Simulating Limit Order Books with Hawkes-Driven Order Flow

Summary

This research framework combines a deterministic limit order book simulator with stochastic order arrivals generated by multivariate marked Hawkes processes. It explains how linear and nonlinear Hawkes models can represent clustered order flow, and reports stability and ergodicity results for both model types. The framework also includes time-rescaling and goodness-of-fit diagnostics for assessing fitted event models.

The authors calibrate exponential and power-law kernels using Binance BTCUSDT and LOBSTER AAPL data. They report that a nearly unstable but subcritical regime is important for reproducing realistic order-flow clustering. The document describes a research method and empirical calibration, rather than a trading strategy or profitability test. Its conclusions depend on the selected datasets and model assumptions; the summary does not provide calibration details, diagnostic outcomes, or evidence about performance in live execution.

Key ideas

• Hawkes processes can model clustering in limit order book event arrivals.
• The framework pairs stochastic order flow with a deterministic limit order book simulator.
• Stability and ergodicity are analyzed for both linear and nonlinear Hawkes models.
• Time-rescaling and goodness-of-fit diagnostics are used to assess fitted processes.
• The reported calibrations suggest near-critical subcritical dynamics help reproduce realistic clustering.

Tags

# A Deterministic Limit Order Book Simulator with Hawkes-Driven Order Flow


# A Deterministic Limit Order Book Simulator with Hawkes-Driven Order Flow









We present a reproducible research framework for market microstructure combining a deterministic C++ limit order book (LOB) simulator with stochastic order flow generated by multivariate marked Hawkes processes. The paper derives full stability and ergodicity proofs for both linear and nonlinear Hawkes models, implements time-rescaling and goodness-of-fit diagnostics, and calibrates exponential and power-law kernels on Binance BTCUSDT and LOBSTER AAPL datasets. Empirical results highlight the nearly-unstable subcritical regime as essential for reproducing realistic clustering in order flow. All code, datasets, and configuration files are publicly available at https://github.com/sohaibelkarmi/High-Frequency-Trading-Simulator

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.