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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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    1. B. Tóth & F. Lillo & J. D. Farmer, 2010. "Segmentation algorithm for non-stationary compound Poisson processes," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 78(2), pages 235-243, November.
    2. Nataliya Bershova & Dmitry Rakhlin, 2013. "The non-linear market impact of large trades: evidence from buy-side order flow," Quantitative Finance, Taylor & Francis Journals, vol. 13(11), pages 1759-1778, November.
    3. J. Doyne Farmer & Austin Gerig & Fabrizio Lillo & Henri Waelbroeck, 2013. "How efficiency shapes market impact," Quantitative Finance, Taylor & Francis Journals, vol. 13(11), pages 1743-1758, November.
    4. Aur'elien Alfonsi & Alexander Schied, 2012. "Capacitary measures for completely monotone kernels via singular control," Papers 1201.2756, arXiv.org, revised Feb 2013.
    5. Esteban Moro & Javier Vicente & Luis G. Moyano & Austin Gerig & J. Doyne Farmer & Gabriella Vaglica & Fabrizio Lillo & Rosario N. Mantegna, 2009. "Market impact and trading profile of large trading orders in stock markets," Papers 0908.0202, arXiv.org.
    6. Aurélien Alfonsi & Alexander Schied, 2013. "Capacitary measures for completely monotone kernels via singular control," Post-Print hal-00659421, HAL.
    7. Iacopo Mastromatteo & Bence Toth & Jean-Philippe Bouchaud, 2013. "Agent-based models for latent liquidity and concave price impact," Papers 1311.6262, arXiv.org, revised Dec 2014.
    8. Jonathan Donier & Julius Bonart & Iacopo Mastromatteo & Jean-Philippe Bouchaud, 2014. "A fully consistent, minimal model for non-linear market impact," Papers 1412.0141, arXiv.org, revised Mar 2015.
    9. Elia Zarinelli & Michele Treccani & J. Doyne Farmer & Fabrizio Lillo, 2014. "Beyond the square root: Evidence for logarithmic dependence of market impact on size and participation rate," Papers 1412.2152, arXiv.org.
    10. Jim Gatheral, 2010. "No-dynamic-arbitrage and market impact," Quantitative Finance, Taylor & Francis Journals, vol. 10(7), pages 749-759.
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    Citations

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    Cited by:

    1. Matthias Schnaubelt & Jonas Rende & Christopher Krauss, 2019. "Testing Stylized Facts of Bitcoin Limit Order Books," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 12(1), pages 1-30, February.
    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. Eduard Silantyev, 2019. "Order flow analysis of cryptocurrency markets," Digital Finance, Springer, vol. 1(1), pages 191-218, November.
    4. 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.
    5. M. Derksen & B. Kleijn & R. de Vilder, 2019. "Clearing price distributions in call auctions," Papers 1904.07583, arXiv.org, revised Nov 2019.
    6. Fr'ed'eric Bucci & Iacopo Mastromatteo & Michael Benzaquen & Jean-Philippe Bouchaud, 2019. "Impact is not just volatility," Papers 1905.04569, arXiv.org.
    7. Lahmiri, Salim & Bekiros, Stelios, 2019. "Cryptocurrency forecasting with deep learning chaotic neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 118(C), pages 35-40.
    8. Frédéric Bucci & Iacopo Mastromatteo & Michael Benzaquen & Jean-Philippe Bouchaud, 2019. "Impact is not just volatility," Post-Print hal-02323182, HAL.
    9. 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 Feb 2019.
    10. 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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