-Philippe Bouchaud, 2019. "Impact is not just volatility," Papers 1905.04569, arXiv.org.
    10. Emilio Said & Ahmed Bel Hadj Ayed & Damien Thillou & Jean-Jacques Rabeyrin & Frédéric Abergel, 2020. "Market Impact: A Systematic Study of the High Frequency Options Market," Post-Print hal-02014248, HAL.
    11. Frédéric Bucci & Iacopo Mastromatteo & Michael Benzaquen & Jean-Philippe Bouchaud, 2019. "Impact is not just volatility," Post-Print hal-02323182, HAL.
    12. Matthias Schnaubelt & Jonas Rende & Christopher Krauss, 2019. "Testing Stylized Facts of Bitcoin Limit Order Books," JRFM, MDPI, vol. 12(1), pages 1-30, February.
    13. Eduard Silantyev, 2019. "Order flow analysis of cryptocurrency markets," Digital Finance, Springer, vol. 1(1), pages 191-218, November.
    14. Emilio Said & Ahmed Bel Hadj Ayed & Damien Thillou & Jean-Jacques Rabeyrin & Frédéric Abergel, 2019. "Market Impact: A Systematic Study of the High Frequency Options Market," Working Papers hal-02014248, HAL.
    15. Emy Lécuyer & Victor Filipe Martins da Rocha, 2022. "Convex Asset Pricing," Working Papers hal-03916844, HAL.
    16. Wenpin Tang, 2023. "Trading and wealth evolution in the Proof of Stake protocol," Papers 2308.01803, arXiv.org, revised Aug 2023.
    17. Jean-Philippe Bouchaud, 2021. "The Inelastic Market Hypothesis: A Microstructural Interpretation," Papers 2108.00242, arXiv.org, revised Jan 2022.
    18. M. Derksen & B. Kleijn & R. de Vilder, 2019. "Clearing price distributions in call auctions," Papers 1904.07583, arXiv.org, revised Nov 2019.
    19. Zoltan Eisler & Jean-Philippe Bouchaud, 2016. "Price impact without order book: A study of the OTC credit index market," Papers 1609.04620, arXiv.org.
    20. Lahmiri, Salim & Bekiros, Stelios, 2019. "Cryptocurrency forecasting with deep learning chaotic neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 118(C), pages 35-40.
    21. Siu Hin Tang & Mathieu Rosenbaum & Chao Zhou, 2023. "Forecasting Volatility with Machine Learning and Rough Volatility: Example from the Crypto-Winter," Papers 2311.04727, arXiv.org, revised Feb 2024.
    22. Emilio Said & Ahmed Bel Hadj Ayed & Damien Thillou & Jean-Jacques Rabeyrin & Fr'ed'eric Abergel, 2019. "Market Impact: A Systematic Study of the High Frequency Options Market," Papers 1902.05418, arXiv.org, revised May 2022.
    23. Ricardo Carreño & Verónica Aguilar & Daniel Pacheco & Marco Antonio Acevedo & Wen Yu & María Elena Acevedo, 2019. "An IoT Expert System Shell in Block-Chain Technology with ELM as Inference Engine," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(01), pages 87-104, January.

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