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DiffVolume: Diffusion Models for Volume Generation in Limit Order Books

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  • Zhuohan Wang
  • Carmine Ventre

Abstract

Modeling limit order books (LOBs) dynamics is a fundamental problem in market microstructure research. In particular, generating high-dimensional volume snapshots with strong temporal and liquidity-dependent patterns remains a challenging task, despite recent work exploring the application of Generative Adversarial Networks to LOBs. In this work, we propose a conditional \textbf{Diff}usion model for the generation of future LOB \textbf{Volume} snapshots (\textbf{DiffVolume}). We evaluate our model across three axes: (1) \textit{Realism}, where we show that DiffVolume, conditioned on past volume history and time of day, better reproduces statistical properties such as marginal distribution, spatial correlation, and autocorrelation decay; (2) \textit{Counterfactual generation}, allowing for controllable generation under hypothetical liquidity scenarios by additionally conditioning on a target future liquidity profile; and (3) \textit{Downstream prediction}, where we show that the synthetic counterfactual data from our model improves the performance of future liquidity forecasting models. Together, these results suggest that DiffVolume provides a powerful and flexible framework for realistic and controllable LOB volume generation.

Suggested Citation

  • Zhuohan Wang & Carmine Ventre, 2025. "DiffVolume: Diffusion Models for Volume Generation in Limit Order Books," Papers 2508.08698, arXiv.org.
    • Handle: RePEc:arx:papers:2508.08698
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    References listed on IDEAS

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    1. Vladimir Filimonov & Didier Sornette, 2012. "Quantifying reflexivity in financial markets: towards a prediction of flash crashes," Papers 1201.3572, arXiv.org, revised Apr 2012.
    2. Luc Bauwens & Nikolaus Hautsch, 2009. "Modelling Financial High Frequency Data Using Point Processes," Springer Books, in: Thomas Mikosch & Jens-Peter Kreiß & Richard A. Davis & Torben Gustav Andersen (ed.), Handbook of Financial Time Series, chapter 41, pages 953-979, Springer.
    3. Tucker Hybinette Balch & Mahmoud Mahfouz & Joshua Lockhart & Maria Hybinette & David Byrd, 2019. "How to Evaluate Trading Strategies: Single Agent Market Replay or Multiple Agent Interactive Simulation?," Papers 1906.12010, arXiv.org.
    4. Vladimir Filimonov & Didier Sornette, 2012. "Quantifying Reflexivity in Financial Markets: Towards a Prediction of Flash Crashes," Swiss Finance Institute Research Paper Series 12-02, Swiss Finance Institute.
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    Cited by:

    1. Zhuohan Wang & Carmine Ventre, 2026. "DiffLOB: Diffusion Models for Counterfactual Generation in Limit Order Books," Papers 2602.03776, arXiv.org.

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