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

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  • Kyungsub Lee

Abstract

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.

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  • Kyungsub Lee, 2023. "Recurrent neural network based parameter estimation of Hawkes model on high-frequency financial data," Papers 2304.11883, arXiv.org.
    • Handle: RePEc:arx:papers:2304.11883
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    References listed on IDEAS

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    1. Ghosh, Pushpendu & Neufeld, Ariel & Sahoo, Jajati Keshari, 2022. "Forecasting directional movements of stock prices for intraday trading using LSTM and random forests," Finance Research Letters, Elsevier, vol. 46(PA).
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    3. E. Bacry & S. Delattre & M. Hoffmann & J. F. Muzy, 2013. "Modelling microstructure noise with mutually exciting point processes," Quantitative Finance, Taylor & Francis Journals, vol. 13(1), pages 65-77, January.
    4. Emmanuel Bacry & Sylvain Delattre & Marc Hoffmann & Jean-François Muzy, 2013. "Modelling microstructure noise with mutually exciting point processes," Post-Print hal-01313995, HAL.
    5. Kyungsub Lee, 2022. "Application of Hawkes volatility in the observation of filtered high-frequency price process in tick structures," Papers 2207.05939, arXiv.org.
    6. Zhang, Yongjie & Chu, Gang & Shen, Dehua, 2021. "The role of investor attention in predicting stock prices: The long short-term memory networks perspective," Finance Research Letters, Elsevier, vol. 38(C).
    7. Wang, Xiaolong & Feng, Jing & Liu, Qi & Li, Yongge & Xu, Yong, 2022. "Neural network-based parameter estimation of stochastic differential equations driven by Lévy noise," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    8. Lee, Kyungsub & Seo, Byoung Ki, 2017. "Marked Hawkes process modeling of price dynamics and volatility estimation," Journal of Empirical Finance, Elsevier, vol. 40(C), pages 174-200.
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