Risk spillover across Chinese industries: novel evidence from multilayer connectedness networks

Purpose Previous research has predominantly concentrated on examining risk spillovers through single-layer networks, neglecting the multi-related and multilayer network characteristics of the economic system. This study constructs multilayer connectedness networks, including return, volatility and extreme risk layers, to systematically analyze the risk spillovers across Chinese industries at the system and industry levels. Design/methodology/approach Previous studies have constructed multilayer networks using Diebold and Yilmaz’s (2012) approach or the time-varying parameter vector autoregressive (TVP-VAR) connectedness model. In this study, we employ the TVP-VAR-extended joint connectedness approach, which improves these methods and captures risk spillovers more accurately. Findings At the system level, the risk spillover across industries exhibits distinct network structures and dynamic evolution behaviors across different layers. During extreme events, the intensity, scope and speed of risk spillovers increase markedly across all layers, with volatility and extreme risk layers demonstrating greater sensitivity to crises. At the industry level, industrial and optional consumption typically serve as risk transmitters, while medicine and health, as well as financial real estate, tend to be risk receivers across three layers. Moreover, industrial, optional consumption and materials exhibit significant systemic importance. Originality/value To the best of our knowledge, this is the first study to apply multilayer networks with return, volatility and extreme risk layers to systematically examine risk spillovers between Chinese industries.