Identification of Breakpoints in Carbon Market Based on Probability Density Recurrence Network

To scientifically judge the structural abrupt transition characteristics of the carbon market price is an important means to comprehensively analyze its fluctuation law and effectively prevent carbon market risks. We propose a framework to identify abrupt transitions in the carbon market from the perspective of a complex network by considering the influence of random factors on the carbon price series, expressing the carbon price series as a sequence of probability density functions, and constructing a recurrence network of carbon price probability density. Based on the community structure, the break index and statistical test method are defined. Simulation verifies the effectiveness and superiority of the method compared with traditional methods. An empirical analysis uses the carbon price data of the European Union carbon market and seven pilot carbon markets in China. The results show that many abrupt transitions in the carbon price series of two markets, whose occurrence period is closely related to major events