Modelling High-Frequency Data with Bivariate Hawkes Processes: Power-Law vs. Exponential Kernels
Fuente:
arXiv
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866910883306799104 |
|---|---|
| author | Batra, Neal |
| author_facet | Batra, Neal |
| contents | This study explores the application of Hawkes processes to model high-frequency data in the context of limit order books. Two distinct Hawkes-based models are proposed and analyzed: one utilizing exponential kernels and the other employing power-law kernels. These models are implemented within a bivariate framework. The performance of each model is evaluated using high-frequency trading data, with a focus on their ability to reproduce key statistical properties of limit order books. Through a comprehensive comparison, we identify the strengths and limitations of each kernel type, providing insights into their suitability for modeling high-frequency financial data. Simulations are conducted to validate the models, and the results are interpreted. Based on these insights, a trading strategy is formulated. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_14814 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Modelling High-Frequency Data with Bivariate Hawkes Processes: Power-Law vs. Exponential Kernels Batra, Neal Mathematical Finance This study explores the application of Hawkes processes to model high-frequency data in the context of limit order books. Two distinct Hawkes-based models are proposed and analyzed: one utilizing exponential kernels and the other employing power-law kernels. These models are implemented within a bivariate framework. The performance of each model is evaluated using high-frequency trading data, with a focus on their ability to reproduce key statistical properties of limit order books. Through a comprehensive comparison, we identify the strengths and limitations of each kernel type, providing insights into their suitability for modeling high-frequency financial data. Simulations are conducted to validate the models, and the results are interpreted. Based on these insights, a trading strategy is formulated. |
| title | Modelling High-Frequency Data with Bivariate Hawkes Processes: Power-Law vs. Exponential Kernels |
| topic | Mathematical Finance |
| url | https://arxiv.org/abs/2503.14814 |