DiffVolume: Diffusion Models for Volume Generation in Limit Order Books
This provides a more realistic and controllable framework for generating financial market microstructure data, though it represents an incremental application of diffusion models to a specific domain.
The authors tackled the problem of generating realistic limit order book volume snapshots by proposing DiffVolume, a conditional diffusion model that better reproduces statistical properties like marginal distribution and autocorrelation decay, and improves downstream liquidity forecasting performance by up to 15% compared to baseline methods.
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.