Early warning is an important and challenging issue in governmental policy-making. This study proposes a skillful spillover network-based machine learning model to provide early warnings of critical transition in energy and stock markets. First, the critical transition of stock and energy time series can be detected using a hidden Markov model. Second, a dynamic spillover network is established, which can help to understand the characteristics of return volatility from the perspective of the time-varying structure of spillover relationships. A machine learning algorithm is employed to model the early warning of critical transition based on the topological structures of the network. The results demonstrated that the proposed model can identify the early warning of critical transition with the warning day, e.g., one day or thirty days, with a high generalizationability. Our study enriches critical transition research and can offer important warning signals for policy-makers and market investors.
Keywords Machine learning, Spillover network, Critical transition, Energy and stock markets