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http://elibrary.kdpu.edu.ua/xmlui/handle/123456789/7410| Назва: | Recurrence Measures of Complexity in Energy Market Dynamics |
| Автори: | Bielinskyi, Andrii O. Soloviev, Vladimir N. Solovieva, Viktoria V. Семеріков, Сергій Олексійович Radin, Michael Соловйов, Володимир Миколайович |
| Ключові слова: | crude oil natural gas recurrence plot recurrence quantification analysis crash indicator-precursor |
| Дата публікації: | 27-тра-2023 |
| Видавництво: | SciTePress |
| Бібліографічний опис: | Bielinskyi A. O. Recurrence Measures of Complexity in Energy Market Dynamics / Andrii O. Bielinskyi, Vladimir N. Soloviev, Viktoria V. Solovieva, Serhiy O. Semerikov, Michael Radin // Proceedings of 10th International Conference on Monitoring, Modeling & Management of Emergent Economy – M3E2. Odessa – Ukraine. November 17 - 18, 2022 / Editors: Serhiy Semerikov, Vladimir Soloviev, Andriy Matviychuk, Vitaliy Kobets, Liubov Kibalnyk, Hanna Danylchuk, Arnold Kiv. – Setúbal : SciTePress, 2023. – P. 122-133. – DOI : 10.5220/0011931800003432 |
| Короткий огляд (реферат): | The instability of the price dynamics of the energy market from a theoretical point of view indicates the inadequacy of the dominant paradigm of the quantitative description of pricing processes, and from a practical point of view, it leads to abnormal shocks and crashes. Through the recurrence quantification analysis, we analyze and construct indicators of intermittent events in energy indices, where periods of regular behavior are replaced by periods of chaotic behavior, which could explain the emergence of crisis events. For further analysis, we have chosen daily data of Henry Hub natural gas spot prices, WTI spot prices, and Europe Brent spot prices. Our empirical results present that all of the presented recurrence measures respond in a particular way during crashes and can be effectively implemented for risk management strategies. |
| Опис: | Ashe, S. and Egan, P. (2023). Examining financial and business cycle interaction using cross recurrence plot analysis. Finance Research Letters, 51:103461. https://doi.org/10.1016/j.frl.2022.103461. Bielinskyi, A., Semerikov, S., Serdyuk, O., Solovieva, V., Soloviev, V. N., and Pichl, L. (2020). Econophysics of sustainability indices. In Kiv, A., editor, Proceedings of the Selected Papers of the Special Edition of International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2-MLPEED 2020), Odessa, Ukraine, July 13-18, 2020, vol. 2713 of CEUR Workshop Proceedings, pages 372–392. CEUR-WS.org. https://ceur-ws.org/Vol-2713/paper41.pdf. Bielinskyi, A., Soloviev, V., Semerikov, S., and Solovieva, V. (2021a). Identifying stock market crashes by fuzzy measures of complexity. Neiro-Nechitki Tekhnolohii Modelyuvannya v Ekonomitsi, 2021(10):3–45. https://doi.org/10.33111/nfmte.2021.003. Bielinskyi, A. O., Hushko, S. V., Matviychuk, A. V., Serdyuk, O. A., Semerikov, S. O., and Soloviev, V. N. (2021b). Irreversibility of financial time series: a case of crisis. In Kiv, A. E., Soloviev, V. N., and Semerikov, S. O., editors, Proceedings of the Selected and Revised Papers of 9th International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2-MLPEED 2021), Odessa, Ukraine, May 26-28, 2021, volume 3048 of CEUR Workshop Proceedings, pages 134–150. CEUR-WS.org. https://ceur-ws.org/Vol-3048/paper04.pdf. Bielinskyi, A. O., Matviychuk, A. V., Serdyuk, O. A., Semerikov, S. O., Solovieva, V. V., and Soloviev, V. N. (2022). Correlational and non-extensive nature of carbon dioxide pricing market. In Ignatenko, O., Kharchenko, V., Kobets, V., Kravtsov, H., Tarasich, Y., Ermolayev, V., Esteban, D., Yakovyna, V., and Spivakovsky, A., editors, ICTERI 2021 Workshops, volume 1635 CCIS of Communications in Computer and Information Science, pages 183–199, Cham. Springer International Publishing. https://doi.org/10.1007/978-3-031-14841-5 12. Bielinskyi, A. O., Serdyuk, O. A., Semerikov, S. O., and Soloviev, V. N. (2021c). Econophysics of cryptocurrency crashes: a systematic review. In Kiv, A. E., Soloviev, V. N., and Semerikov, S. O., editors, Proceedings of the Selected and Revised Papers of 9th International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2-MLPEED 2021), Odessa, Ukraine, May 26-28, 2021, volume 3048 of CEUR Workshop Proceedings, pages 31–133. CEUR-WS.org. https://ceur-ws.org/Vol-3048/paper03.pdf. Bielinskyi, A. O. and Soloviev, V. N. (2018). Complex network precursors of crashes and critical events in the cryptocurrency market. CEUR Workshop Proceedings, 2292:37 – 45. https://ceur-ws.org/Vol-2292/paper02.pdf. Bondarenko, M. (2021). Modeling relation between atthe-money local volatility and realized volatility of stocks. Neiro-Nechitki Tekhnolohii Modelyuvannya v Ekonomitsi, 2021(10):46–66. https://doi.org/10.33111/nfmte.2021.046. Charles, L., Webber, J., Cornel, I., and Norbert, M., editors (2015). Recurrence Plots and Their Quantifications: Expanding Horizons, vol.180 of Springer Proceedings in Physics. Springer. https://doi.org/10.1007/978-3-319-29922-8. Coleman, L. (2012). Explaining crude oil prices using fundamental measures. Energy Policy, 40:318–324. https://doi.org/10.1016/j.enpol.2011.10.012. Dong, Y., Zhang, M., and Zhou, R. (2018). Classification of network game traffic using machine learning. In Yuan, H., Geng, J., Liu, C., Bian, F., and Surapunt, T., editors, Geo-Spatial Knowledge and Intelligence, pages 134–145, Singapore. Springer Singapore. https://doi.org/10.1007/978-981-13-0893-2 15. Dées, S., Karadeloglou, P., Kaufmann, R. K., and Sánchez, M. (2007). Modelling the world oil market: Assessment of a quarterly econometric model. Energy Policy, 35(1):178–191. https://doi.org/10.1016/j.enpol.2005.10.017. Eckmann, J.-P., Kamphorst, S. O., and Ruelle, D. (1987). Recurrence plots of dynamical systems. Europhysics Letters (EPL), 4(9):973–977. https://dx.doi.org/10.1209/0295-5075/4/9/004. Eckmann, J.-P. and Ruelle, D. (1985). Ergodic theory of chaos and strange attractors. Rev. Mod. Phys., 57:617–656. https://doi.org/10.1103/RevModPhys.57.617. Fang, T., Zheng, C., and Wang, D. (2023). Forecasting the crude oil prices with an emd-isbm-fnn model. Energy, 263:125407. https://doi.org/10.1016/j.energy.2022.125407. Flood, R. P. and Hodrick, R. J. (1990). On testing for speculative bubbles. Journal of Economic Perspectives, 4(2):85–101. https://www.aeaweb.org/articles?id=10.1257/jep.4.2.85. Goldberger, A. L., Amaral, L. A. N., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., Mietus, J. E., Moody, G. B., Peng, C.-K., and Stanley, H. E. (2000). Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals. Circulation, 101(23):e215–e220. https://doi.org/10.1161/01.cir.101.23.e215. Guliyev, H. and Mustafayev, E. (2022). Predicting the changes in the wti crude oil price dynamics using machine learning models. Resources Policy, 77:102664. https://doi.org/10.1016/j.resourpol.2022.102664. He, Q. and Huang, J. (2020). A method for analyzing correlation between multiscale and multivariate systems—multiscale multidimensional cross recurrence quantification (mmdcrqa). Chaos, Solitons & Fractals, 139:110066. https://doi.org/10.1016/j.chaos.2020.110066. Ji, Q., Bouri, E., Roubaud, D., and Kristoufek, L. (2019). Information interdependence among energy, cryptocurrency and major commodity markets. Energy Economics, 81:1042–1055. https://doi.org/10.1016/j.eneco.2019.06.005. Kantz, H. and Schreiber, T. (2003). Nonlinear Time Series Analysis. Cambridge University Press, 2 edition. https://doi.org/10.1017/CBO9780511755798. Kilian, L. (2009). Not all oil price shocks are alike: Disentangling demand and supply shocks in the crude oil market. American Economic Review, 99(3):1053–69. https://www.aeaweb.org/articles?id=10.1257/aer.99.3.1053. Kirchner, M., Schubert, P., Liebherr, M., and Haas, C. T. (2014). Detrended fluctuation analysis and adaptive fractal analysis of stride time data in parkinson’s disease: Stitching together short gait trials. PLOS ONE, 9(1):1–6. https://doi.org/10.1371/journal.pone.0085787. Kiv, A. E., Soloviev, V. N., Semerikov, S. O., Danylchuk, H. B., Kibalnyk, L. O., Matviychuk, A. V., and Striuk, A. M. (2021). Machine learning for prediction of emergent economy dynamics III. In Kiv, A. E., Soloviev, V. N., and Semerikov, S. O., editors, Proceedings of the Selected and Revised Papers of 9th International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2-MLPEED 2021), Odessa, Ukraine, May 26-28, 2021, volume 3048 of CEUR Workshop Proceedings, pages i–xxxi. CEUR-WS.org. https://ceur-ws.org/Vol-3048/paper00.pdf. Kmytiuk, T. and Majore, G. (2021). Time series forecasting of agricultural product prices using Elman and Jordan recurrent neural networks. Neiro-Nechitki Tekhnolohii Modelyuvannya v Ekonomitsi, 2021(10):67–85. https://doi.org/10.33111/nfmte.2021.067. Kobets, V. and Novak, O. (2021). EU countries clustering for the state of food security using machine learning techniques. Neiro-Nechitki Tekhnolohii Modelyuvannya v Ekonomitsi, 2021(10):86–118. https://doi.org/10.33111/nfmte.2021.086. Kucherova, H., Honcharenko, Y., Ocheretin, D., and Bilska, O. (2021). Fuzzy logic model of usability of websites of higher education institutions in the context of digitalization of educational services. Neiro-Nechitki Tekhnolohii Modelyuvannya v Ekonomitsi, 2021(10):119–135. https://doi.org/10.33111/nfmte.2021.119. Li, J., Wu, Q., Tian, Y., and Fan, L. (2021). Monthly henry hub natural gas spot prices forecasting using variational mode decomposition and deep belief network. Energy, 227:120478. https://doi.org/10.1016/j.energy.2021.120478. Lukianenko, D. and Strelchenko, I. (2021). Neuromodeling of features of crisis contagion on financial markets between countries with different levels of economic development. Neiro-Nechitki Tekhnolohii Modelyuvannya v Ekonomitsi, 2021(10):136–163. https://doi.org/10.33111/nfmte.2021.136. Marwan, N., Carmen Romano, M., Thiel, M., and Kurths, J. (2007). Recurrence plots for the analysis of complex systems. Physics Reports, 438(5):237–329. https://doi.org/10.1016/j.physrep.2006.11.001. Marwan, N. and Webber, C. L. (2015). Mathematical and computational foundations of recurrence quantifications. In Webber, C. L. and Marwan, N., editors, Recurrence Quantification Analysis: Theory and Best Practices, pages 3–43. Springer International Publishing, Cham. https://doi.org/10.1007/978-3-319-07155-8 1. Marwan, N., Wessel, N., Meyerfeldt, U., Schirdewan, A., and Kurths, J. (2002). Recurrence-plot-based measures of complexity and their application to heart-rate-variability data. Phys. Rev. E, 66:026702. https://doi.org/10.1103/physreve.66.026702. Miroshnychenko, I., Kravchenko, T., and Drobyna, Y. (2021). Forecasting electricity generation from renewable sources in developing countries (on the example of Ukraine). Neiro-Nechitki Tekhnolohii Modelyuvannya v Ekonomitsi, 2021(10):164–198. https://doi.org/10.33111/nfmte.2021.164. Ott, E., Sauer, T., and Yorke, J. (1994). Coping with Chaos. Wiley Series in Nonlinear Science. Wiley. Poincaré, H. (2017). The Three-Body Problem and the Equations of Dynamics: Poincaré’s Foundational Work on Dynamical Systems Theory. Astrophysics and Space Science Library. Springer, Cham, 1 edition. https://doi.org/10.1007/978-3-319-52899-1. Romano, M. C., Thiel, M., Kurths, J., and von Bloh, W. (2004). Multivariate recurrence plots. Physics Letters A, 330(3):214–223. https://doi.org/10.1016/j.physleta.2004.07.066. Sari, R., Soytas, U., and Hacihasanoglu, E. (2011). Do global risk perceptions influence world oil prices? Energy Economics, 33(3):515–524. https://doi.org/10.1016/j.eneco.2010.12.006. Shahzad, U., Jena, S. K., Tiwari, A. K., Doğan, B., and Magazzino, C. (2022). Time-frequency analysis between bloomberg commodity index (bcom) and wti crude oil prices. Resources Policy, 78:102823. https://doi.org/10.1016/j.resourpol.2022.102823. Soloviev, V. N., Bielinskyi, A. O., and Kharadzjan, N. A. (2020). Coverage of the coronavirus pandemic through entropy measures. CEUR Workshop Proceedings, 2832:24 – 42. https://ceur-ws.org/Vol-2832/paper02.pdf. Takens, F. (1981). Detecting strange attractors in turbulence. In Rand, D. and Young, L.-S., editors, Dynamical Systems and Turbulence, Warwick 1980, pages 366–381, Berlin, Heidelberg. Springer Berlin Heidelberg. https://doi.org/10.1007/BFb0091924. U.S. Energy Information Administration (1986). Spot prices for crude oil and petroleum products. https://www.eia.gov/dnav/pet/pet pri spt s1 d.htm. U.S. Energy Information Administration (1997). Natural gas spot and futures prices (nymex). https://www.eia.gov/dnav/ng/NG PRI FUT S1 W.htm. Webber, C. and Zbilut, J. (2005). Recurrence quantification analysis of nonlinear dynamical systems. In Riley, M. A. and Orden, G. C. V., editors, Tutorials in Contemporary Nonlinear Methods for the Behavioral Sciences, chapter 2. National Science Foundation (NSF). Webber, C. L. and Zbilut, J. P. (1994). Dynamical assessment of physiological systems and states using recurrence plot strategies. Journal of Applied Physiology, 76(2):965–973. https://doi.org/10.1152/jappl.1994.76.2.965. Wu, G. and Zhang, Y.-J. (2014). Does china factor matter? an econometric analysis of international crude oil prices. Energy Policy, 72:78–86. https://doi.org/10.1016/j.enpol.2014.04.026. Yin, T. and Wang, Y. (2022). Predicting the price of wti crude oil futures using artificial intelligence model with chaos. Fuel, 316:122523. https://doi.org/10.1016/j.fuel.2021.122523. Zbilut, J. P. and Webber, C. L. (1992). Embeddings and delays as derived from quantification of recurrence plots. Physics Letters A, 171(3):199–203. https://doi.org/10.1016/0375-9601(92)90426-M. Zhang, Y., He, M., Wen, D., and Wang, Y. (2023). Forecasting crude oil price returns: Can nonlinearity help? Energy, 262:125589. https://doi.org/10.1016/j.energy.2022.125589. Zhang, Y.-J. and Wang, J. (2015). Exploring the wti crude oil price bubble process using the markov regime switching model. Physica A: Statistical Mechanics and its Applications, 421:377–387. https://doi.org/10.1016/j.physa.2014.11.051. Zhang, Y.-J. and Wu, Y.-B. (2019). The time-varying spillover effect between wti crude oil futures returns and hedge funds. International Review of Economics & Finance, 61:156–169. https://doi.org/10.1016/j.iref.2019.02.006. Zou, Y., Yu, L., and He, K. (2023). Forecasting crude oil risk: A multiscale bidirectional generative adversarial network based approach. Expert Systems with Applications, 212:118743. https://doi.org/10.1016/j.eswa.2022.118743. |
| URI (Уніфікований ідентифікатор ресурсу): | https://doi.org/10.5220/0011931800003432 https://www.scitepress.org/Link.aspx?doi=10.5220/0011931800003432 http://elibrary.kdpu.edu.ua/xmlui/handle/123456789/7410 |
| ISBN: | 978-989-758-640-8 |
| ISSN: | 2975-9234 |
| Розташовується у зібраннях: | Кафедра інформатики та прикладної математики |
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