Identification of Breakpoints in Carbon Market Based on Probability Density Recurrence Network
Abstract
:1. Introduction
2. Preliminaries and Proposed Method
2.1. Preliminaries
2.1.1. Bai–Perron Test
2.1.2. Recurrence Network
2.2. The Proposed Method
2.2.1. Probability Density Recurrence Network
2.2.2. Definition of Break Index and Statistical Test Based on Community Structure
2.2.3. Method to Identify Breakpoints in Carbon Market Based on PDRN
Method to Identify Breakpoints of One-Dimensional Carbon Price Data
Method to Identify Breakpoints of High-Dimensional Carbon Price Data
3. Numerical Simulation
4. Empirical Analysis of Breakpoints Identification in EU ETS
4.1. Identification of Breakpoints of Carbon Price in the EU ETS Based on Daily Highest–Lowest Prices
4.2. Identification of Breakpoints of Carbon Price in the EU ETS Based on Daily Closing Price
5. Empirical Analysis of Identification of Carbon Price Breakpoint in China’s Carbon Market
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
References
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Index | Closing Price | Highest Price | Lowest Price |
---|---|---|---|
Mean | 11.63625 | 11.73901 | 11.44756 |
Max | 30.77000 | 30.1000 | 30.24000 |
Min | 2.700000 | 2.900000 | 2.490000 |
Std | 8.438122 | 8.532572 | 8.295047 |
Skewness | 0.895698 | 0.910802 | 0.904718 |
Kurtosis | 2.110458 | 2.140502 | 2.133726 |
JB test | 333.3657 *** | 338.0814 *** | 335.3738 *** |
ADF test | −1.792782 (−3.433422) | −1.830002 (−3.433424) | −1.836951 (−3.433424) |
Pilot Area | Break Time | Break Index γ | p-Values |
---|---|---|---|
Shanghai | 12 November 2014 | 0.5981 | 0.0040 |
1 July 2015 | 0.5772 | 0.0490 | |
26 August 2015 | 0.5814 | 0.0180 | |
28 February 2017 | 0.7834 | 0.0000 | |
24 October 2018 | 0.6351 | 0.0000 | |
26 April 2019 | 0.5889 | 0.0040 | |
Shenzhen | 12 November 2014 | 0.6033 | 0.0000 |
12 January 2015 | 0.5836 | 0.0160 | |
26 August 2015 | 0.6098 | 0.0000 | |
29 June 2017 | 0.6052 | 0.0000 | |
7 July 2020 | 0.6838 | 0.0000 | |
Tianjin | 17 September 2014 | 0.5907 | 0.0300 |
12 November 2014 | 0.5870 | 0.0080 | |
1 July 2015 | 0.7004 | 0.0000 | |
26 August 2015 | 0.5860 | 0.0060 | |
24 August 2016 | 0.6009 | 0.0010 | |
7 July 2020 | 0.6190 | 0.0000 |
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Zhu, M.; Xu, H.; Gao, X.; Wang, M.; Vilela, A.L.M.; Tian, L. Identification of Breakpoints in Carbon Market Based on Probability Density Recurrence Network. Energies 2022, 15, 5540. https://doi.org/10.3390/en15155540
Zhu M, Xu H, Gao X, Wang M, Vilela ALM, Tian L. Identification of Breakpoints in Carbon Market Based on Probability Density Recurrence Network. Energies. 2022; 15(15):5540. https://doi.org/10.3390/en15155540
Chicago/Turabian StyleZhu, Mengrui, Hua Xu, Xingyu Gao, Minggang Wang, André L. M. Vilela, and Lixin Tian. 2022. "Identification of Breakpoints in Carbon Market Based on Probability Density Recurrence Network" Energies 15, no. 15: 5540. https://doi.org/10.3390/en15155540
APA StyleZhu, M., Xu, H., Gao, X., Wang, M., Vilela, A. L. M., & Tian, L. (2022). Identification of Breakpoints in Carbon Market Based on Probability Density Recurrence Network. Energies, 15(15), 5540. https://doi.org/10.3390/en15155540