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Uncovering Market Disorder and Liquidity Trends Detection

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  • Etienne Chevalier
  • Yadh Hafsi
  • Vathana Ly Vath

Abstract

The primary objective of this paper is to conceive and develop a new methodology to detect notable changes in liquidity within an order-driven market. We study a market liquidity model which allows us to dynamically quantify the level of liquidity of a traded asset using its limit order book data. The proposed metric holds potential for enhancing the aggressiveness of optimal execution algorithms, minimizing market impact and transaction costs, and serving as a reliable indicator of market liquidity for market makers. As part of our approach, we employ Marked Hawkes processes to model trades-through which constitute our liquidity proxy. Subsequently, our focus lies in accurately identifying the moment when a significant increase or decrease in its intensity takes place. We consider the minimax quickest detection problem of unobservable changes in the intensity of a doubly-stochastic Poisson process. The goal is to develop a stopping rule that minimizes the robust Lorden criterion, measured in terms of the number of events until detection, for both worst-case delay and false alarm constraint. We prove our procedure's optimality in the case of a Cox process with simultaneous jumps, while considering a finite time horizon. Finally, this novel approach is empirically validated by means of real market data analyses.

Suggested Citation

  • Etienne Chevalier & Yadh Hafsi & Vathana Ly Vath, 2023. "Uncovering Market Disorder and Liquidity Trends Detection," Papers 2310.09273, arXiv.org.
    • Handle: RePEc:arx:papers:2310.09273
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    References listed on IDEAS

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    1. Hamilton, James D., 1988. "Rational-expectations econometric analysis of changes in regime : An investigation of the term structure of interest rates," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 385-423.
    2. E. Bacry & K. Dayri & J. F. Muzy, 2011. "Non-parametric kernel estimation for symmetric Hawkes processes. Application to high frequency financial data," Papers 1112.1838, arXiv.org.
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    Cited by:

    1. Yadh Hafsi & Edoardo Vittori, 2024. "Optimal Execution with Reinforcement Learning," Papers 2411.06389, arXiv.org, revised Nov 2025.

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