Forecasting duration in high-frequency financial data using a self-exciting flexible residual point process

TL;DR

This paper introduces a novel self-exciting residual point process model for accurately forecasting limit order book durations in high-frequency trading, capturing heavy-tailed interarrival times and demonstrating superior predictive performance.

Contribution

The work develops a flexible residual point process that incorporates empirical distribution features and analyzes its stochastic stability, advancing high-frequency data forecasting methods.

Findings

Model achieves strong predictive performance over alternatives.

Incorporates empirical distributional features of interarrival times.

Ensures stochastic stability with irreducibility, aperiodicity, and positive Harris recurrence.

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.