标题： 用二元霍克斯过程对高频数据进行建模：幂律核与指数核 显示英文标题

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

摘要： 本研究探讨了Hawkes过程在限单簿上下文中对高频数据建模的应用。 提出了两种不同的基于Hawkes的模型并进行了分析：一种使用指数核，另一种使用幂律核。 这些模型是在一个双变量框架内实现的。 通过使用高频交易数据评估每个模型的性能，重点关注它们复制限单簿关键统计特性的能力。 通过全面比较，我们确定了每种核类型的优缺点，提供了关于其适用于建模高频金融数据的见解。 进行了模拟以验证模型，并对结果进行了解释。 基于这些见解，制定了一种交易策略。 显示英文摘要

摘要： 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.