Multilayer network analysis for measuring the inter-connectedness between the oil market and G20 stock markets

• Published in: Energy economics 2023-04, Vol.120, p.106639, Article 106639
• Main Authors: Dai, Zhifeng, Tang, Rui, Zhang, Xinhua
• Format: Article
• Language: English
• Subjects: Connectedness; Multilayer networks; Oil market; System risk
• ISSN: 0140-9883; 1873-6181
• DOI: 10.1016/j.eneco.2023.106639

We constructed a multilayer information spillover network containing a return spillover layer, a volatility spillover layer, and an extreme risk spillover layer to explore the system risk in the oil and G20 stock markets during 2006–2022. This paper explores the topology of the static and dynamic multilayer networks from both system-level and market-level perspectives. We find that: (i) At the system-level, the structure of layers is significantly different from each other, and risk is transmitted mainly in the volatility spillover layer. In times of crisis, the spillover effect between layers increases. In addition, multilayer networks are more sensitive to risk identification and can identify risks earlier. (ii) At the market-level, developed markets tend to have high connectivity and act as risk-emitter in spillover networks. Developing markets tend to be risk-receivers. Markets with a homogeneous economic structure are more likely to receive shocks and change from risk-receiving to risk-emitting markets. Multilayer information spillover networks provide comprehensive information on financial linkages between national stock markets, which helps regulators and investors to prevent system risks better and allocate assets appropriately. •This paper constructs a multilayer information spillover network.•Each layer exhibits a unique network structure and spillover evolution behaviour.•Static and dynamic multilayer networks for G20 stock market are built for studying their connectedness.•Developed countries tend to have high connectivity and act as risk-sending countries in spillover networks.