Mechanisms of High-Frequency Financial Data on Market Microstructure
Journal: Modern Economics & Management Forum DOI: 10.32629/memf.v6i4.4249
Abstract
This paper systematically reviews the methods and tools used to analyze high-frequency financial data within the framework of market microstructure research. It focuses on classical structural models such as ACD, GARCH, Hawkes processes, VAR, and limit order book models, alongside emerging data-driven approaches including machine learning and Bayesian methods with a novel asynchronous clock integration framework. The theoretical features, strengths, and limitations of these models in explaining microstructure dynamics, handling high-frequency data characteristics, and addressing modeling challenges are discussed. Emphasis is placed on the complementary roles of structural and data-driven models in balancing interpretability and predictive power. Finally, future directions including cross-market structural modeling, multi-factor mechanism integration, and model ensemble strategies are proposed to support deeper theoretical understanding and practical market supervision as well as advance real-time national market stability mechanisms.
Keywords
high-frequency financial data, market microstructure, ACD model, machine learning, limit order book model
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[3]Zhang, Xuekui, Huang, Yuying, Ke Xu, and Li Xing. Novel modelling strategies for high-frequency stock trading data. Financial Innovation 9, no. 1 (2023): 39.
[4]Smith, Reginald D. Is high-frequency trading inducing changes in market microstructure and dynamics? arXiv preprint arXiv: 1006. 5490 (2010).
[5]Ponta, Linda, Enrico Scalas, Marco Raberto, and Silvano Cincotti. Statistical analysis and agent-based microstructure modeling of high-frequency financial trading. IEEE Journal of selected topics in signal processing 6, no. 4 (2011): 381-387.
[6]Doering, Jonathan, Michael Fairbank, and Sheri Markose. Convolutional neural networks applied to high-frequency market microstructure forecasting. In 2017 9th computer science and electronic engineering (ceec), pp. 31-36. IEEE, 2017.
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