Forecasting Market Volatility Using High Frequency Data and Mathematical Finance Methods

Abstract

Market volatility prediction is of great significance in the financial field, and is crucial to investment decision-making and risk management. In order to predict market volatility more accurately, high-frequency data and mathematical financial methods will be combined. High-frequency data provide more detailed market information, while mathematical financial methods provide rigorous models and tools. By combining high-frequency data and mathematical financial methods, it is expected to achieve accurate forecasting of market volatility. Specifically, it is hoped that it can reveal the volatility asymmetry and volatility aggregation of market volatility, and provide real-time and dynamic volatility forecasts. This will help investors and risk managers to better understand and assess market risks, so as to formulate more effective investment and risk management strategies.