This study examines the use of a recurrent neural network for estimating the parameters of a Hawkes model based on high-frequency financial data, and subsequently, for computing volatility. Neural networks have shown promising results in various fields, and interest in finance is also growing. Our approach demonstrates significantly faster computational performance compared to traditional maximum likelihood estimation methods while yielding comparable accuracy in both simulation and empirical studies. Furthermore, we demonstrate the application of this method for real-time volatility measurement, enabling the continuous estimation of financial volatility as new price data keeps coming from the market.

翻译：本研究探讨了利用循环神经网络对基于高频金融数据的霍克斯模型进行参数估计，并进而计算波动性的方法。神经网络已在多个领域展现出令人瞩目的成果，其在金融领域的应用兴趣也日益增长。与传统最大似然估计方法相比，我们的方法在计算性能上显著提升，同时在模拟和实证研究中均保持了相当的准确性。此外，我们展示了该方法在实时波动性测量中的应用，能够随着市场持续输入新的价格数据，对金融波动性进行连续估计。