Early Detection of Latent Microstructure Regimes in Limit Order Books
For practitioners in high-frequency finance, this provides a method for early detection of market stress with theoretical guarantees, though results degrade in low signal-to-noise conditions.
The paper formalizes a three-regime causal model for limit order books, where a latent build-up phase precedes observable stress, and proposes a trigger-based detector that achieves a mean lead-time of +18.6 ± 3.2 timesteps with perfect precision in simulations, outperforming baselines.
Limit order books can transition rapidly from stable to stressed conditions, yet standard early-warning signals such as order flow imbalance and short-term volatility are inherently reactive. We formalise this limitation via a three-regime causal data-generating process (stable $\to$ latent build-up $\to$ stress) in which a latent deterioration phase creates a prediction window prior to observable stress. Under mild assumptions on temporal drift and regime persistence, we establish identifiability of the latent build-up regime and derive guarantees for strictly positive expected lead-time and non-trivial probability of early detection. We propose a trigger-based detector combining MAX aggregation of complementary signal channels, a rising-edge condition, and adaptive thresholding. Across 200 simulations, the method achieves mean lead-time $+18.6 \pm 3.2$ timesteps with perfect precision and moderate coverage, outperforming classical change-point and microstructure baselines. A preliminary application to one week of BTC/USDT order book data shows consistent positive lead-times while baselines remain reactive. Results degrade in low signal-to-noise and short build-up regimes, consistent with theory.