Modelling High-Frequency Data with Bivariate Hawkes Processes: Power-Law vs. Exponential Kernels
Abstract
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.
Cite
@article{arxiv.2503.14814,
title = {Modelling High-Frequency Data with Bivariate Hawkes Processes: Power-Law vs. Exponential Kernels},
author = {Neal Batra},
journal= {arXiv preprint arXiv:2503.14814},
year = {2025}
}