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

Mengrui Zhu, Hua Xu, Xingyu Gao, Minggang Wang, André L. M. Vilela and Lixin Tian
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Mengrui Zhu: School of Mathematical Science, Nanjing Normal University, Nanjing 210042, China
Hua Xu: Department of Mathematics, Nanjing Normal University Taizhou College, Taizhou 225300, China
Xingyu Gao: School of Mathematics and Statistics, Changshu Institute of Technology, Changshu 215500, China
Minggang Wang: School of Mathematical Science, Nanjing Normal University, Nanjing 210042, China
André L. M. Vilela: Center for Polymer Studies and Department of Physics, Boston University, Boston, MA 02115, USA
Lixin Tian: School of Mathematical Science, Nanjing Normal University, Nanjing 210042, China

Energies, 2022, vol. 15, issue 15, 1-18

Abstract: The scientific judgement of 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. However, the existing methods for identifying structural changes of the carbon market based on carbon price data mostly regard the carbon price series as a deterministic time series and pay less attention to the uncertainty implied by the carbon price series. We propose a framework for identifying 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, using the distribution of probability density to reveal the uncertainty information implied by carbon price series and constructing a recurrence network of carbon price probability density. Based on the community structure, the break index and statistical test method are defined. The 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 many abrupt transitions in the carbon price series of the two markets, whose occurrence period is closely related to major events.

Keywords: carbon market; probability distribution; recurrence network; breakpoints (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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