DiffVolume: Diffusion Models for Volume Generation in Limit Order Books

• URL: http://arxiv.org/abs/2508.08698v1
• Date: Tue, 12 Aug 2025 07:42:00 GMT
• Title: DiffVolume: Diffusion Models for Volume Generation in Limit Order Books
• Authors: Zhuohan Wang, Carmine Ventre,
• Abstract summary: We propose a conditional textbfDiffusion model for the generation of future LOB textbfVolume snapshots (textbfDiffVolume)<n>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.
• Score: 1.5193212081459284
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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