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Modelling High-Frequency Data with Bivariate Hawkes Processes: Power-Law vs. Exponential Kernels

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  • Neal Batra

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.

Suggested Citation

  • Neal Batra, 2025. "Modelling High-Frequency Data with Bivariate Hawkes Processes: Power-Law vs. Exponential Kernels," Papers 2503.14814, arXiv.org.
    • Handle: RePEc:arx:papers:2503.14814
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    1. Anatoliy Swishchuk & Aiden Huffman, 2018. "General Compound Hawkes Processes in Limit Order Books," Papers 1812.02298, arXiv.org.
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