Multidimensional risk contagions in commodity markets: A multi-layer information networks method

To explore multidimensional risk contagion in the commodity futures markets, this study constructs a multilayer information spillover network that includes the extreme risk spillover layer, the volatility spillover layer, and the return spillover layer. This multilayer network is constructed based on the LASSO-VAR-DY model. The topological characteristics of multi-layer networks are measured to examine both system and market levels from static and dynamic perspectives. This study finds that the risk transmission patterns in the commodity markets exhibit dynamic characteristics and experience significant fluctuations under the influence of major economic events. Structural differences exist in the risk spillover patterns across different layers. In the long term, the return layer demonstrates greater uniqueness, whereas in the short term, the volatility layer serves as a key channel for risk transmission. The propagation of extreme risk is likely driven by the combined effects of returns and volatility. Furthermore, Brent crude oil, WTI, fuel oil consistently act as major risk transmitters and receivers across all layers. The global financial crisis and the COVID-19 pandemic had the most pronounced impact on the multilayer risk spillover network in the commodity markets, leading to increased network homogenization. In addition to traditional safe-haven assets such as gold and natural gas, certain agricultural commodities—including orange juice, lean hogs, rough rice, coffee, and cocoa—also exhibited independence during these crises.