Theoretical Exploration of Fourier Frequency Domain Filtering in High-Frequency Noise Control and Risk Measurement in the Financial Market

This study systematically explores the theoretical framework and practical value of Fourier frequency-domain filtering techniques in addressing high-frequency data noise in financial markets. By targeting the market microstructure noise inherent in high-frequency trading data, we propose a Fourier transform-based frequency-domain filtering method that effectively separates genuine price signals from noise components. The research demonstrates that this approach not only significantly improves volatility estimation accuracy but also enhances the effectiveness of liquidity measurement metrics, providing more reliable quantitative tools for risk management. Through theoretical analysis and empirical verification, this paper validates the innovative and practical applications of Fourier frequency-domain filtering in financial risk measurement, offering substantial theoretical and practical significance for high-frequency trading risk management.