Volatility spillover in a financial network: A quantile vector autoregression analysis of Vietnam under normal and extreme market conditions

Abstract

While existing literature has extensively examined volatility spillovers across various financial networks, studies focusing on Vietnam remain fragmented. A comprehensive understanding of a financial network in which Vietnam acts as the center is still lacking. This thesis aims to fill that gap by investigating volatility spillover effects between Vietnam’s financial market – represented by the VN-Index – and global markets that contribute significantly to FDI inflows into Vietnam, including the U.S., Japan, South Korea, Hong Kong, Taiwan, China, and Singapore, along with key commodity markets – oil and gold. Using a Quantile Vector Autoregression (QVAR) model combined with Forecast Error Variance Decomposition (FEVD), the study identifies major external sources of volatility affecting Vietnam, assesses Vietnam’s role within the global financial network, and evaluates how these spillovers evolve across normal and extreme market conditions over period of 2004-2024. The findings reveal four key insights. First, the U.S. consistently exerts the strongest influence on Vietnam’s financial market across quantiles. Second, commodities, in comparison to the FDI channel, play a limited role in transmitting volatility to Vietnam. Third, Vietnam consistently acts as a net receiver of volatility. Fourth, the spillover effects in response to negative shocks tend to intensify over time, while those from small or positive shocks are only significant during period of crises, such as the Global Financial Crisis and the COVID-19 pandemic. These results have important implications for policymakers and investors aiming to enhance financial resilience and manage external risks effectively