Newest papers

Probabilistic Limit Order Book Forecasting with Conditional Diffusion Models

Junyu Chen, Franklin Liu, Moshu Xu, Lijian Yang · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.25495 · cited by 0 works (OpenAlex) · open-access PDF

German

Die Dynamik des Modellierungsintervallordners (LOB) ist für die Marktprognose und die Ausführungsstrategie-Bewertung von zentraler Bedeutung. Während Diffusionsmodelle eine starke empirische Leistung erzielen, fehlen den bestehenden LOB-Snapshot-Modellen typischerweise ein gelernter wiederkehrender Encoder und formale statistische Garantien. Wir führen LOBGrad ein, ein bedingter DDPM-Framework für probabilistische LOB-Prognose, der einen autoregressiven RNN-Codec mit einem diffusionsbasierten Generator kombiniert. Insbesondere etablieren wir eine Oracle-Ungleichheit für den einstufigen DDPM-Lärmvorhersageobjektiv unter exponentiellen Mischungen und Regelmäßigkeit

Machine translation (Meta M2M100); the English original follows.

Abstract

Modeling limit order book (LOB) dynamics is central to market forecasting and execution-strategy evaluation. While diffusion models achieve strong empirical performance, existing LOB snapshot models typically lack a learned recurrent encoder and formal statistical guarantees. We introduce LOBGrad, a conditional DDPM framework for probabilistic LOB forecasting that combines an autoregressive RNN encoder with a diffusion-based generator. In particular, we establish an excess-risk oracle inequality for the one-step DDPM noise-prediction objective under exponential mixing and regularity assumptions. We further show that, under contractive dynamics, the dependence on the conditioning-window length can be reduced to a dependence on the latent-state dimension. Experiments on BTC/USDT, GOOGL, and META show that LOBGrad improves on its encoder-free diffusion ablation across all reported queue metrics and obtains competitive results against other baselines. It also reproduces the key stylized facts of LOB microstructure, and yields reasonably calibrated queue-based predictive intervals. A stylized downstream task illustrates the potential value of the generated forecasts.

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Topics: Probabilistic logic, Stylized fact, Autoregressive model, Computer science, Gaussian, Statistical model

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