{"ID":2883117,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.08698","arxiv_id":"2508.08698","title":"DiffVolume: Diffusion Models for Volume Generation in Limit Order Books","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.","short_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 Net...","url_abs":"https://arxiv.org/abs/2508.08698","url_pdf":"https://arxiv.org/pdf/2508.08698v1","authors":"[\"Zhuohan Wang\",\"Carmine Ventre\"]","published":"2025-08-12T07:42:00Z","proceeding":"q-fin.TR","tasks":"[\"q-fin.TR\",\"cs.LG\"]","methods":"[\"Diffusion Model\"]","has_code":false}