Stock Market Contagion in the Time of COVID-19: A Multivariate AR-FIAPARCH–DCC Approach

In this paper, we investigate the intricate relationship between health crises and financial market volatility over the period from May 1, 2015, to August 22, 2022. We employ a sophisticated methodological framework that integrates multivariate Fractionally Integrated Asymmetric Power ARCH (FIAPARCH) analysis with a dynamic Markov Switching Regression model. Our dataset encompasses a diverse set of financial indices, including major markets such as Japan, France, Germany, the United Kingdom, and the United States, as well as key players like Hong Kong, Singapore, Taiwan, Spain, and Italy. Additionally, we incorporate commodities, specifically the WTI oil index, gold, and gas, to capture the interdependencies between financial and commodity markets. Our empirical findings do not uniformly support a contagion effect across markets. Instead, we observe relatively weak inter-market effects, particularly among developed economies, suggesting a potential trend towards isolation or decoupling during the initial stages of the crisis. Further analysis reveals that volatile market regimes align closely with the crisis period, as anticipated. Notably, we identify significant increases in Dynamic Conditional Correlations (DCCs) between specific index pairs, particularly China and Oil, China and Italy, and China and Gas. Moreover, we observe a consistent upward trajectory in DCCs across all pairs, albeit with varying degrees of intensity over time. These findings, confirmed by robustness checks, underscore the necessity of a nuanced understanding of Dynamic Conditional Correlations to comprehensively unravel the mechanisms of financial contagion.