# Recurrent neural network based parameter estimation of Hawkes model on high-frequency financial data

arXiv: https://arxiv.org/abs/2304.11883 · HTML: https://mlsanity.com/paper/2304.11883/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 14 references that are in the map.
- First posted on arXiv 24 Apr 2023.

## Builds on (most cited first, 3 of 3)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [Forecasting directional movements of stock prices for intraday trading using LSTM and random forests](https://mlsanity.com/paper/2004.10178/) (2020)

## Built on by (most cited first, 2 of 2)

- [NeuroMemFPP: A recurrent neural approach for memory-aware parameter estimation in fractional Poisson process](https://mlsanity.com/paper/2512.05893/) (2025)
- [NeuroMem-FHP: A Likelihood-Free Deep Learning Framework for Parameter Estimation of Fractional Hawkes Process](https://mlsanity.com/paper/2607.11177/) (2026)

## Abstract (arXiv)

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.