# Dynamic forecasting and mechanisms of volatility synchronization in complex financial systems

## TL;DR

This paper introduces a new method to predict and understand how volatility in financial markets synchronizes, using machine learning and a coupled volatility model.

## Contribution

A novel coupled stochastic volatility model and dynamic prediction method for volatility synchronization in financial systems.

## Key findings

- The proposed model shows high in-sample consistency with market behavior.
- It outperforms other models in predicting stock market volatility synchronization accuracy.
- The methodology identifies significant risk events through dynamic simulation and empirical analysis.

## Abstract

Synchronization, which has been a common natural phenomenon, occurs frequently in complex financial systems and is an important contagion mechanism for systemic financial risks and even financial crises. In view of this, we construct a coupled stochastic volatility model and its volatility synchronization analysis framework and combine machine learning methods and rolling cycle window to propose a prediction method for dynamic volatility synchronization. Taking the Shanghai Composite Index (SSEC) and Shenzhen Component Index (SZI) as binary synchronization examples, we analyze the dynamic forecasting performance of the proposed method in an in-sample and out-of-sample empirical comparison by combining multiple loss functions and Superior Predictive Ability (SPA) tests for high-frequency data. It is found that the in-sample estimates of our proposed model are highly consistent with the market behavior and that the model outperforms other models in predicting stock market volatility synchronization accuracy. In addition, by combining dynamic simulation with multivariate empirical mechanism analysis, our methodology not only explores synchronization dynamics but also identifies significant risk events, providing a comprehensive framework for understanding complex system behaviors.