Anomaly Detection Method for High-Frequency Financial Market Volatility Data Based on LLM
DOI:
https://doi.org/10.5281/zenodo.11636947ARK:
https://n2t.net/ark:/40704/JETBM.v1n3a05PURL:
https://purl.archive.org/suas/JETBM.v1n3a05References:
37Keywords:
Financial Market Volatility, High-frequency Data, Anomaly Detection, Large Language ModelsAbstract
This article introduces the importance and challenges of detecting anomalies in high-frequency volatility data in financial markets. Traditional methods such as SV and GARCH models have been unable to cope with the rapidly changing and increasing complexity of the market environment, so new strategies must be developed to identify abnormal fluctuations quickly. This paper proposes a method based on local linear mapping (LLM), which aims to improve anomaly detection accuracy, monitor market fluctuations in real-time, and identify potential risk events, enhancing investment decisions and promoting financial market stability and sustainable development.
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