Cross-market volatility spillovers between China and the United States: A DCC-EGARCH-t-Copula framework with out-of-sample forecasting.

Abstract

This study examines volatility spillovers between Chinese and U.S. equity markets by developing a comprehensive framework that captures asymmetric volatility, extreme co-movements, and dynamic correlations. We propose an integrated methodology combining EGARCH models with Student-t innovations, a Student-t copula, and a Dynamic Conditional Correlation (DCC) structure. Using daily returns of the Hang Seng Index (HSI) and the S&P 500, our analysis reveals three principal findings. First, the EGARCH model effectively captures the pronounced leverage effect and fat-tailed distributions characteristic of both markets. Second, the Student-t copula demonstrates the best fit among competing specifications, indicating significant symmetric tail dependence between the two markets. Third, time-varying correlations exhibit high persistence, rising during crises yet remaining within a moderate range. Crucially, out-of-sample forecasting shows that our unified framework achieves superior predictive accuracy relative to standard benchmarks. These findings provide valuable insights for investors designing hedging strategies, exchanges determining margin requirements, and policymakers monitoring financial contagion. Our approach offers a robust tool for analyzing volatility transmission between developed and emerging markets.