DSpace Home
→
Електронні публікації
→
Фізико-математичний факультет
→
Кафедра інформатики та прикладної математики
→
View Item

Correlational and Non-extensive Nature of Carbon Dioxide Pricing Market

Bielinskyi, Andrii O.; Matviychuk, Andriy V.; Serdyuk, Oleksandr A.; Семеріков, Сергій Олексійович; Solovieva, Victoria V.; Соловйов, Володимир Миколайович; Бєлінський, Андрій Олександрович; Матвійчук, Андрій Вікторович; Сердюк, О. А.; Соловйова, Вікторія Володимирівна

URI: https://link.springer.com/chapter/10.1007/978-3-031-14841-5_12
https://doi.org/10.1007/978-3-031-14841-5_12
http://elibrary.kdpu.edu.ua/xmlui/handle/123456789/7028

Date: 2022-09-14

Abstract:

In this paper, at the first time, the analysis of correlational and non-extensive properties of the CO2 emission market relying on the carbon emissions futures time series for the period 04.07.2008–10.05.2021 is performed, and the daily data of the power sector from the U.S. Carbon Monitor for the period 01.01.2019–10.05.2021, which consist the data of both individual countries (USA, Germany, China, India, United Kingdom, et al.) and global emissions (World) are investigated using such approach. To demonstrate the applicability of these methods on systems of another nature and complexity, the analysis of the Dow Jones Industrial Average (DJIA) index is presented. The results show that both futures and the DJIA are presented to be non-extensive, and the distribution of their normalized returns can be better described by power-law probability distributions, particularly, by q-Gaussian. Tsallis triplet for the entire time series of CO2 emissions futures and the DJIA is estimated, and q-triplet as an indicator of crisis phenomena is presented, relying on the sliding window algorithm. It can be seen that the triplet behaves characteristically during economic crises. This study shows that the toolkit of the random matrix theory (RMT) allows to investigate the correlational nature of the carbon emissions market and to build appropriate indicators of crisis phenomena, which clearly reflect the collective dynamics of the entire research base during events of this kind.

Description:

Alptekin, O., Alptekin, N., Saraç, B.: Evaluation of low carbon development of European union countries and turkey using grey relational analysis. Tehnicki Vjesnik 25(5), 1497–1505 (2018). https://doi.org/10.17559/TV-20170126185956 Bielinskyi, A.O., et al.: Predictors of oil shocks. Econophysical approach in environmental science. In: IOP Conference Series: Earth and Environmental Science, vol. 628, p. 012019 (2021). https://doi.org/10.1088/1755-1315/628/1/012019 Cong, R., Lo, A.: Emission trading and carbon market performance in Shenzhen, China. Appl. Energy 193(C), 414–425 (2017). http://EconPapers.repec.org/RePEc:eee:appene:v:193:y:2017:i:c:p:414--425 Dyson, F.J.: Statistical theory of the energy levels of complex systems. I. J. Math. Phys. 3(1), 140–156 (1962). https://doi.org/10.1063/1.1703773 Jiang, M., An, H., Gao, X., Liu, S., Xi, X.: Factors driving global carbon emissions: a complex network perspective. Resour. Conserv. Recycl. 146, 431–440 (2019). https://doi.org/10.1016/j.resconrec.2019.04.012 Jiang, S., Guo, J., Yang, C., Tian, L.: Random matrix analysis of cross-correlation in energy market of Shanxi, China. Int. J. Nonlinear Sci. 23(2), 96–101 (2017) Kantelhardt, J.W., Zschiegner, S.A., Koscielny-Bunde, E., Havlin, S., Bunde, A., Stanley, H.: Multifractal detrended fluctuation analysis of nonstationary time series. Physica A 316(1), 87–114 (2002). https://doi.org/10.1016/S0378-4371(02)01383-3 Karatasou, S., Santamouris, M.: Multifractal analysis of high-frequency temperature time series in the urban environment. Climate 6(2) (2018). https://doi.org/10.3390/cli6020050. http://www.mdpi.com/2225-1154/6/2/50 Kisel’ák, J., Dušek, J., Stehlík, M.: Recurrence of CH4 and CO2 emissions measured by a non-steady state flow-through chamber system. In: AIP Conference Proceedings, vol. 2046, no. 1, p. 020046 (2018). https://doi.org/10.1063/1.5081566 Krishnamurti, C., Hoque, A.: Efficiency of European emissions markets: lessons and implications. Energy Policy 39(10), 6575–6582 (2011). https://doi.org/10.1016/j.enpol.2011.07.062. Sustainability of biofuels Laloux, L., Cizeau, P., Bouchaud, J.P., Potters, M.: Noise dressing of financial correlation matrices. Phys. Rev. Lett. 83(7), 1467–1470 (1999). https://doi.org/10.1103/physrevlett.83.1467 Li, Y.L., Chen, B., Chen, G.Q.: Carbon network embodied in international trade: global structural evolution and its policy implications. Energy Policy 139, 111316 (2020). https://doi.org/10.1016/j.enpol.2020.111316 Liang, J.: Analysis and test of multifractal characteristics of the European carbon emissions market-based on the framework of wavelet leaders. Low Carbon Econ. 07(01), 54–61 (2016). https://doi.org/10.4236/lce.2016.71006 Liu, L., et al.: Household CO2 emissions: current status and future perspectives. Int. J. Environ. Res. Public Health 17(19) (2020). https://doi.org/10.3390/ijerph17197077. http://www.mdpi.com/1660-4601/17/19/7077 Lyra, M.L., Tsallis, C.: Nonextensivity and multifractality in low-dimensional dissipative systems. Phys. Rev. Lett. 80, 53–56 (1998). https://doi.org/10.1103/PhysRevLett.80.53 Marwan, N., Wessel, N., Meyerfeldt, U., Schirdewan, A., Kurths, J.: Recurrence-plot-based measures of complexity and their application to heart-rate-variability data. Phys. Rev. E 66, 026702 (2002). https://doi.org/10.1103/PhysRevE.66.026702 The Mathworks Inc., Natick, Massachusetts: MATLAB version 8.6.0.267246 (R2015b) (2015) Mehta, M.L. (ed.): Random Matrices (Revised and Enlarged Second Edition). Academic Press, San Diego, revised and enlarged 2nd edn. (1991). https://doi.org/10.1016/C2009-0-22297-5 Nikolis, G., Prigogine, I.: Exploring Complexity. An Introduction. W. H, Freeman and Company (1989) Nogueira, D.C.S., et al.: Multifractal and joint multifractal analysis of the spatial variability of CO2 emission and other soil properties. GU General Assembly 2021 (online) (EGU21-16174) (2021). https://doi.org/10.5194/egusphere-egu21-16174 Pavlos, G., et al.: Tsallis non-extensive statistics and solar wind plasma complexity. Physica A 422, 113–135 (2015). https://doi.org/10.1016/j.physa.2014.12.007 Plerou, V., Gopikrishnan, P., Rosenow, B., Amaral, L.A.N., Guhr, T., Stanley, H.E.: Random matrix approach to cross correlations in financial data. Phys. Rev. E 65, 066126 (2002). https://doi.org/10.1103/PhysRevE.65.066126 Shen, J., Zheng, B.: Cross-correlation in financial dynamics. EPL (Europhysics Letters) 86(4), 48005 (2009). https://doi.org/10.1209/0295-5075/86/48005 Soloviev, V., Bielinskyi, A., Kharadzjan, N.: Coverage of the coronavirus pandemic through entropy measures. In: CEUR Workshop Proceedings, vol. 2832, pp. 24–42 (2020) Soloviev, V., Bielinskyi, A., Solovieva, V.: Entropy analysis of crisis phenomena for DJIA index. In: CEUR Workshop Proceedings, vol. 2393, pp. 434–449 (2019) Soloviev, V.N., Belinskiy, A.: Complex systems theory and crashes of cryptocurrency market. In: Ermolayev, V., Suárez-Figueroa, M.C., Yakovyna, V., Mayr, H.C., Nikitchenko, M., Spivakovsky, A. (eds.) ICTERI 2018. CCIS, vol. 1007, pp. 276–297. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-13929-2_14 Soloviev, V., Solovieva, V., Tuliakova, A., Hostryk, A., Pichl, L.: Complex networks theory and precursors of financial crashes. In: CEUR Workshop Proceedings, vol. 2713, pp. 53–67 (2020) Soloviev, V., Yevtushenko, S., Batareyev, V.: Comparative analysis of the cryptocurrency and the stock markets using the Random Matrix Theory. In: CEUR Workshop Proceedings, vol. 2546, pp. 87–100 (2019) Soloviev, V.N., Bielinskyi, A., Serdyuk, O., Solovieva, V., Semerikov, S.: Lyapunov exponents as indicators of the stock market crashes. In: CEUR Workshop Proceedings, vol. 2732, pp. 455–470 (2020) Sparavigna, A.C.: Carbon dioxide concentration and emissions in atmosphere: trends and recurrence plots. Int. J. Sci. 3(10), 8–15 (2014). https://doi.org/10.18483/ijSci.582. http://ideas.repec.org/a/adm/journl/v3y2014i10p8-15.html Suh, D.H.: An entropy approach to regional differences in carbon dioxide emissions: implications for ethanol usage. Sustainability 10(1) (2018). https://doi.org/10.3390/su10010243. http://www.mdpi.com/2071-1050/10/1/243 UNFCCC: Adoption of the Paris agreement (2015). http://unfccc.int/resource/docs/2015/cop21/eng/l09r01.pdf Urama, T., Ezepue, P., Nnanwa, C.: Analysis of cross-correlations in emerging markets using random matrix theory. J. Math. Financ. 7, 291–307 (2017). https://doi.org/10.4236/jmf.2017.72015 Wang, G.J., Xie, C., Chen, S., Han, F.: Cross-correlations between energy and emissions markets: new evidence from fractal and multifractal analysis. Math. Problems Eng. 2014, 1–13 (2014). https://doi.org/10.1155/2014/197069. https://ideas.repec.org/a/hin/jnlmpe/197069.html Wigner, E.P.: On a class of analytic functions from the quantum theory of collisions. Ann. Math. 53(1), 36–67 (1951). http://www.jstor.org/stable/1969342 Zbilut, J.P., Webber, C.L.: Embeddings and delays as derived from quantification of recurrence plots. Phys. Lett. A 171(3), 199–203 (1992). https://doi.org/10.1016/0375-9601(92)90426-M Zou, S., Zhang, T.: Cross-correlation analysis between energy and carbon markets in China based on multifractal theory. Int. J. Low-Carbon Technol. 15(3), 389–397 (2020). https://doi.org/10.1093/ijlct/ctaa010

Show full item record