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The market nanostructure origin of asset price time reversal asymmetry

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  • Marcus Cordi
  • Damien Challet
  • Serge Kassibrakis

Abstract

We introduce a method to infer lead-lag networks between the states of elements of complex systems, determined at different timescales. As such networks encode a causal structure of a system, inferring lead-lag networks for many pairs of timescales provides a global picture of the mutual influence between timescales. We apply our method to two trader-resolved FX data sets and document a strong and complex asymmetric influence of timescales on the structure of lead-lag networks. This asymmetry extends to the propagation of trader activity between timescales. For both retail and institutional traders, we find that historical activity over longer timescales has a greater correlation with future activity over shorter timescales (Zumbach effect), for sufficiently large timescales both in the past and future (about one hour for retail traders and two hours for institutional traders); remarkably the effect is opposite for smaller timescales, and much weaker for retail traders.

Suggested Citation

  • Marcus Cordi & Damien Challet & Serge Kassibrakis, 2021. "The market nanostructure origin of asset price time reversal asymmetry," Quantitative Finance, Taylor & Francis Journals, vol. 21(2), pages 295-304, February.
    • Handle: RePEc:taf:quantf:v:21:y:2021:i:2:p:295-304
    DOI: 10.1080/14697688.2020.1753883
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    Cited by:

    1. Ranieri Dugo & Giacomo Giorgio & Paolo Pigato, 2024. "Multivariate Rough Volatility," Papers 2412.14353, arXiv.org, revised Aug 2025.
    2. Carlo Campajola & Fabrizio Lillo & Daniele Tantari, 2019. "Unveiling the relation between herding and liquidity with trader lead-lag networks," Papers 1909.10807, arXiv.org, revised Mar 2020.
    3. Artem Stopochkin & Inessa Sytnik & Janusz Wielki & Nataliia Zemlianska, 2021. "Methodology for Building Trader's Investment Strategy Based on Assessment of the Market Value of the Company," European Research Studies Journal, European Research Studies Journal, vol. 0(1), pages 913-935.
    4. Ranieri Dugo & Giacomo Giorgio & Paolo Pigato, 2024. "The Multivariate Fractional Ornstein-Uhlenbeck Process," CEIS Research Paper 581, Tor Vergata University, CEIS, revised 28 Aug 2024.
    5. Wojciech Wisniewski & Yuri Kalnishkan & David Lindsay & Si^an Lindsay, 2024. "Temporal distribution of clusters of investors and their application in prediction with expert advice," Papers 2406.19403, arXiv.org.

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