  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.
