Exploring volatility spillover of Chinese financial system using empirical mode decomposition and multilayer networks

Purpose This paper aims to investigate the shock transmission structure among Chinese financial institutions. Design/methodology/approach Based on the market data of 34 listed financial institutions from 2011 to 2023, this study proposes the empirical mode decomposition into the generalized forecast variance decomposition framework to create multilayer networks that include volatility spillover layers at multidimensional time scales for analyzing the shock transmission structure among Chinese financial institutions. Findings Empirical research indicates that layers at different time scales exhibit distinct network structures, and multilayer network analysis provides a more comprehensive understanding of the financial system. The volatility spillover networks exhibit notable time-varying characteristics with significant periodic patterns linked to endogenous and exogenous shocks. The importance of financial institutions is dynamic. Key nodes in the multilayer spillover network mainly come from the banking and securities sectors, with banks being primarily joint-stock commercial banks and urban commercial banks and securities institutions controlled mainly by large central enterprises or local governments. Originality/value This paper explores how to use an adaptive decomposition method to more accurately construct multilayer networks for risk contagion and proposes a new “time-frequency domain” multilayer network analysis framework. By constructing multilayer networks at different time scales, the risk contagion patterns driven by different economic mechanisms are elucidated. Finally, this article provides a more refined and robust evaluation of the systemic importance of financial institutions from a dynamic network perspective with multiple time scales.