Forecasting duration in high-frequency financial data using a self-exciting flexible residual point process
Fuente:
arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866911558788972544 |
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| author | Lee, Kyungsub |
| author_facet | Lee, Kyungsub |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_00346 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Forecasting duration in high-frequency financial data using a self-exciting flexible residual point process Lee, Kyungsub Statistical Finance Trading and Market Microstructure Applications 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. |
| title | Forecasting duration in high-frequency financial data using a self-exciting flexible residual point process |
| topic | Statistical Finance Trading and Market Microstructure Applications |
| url | https://arxiv.org/abs/2604.00346 |