Forecasting duration in high-frequency financial data using a self-exciting flexible residual point process
Abstract
This paper presents a method for forecasting limit order book durations using a self-exciting flexible residual point process. High-frequency events in modern exchanges exhibit heavy-tailed interarrival times, posing a significant challenge for accurate prediction. The proposed approach incorporates the empirical distributional features of interarrival times while preserving the self-exciting and decay structure. This work also examines the stochastic stability of the process, which can be interpreted as a general state-space Markov chain. Under suitable conditions, the process is irreducible, aperiodic, positive Harris recurrent, and has a stationary distribution. An empirical study demonstrates that the model achieves strong predictive performance compared with several alternative approaches when forecasting durations in ultra-high-frequency trading data.
Cite
@article{arxiv.2604.00346,
title = {Forecasting duration in high-frequency financial data using a self-exciting flexible residual point process},
author = {Kyungsub Lee},
journal= {arXiv preprint arXiv:2604.00346},
year = {2026}
}