Dynamic forecasting and mechanisms of volatility synchronization in complex financial systems

Area of Science:

• Financial Econometrics
• Complex Systems Analysis
• Machine Learning Applications

Background:

• Volatility synchronization is a key contagion mechanism in financial markets, contributing to systemic risk.
• Existing models struggle to accurately capture and predict dynamic volatility synchronization.
• Understanding these synchronization patterns is vital for financial stability.

Purpose of the Study:

• To develop a novel prediction method for dynamic volatility synchronization in financial markets.
• To analyze the forecasting performance of the proposed method using real-world stock market data.
• To provide a comprehensive framework for understanding complex financial system behaviors and risk events.

Main Methods:

• Construction of a coupled stochastic volatility model with a volatility synchronization analysis framework.
• Integration of machine learning techniques and a rolling cycle window for dynamic prediction.
• Empirical analysis using high-frequency data from the Shanghai Composite Index (SSEC) and Shenzhen Component Index (SZI), employing multiple loss functions and Superior Predictive Ability (SPA) tests.

Main Results:

• The proposed model demonstrates high consistency with market behavior in in-sample estimations.
• The method significantly outperforms other models in predicting stock market volatility synchronization accuracy.
• Dynamic simulation and multivariate empirical analysis successfully identified significant risk events.

Conclusions:

• The developed methodology offers a robust framework for predicting and understanding financial volatility synchronization.
• The findings enhance our ability to manage systemic financial risks and prevent crises.
• This approach provides valuable insights into the dynamics of complex financial systems.