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

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 a...

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Bibliographic Details
Published in:Finance research letters 2023-07, Vol.55, p.103922, Article 103922
Main Author: Lee, Kyungsub
Format: Article
Language:English
Subjects:
Estimation
Hawkes model
High-frequency stock price
Neural network
Volatility
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Summary: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. •Parameters of the Hawkes model for high-frequency price dynamics are estimated based on LSTM.•Volatility of price is computed by utilizing the estimates obtained by LSTM.•Estimation results showed fairly high accuracy and very fast computation time.
ISSN:1544-6123
1544-6131
DOI:10.1016/j.frl.2023.103922