INVESTIGATING THE INTERDEPENDENCE OF STOCK EXCHANGES USING MULTIVARIATE STOCHASTIC VOLATILITY MODELS
INVESTIGATING THE INTERDEPENDENCE OF STOCK EXCHANGES USING MULTIVARIATE STOCHASTIC VOLATILITY MODELS

Summary/Abstract: This study investigates the interdependence of major global stock exchanges by examining volatility clustering, uncertainty, spillover dynamics, and timevarying return correlations using multivariate stochastic volatility (MSV) models. Drawing on daily data from nine major indices representing the United States, Europe, and Asia—specifically, the S&P 500, Dow Jones, NASDAQ, DAX, CAC 40, FTSE 100, Nikkei 225, Hang Seng, and Shanghai Composite—the analysis spans from January 5, 2001, to January 5, 2024, offering a comprehensive view of long-term market interactions. The empirical approach is based on three model variations: the standard MSV, Constant Correlation MSV (CC-MSV), and Dynamic Correlation MSV (DC-MSV), estimated within a Bayesian framework using Markov Chain Monte Carlo (MCMC) methods. The findings reveal the presence of significant volatility clustering and persistence, particularly in Asian markets, as well as substantial intra- and inter-regional spillover effects. Volatility shocks originating from one market were shown to influence others, with U.S. markets identified as the primary transmitters of global volatility. Additionally, the results indicate that the correlations between markets are dynamic and tend to intensify during periods of financial distress, reflecting stronger market comovements in times of global uncertainty. The DC-MSV model, which allows for both time-varying volatility and correlations, outperforms the other specifications, as evidenced by lower Deviance Information Criterion (DIC) values across regions. These results underscore the increasing integration of global financial markets and the diminishing effectiveness of traditional geographic diversification strategies. From a policy perspective, the findings emphasize the need for financial regulators to monitor international volatility spillovers and systemic risks, particularly in an era of heightened financial globalization. For investors and portfolio managers, understanding the structure and dynamics of cross-market volatility is crucial for improving risk management, portfolio allocation, and hedging strategies. This study makes a significant contribution to the literature by applying advanced stochastic volatility models to a large and geographically diverse set of stock markets, providing robust insights into the evolving nature of global market interdependence.

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