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  4. Кафедра інформатики та прикладної математики
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Назва: Assessing Energy-related Markets through Multifractal Detrended Cross-correlation Analysis
Автори: Bielinskyi, Andrii
Соловйов, Володимир Миколайович
Семеріков, Сергій Олексійович
Solovieva, Victoria
Matviychuk, Andriy
Kiv, Arnold
Бєлінський, Андрій Олександрович
Соловйова, Вікторія Володимирівна
Матвійчук, Андрій Вікторович
Ків, Арнольд Юхимович
Ключові слова: crude oil
natural gas
sustainable sevelopment
multifractality
multifractal detrended cross-correlation analysis
cross-correlations
Дата публікації: 12-вер-2022
Видавництво: SciTePress
Бібліографічний опис: Bielinskyi A. Assessing Energy-related Markets through Multifractal Detrended Cross-correlation Analysis / Andrii Bielinskyi, Vladimir Soloviev, Serhiy Semerikov, Victoria Solovieva, Andriy Matviychuk, Arnold Kiv // Proceedings of the 5th International Scientific Congress Society of Ambient Intelligence - ISC SAI / Editors : Victoria Solovieva, Serhii Hushko. – Setúbal : SciTePress, 2022. – P. 456-467. – DOI : 10.5220/0011365500003350.
Короткий огляд (реферат): Regulatory actions aimed the sustainable development force ordinary traders, policymakers, institutional investors to develop new types of risk management strategies, seek better decision-making processes that would allow them more effectively reallocate funds when trading and investing in energy markets such as oil and gas. Due to their supply and demand, they are presented to non-equilibrium, chaotic, long-range dependent, etc. Oil and gas play an important role not only in the financial markets, but they are important in many goods and services, and their extensive usage leads to environmental damage. Thus, the dynamics of the corresponding energy-related indices is a useful indicator of the current environmental development, and their modeling is of paramount importance. We have addressed one of the methods of multifractal analysis which is known as Detrended Cross-Correlation Analysis (DCCA) and its multifractal extension (MF-DCCA) to get reliable and efficient indicators of cri tical events in the oil and gas markets. For example, we have taken daily data of Henry Hub natural gas spot prices (US$/MMBTU), WTI spot prices (US$/BBL), and Europe Brent spot prices (US$/BBL) from 7 February 1997 to 14 December 2021. Regarding their (multifractal) cross-correlations, we get such indicators as the DCCA coefficient 𝜌 𝐷𝐶𝐶𝐴 , the cross-correlation Hurst exponent, the maximal, minimal, and mean singularity strength, the width of multifractality, and its asymmetry with the usage of sliding window approach. Our empirical results present that all of the presented indicators respond characteristically during crashes and can be effectively used for modeling current and further perspectives in energy markets and sustainable development indices.
Опис: Zebende, G. (2011). DCCA cross-correlation coefficient: Quantifying level of cross-correlation. Physica A: Statistical Mechanics and Its Applications, 390(4), 614–618. https://doi.org/10.1016/j.physa.2010.10.022 Peng, C. K., Buldyrev, S. V., Havlin, S., Simons, M., Stanley, H. E., & Goldberger, A. L. (1994). Mosaic organization of DNA nucleotides. Physical Review E, 49(2), 1685–1689. https://doi.org/10.1103/physreve.49.1685 Zhou, W. X. (2008). Multifractal detrended cross-correlation analysis for two nonstationary signals. Physical Review E, 77(6). https://doi.org/10.1103/physreve.77.066211 Ito, M. I., & Ohnishi, T. (2020). Evaluation of the Heterogeneous Spatial Distribution of Population and Stores/Facilities by Multifractal Analysis. Frontiers in Physics, 8. https://doi.org/10.3389/fphy.2020.00291 Zhang, X., Liu, H., Zhao, Y., & Zhang, X. (2019). Multifractal detrended fluctuation analysis on air traffic flow time series: A single airport case. Physica A: Statistical Mechanics and Its Applications, 531, 121790. https://doi.org/10.1016/j.physa.2019.121790 Mandelbrot, B. B. (2021). The Fractal Geometry of Nature. Echo Point Books & Media, LLC. Aloui, C., & Mabrouk, S. (2010). Value-at-risk estimations of energy commodities via long-memory, asymmetry and fat-tailed GARCH models. Energy Policy, 38(5), 2326–2339. https://doi.org/10.1016/j.enpol.2009.12.020 Herrera, R., Rodriguez, A., & Pino, G. (2017). Modeling and forecasting extreme commodity prices: A Markov-Switching based extreme value model. Energy Economics, 63, 129–143. https://doi.org/10.1016/j.eneco.2017.01.012 Mandelbrot, B. (1967). The Variation of Some Other Speculative Prices. The Journal of Business, 40(4), 393. https://doi.org/10.1086/295006 Hurst, H. E. (1951). Long-Term Storage Capacity of Reservoirs. Transactions of the American Society of Civil Engineers, 116(1), 770–799. https://doi.org/10.1061/taceat.0006518 Lo, A. W. (1991). Long-Term Memory in Stock Market Prices. Econometrica, 59(5), 1279. https://doi.org/10.2307/2938368 Kantelhardt, J. W., Zschiegner, S. A., Koscielny-Bunde, E., Havlin, S., Bunde, A., & Stanley, H. (2002). Multifractal detrended fluctuation analysis of nonstationary time series. Physica A: Statistical Mechanics and Its Applications, 316(1–4), 87–114. https://doi.org/10.1016/s0378-4371(02)01383-3 Podobnik, B., & Stanley, H. E. (2008). Detrended Cross-Correlation Analysis: A New Method for Analyzing Two Nonstationary Time Series. Physical Review Letters, 100(8). https://doi.org/10.1103/physrevlett.100.084102 Bielinskyi, A. O., Khvostina, I., Mamanazarov, A., Matviychuk, A., Semerikov, S., Serdyuk, O., Solovieva, V., & Soloviev, V. N. (2021b). Predictors of oil shocks. Econophysical approach in environmental science. IOP Conference Series: Earth and Environmental Science, 628(1), 012019. https://doi.org/10.1088/1755-1315/628/1/012019 Bielinskyi, A., Semerikov, S., Serdiuk, O., Solovieva, V., Soloviev, V., & Pichl, L. (2020). Econophysics of sustainability indices. In A. E. Kiv (Ed.), Proceedings of the Selected Papers of the Special Edition of International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2-MLPEED 2020) (pp. 372–392). CEUR-WS.org. Soloviev, V. N., & Belinskiy, A. O. (2019) Complex Systems Theory and Crashes of Cryptocurrency Market. In: Ermolayev V., Suárez-Figueroa M., Yakovyna V., Mayr H., Nikitchenko M., Spivakovsky A. (eds) Information and Communication Technologies in Education, Research, and Industrial Applications. ICTERI 2018. Communications in Computer and Information Science, vol 1007. Springer, Cham. https://doi.org/10.1007/978-3-030-13929-2_14 Hoayek, A., Hamie, H., & Auer, H. (2020). Modeling the Price Stability and Predictability of Post Liberalized Gas Markets Using the Theory of Information. Energies, 13(11), 3012. https://doi.org/10.3390/en13113012 Joo, K., Suh, J. H., Lee, D., & Ahn, K. (2020). Impact of the global financial crisis on the crude oil market. Energy Strategy Reviews, 30, 100516. https://doi.org/10.1016/j.esr.2020.100516 Lautier, D. H., Raynaud, F., & Robe, M. A. (2019). Shock Propagation Across the Futures Term Structure: Evidence from Crude Oil Prices. The Energy Journal, 40(3). https://doi.org/10.5547/01956574.40.3.dlau Hu, Y., Chen, Y., Tang, S., Feng, L., & Huang, C. (2021). An Explanation of Energy Return on Investment From an Entropy Perspective. Frontiers in Energy Research, 9. https://doi.org/10.3389/fenrg.2021.633528 Engelen, S., Norouzzadeh, P., Dullaert, W., & Rahmani, B. (2011). Multifractal features of spot rates in the Liquid Petroleum Gas shipping market. Energy Economics, 33(1), 88–98. https://doi.org/10.1016/j.eneco.2010.05.009 Garnier, J., & Solna, K. (2019). Emergence of turbulent epochs in oil prices. Chaos, Solitons & Fractals, 122, 281–292. https://doi.org/10.1016/j.chaos.2019.03.016 Ali, H., Aslam, F., & Ferreira, P. (2021). Modeling Dynamic Multifractal Efficiency of US Electricity Market. Energies, 14(19), 6145. https://doi.org/10.3390/en14196145 Fang, W., Gao, X., Huang, S., Jiang, M., & Liu, S. (2018). Reconstructing time series into a complex network to assess the evolution dynamics of the correlations among energy prices. Open Physics, 16(1), 346–354. https://doi.org/10.1515/phys-2018-0047 Xu, H., Wang, M., & Yang, W. (2020). Information Linkage between Carbon and Energy Markets: Multiplex Recurrence Network Approach. Complexity, 2020, 1–12. https://doi.org/10.1155/2020/5841609 Kassouri, Y., Bilgili, F., & Kuşkaya, S. (2022). A wavelet-based model of world oil shocks interaction with CO2 emissions in the US. Environmental Science & Policy, 127, 280–292. https://doi.org/10.1016/j.envsci.2021.10.020 Hussain, S. I., Nur-Firyal, R., & Ruza, N. (2021). Linkage transitions between oil and the stock markets of countries with the highest COVID-19 cases. Journal of Commodity Markets, 100236. https://doi.org/10.1016/j.jcomm.2021.100236 Wang, G. J., Xie, C., Chen, S., & Han, F. (2014). Cross-Correlations between Energy and Emissions Markets: New Evidence from Fractal and Multifractal Analysis. Mathematical Problems in Engineering, 2014, 1–13. https://doi.org/10.1155/2014/197069 Zou, S., & Zhang, T. (2020). Cross-correlation analysis between energy and carbon markets in China based on multifractal theory. International Journal of Low-Carbon Technologies, 15(3), 389–397. https://doi.org/10.1093/ijlct/ctaa010 Quintino, D. D., Burnquist, H. L., & Ferreira, P. J. S. (2021). Carbon Emissions and Brazilian Ethanol Prices: Are They Correlated? An Econophysics Study. Sustainability, 13(22), 12862. https://doi.org/10.3390/su132212862 Natural Gas Futures Prices (NYMEX). (1997–2021). [Dataset]. U.S. Energy Information Administration. https://www.eia.gov/dnav/ng/ng_pri_fut_s1_d.htm Spot Prices for Crude Oil and Petroleum Products. (1986–2021). [Dataset]. U.S. Energy Information Administration. https://www.eia.gov/dnav/pet/pet_pri_spt_s1_d.htm Bielinskyi, A. O., Serdyuk, O. A., Semerikov, S. O., & Soloviev, V. N. (2021, December). Econophysics of cryptocurrency crashes: a systematic review. In A. E. Kiv, V. N. Soloviev, & S. O. Semerikov (Eds.), Selected and Revised Papers of 9th International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2-MLPEED 2021) (pp. 31–133). Bielinskyi, A. O., Hushko, S. V., Matviychuk, A. V., Serdyuk, O. A., Semerikov, S. O., & Soloviev, V. N. (2021, December). Irreversibility of financial time series: a case of crisis. In A. E. Kiv, V. N. Soloviev, & S. O. Semerikov (Eds.), Selected and Revised Papers of 9th International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2-MLPEED 2021) (pp. 134–150). Mensi, W., Sensoy, A., Vo, X. V., & Kang, S. H. (2020). Impact of COVID-19 outbreak on asymmetric multifractality of gold and oil prices. Resources Policy, 69, 101829.
URI (Уніфікований ідентифікатор ресурсу): https://www.scitepress.org/Link.aspx?doi=10.5220/0011365500003350
https://doi.org/10.5220/0011365500003350
http://elibrary.kdpu.edu.ua/xmlui/handle/123456789/7029
ISBN: 978-989-758-600-2
Розташовується у зібраннях:Кафедра інформатики та прикладної математики

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