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Financial Knudsen number: breakdown of continuous price dynamics and asymmetric buy and sell structures confirmed by high precision order book information

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  • Yoshihiro Yura
  • Hideki Takayasu
  • Didier Sornette
  • Misako Takayasu

Abstract

We generalise the description of the dynamics of the order book of financial markets in terms of a Brownian particle embedded in a fluid of incoming, exiting and annihilating particles by presenting a model of the velocity on each side (buy and sell) independently. The improved model builds on the time-averaged number of particles in the inner layer and its change per unit time, where the inner layer is revealed by the correlations between price velocity and change in the number of particles (limit orders). This allows us to introduce the Knudsen number of the financial Brownian particle motion and its asymmetric version (on the buy and sell sides). Not being considered previously, the asymmetric Knudsen numbers are crucial in finance in order to detect asymmetric price changes. The Knudsen numbers allows us to characterise the conditions for the market dynamics to be correctly described by a continuous stochastic process. Not questioned until now for large liquid markets such as the USD/JPY and EUR/USD exchange rates, we show that there are regimes when the Knudsen numbers are so high that discrete particle effects dominate, such as during market stresses and crashes. We document the presence of imbalances of particles depletion rates on the buy and sell sides that are associated with high Knudsen numbers and violent directional price changes. This indicator can detect the direction of the price motion at the early stage while the usual volatility risk measure is blind to the price direction.

Suggested Citation

  • Yoshihiro Yura & Hideki Takayasu & Didier Sornette & Misako Takayasu, 2015. "Financial Knudsen number: breakdown of continuous price dynamics and asymmetric buy and sell structures confirmed by high precision order book information," Papers 1508.06024, arXiv.org.
  • Handle: RePEc:arx:papers:1508.06024
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    Cited by:

    1. Haochen Li & Yi Cao & Maria Polukarov & Carmine Ventre, 2023. "An Empirical Analysis on Financial Markets: Insights from the Application of Statistical Physics," Papers 2308.14235, arXiv.org, revised Dec 2023.
    2. Peter B. Lerner, 2021. "Transmission of Trading Orders through Communication Line with Relativistic Delay," IJFS, MDPI, vol. 9(1), pages 1-11, February.
    3. Gontis, V. & Havlin, S. & Kononovicius, A. & Podobnik, B. & Stanley, H.E., 2016. "Stochastic model of financial markets reproducing scaling and memory in volatility return intervals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 1091-1102.
    4. Nian, Fuzhong & Liu, Xinghao & Diao, Hongyuan, 2022. "Mechanism of investor behavior propagation in stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).
    5. Arthur Matsuo Yamashita Rios de Sousa & Hideki Takayasu & Misako Takayasu, 2017. "Detection of statistical asymmetries in non-stationary sign time series: Analysis of foreign exchange data," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-18, May.
    6. Vygintas Gontis & Shlomo Havlin & Aleksejus Kononovicius & Boris Podobnik & H. Eugene Stanley, 2015. "Stochastic model of financial markets reproducing scaling and memory in volatility return intervals," Papers 1507.05203, arXiv.org, revised Oct 2016.
    7. Haochen Li & Maria Polukarova & Carmine Ventre, 2023. "Detecting Financial Market Manipulation with Statistical Physics Tools," Papers 2308.08683, arXiv.org.

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