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

Recurrence quantification analysis of energy market crises: a nonlinear approach to risk management

Bielinskyi, Andrii O.; Soloviev, Vladimir N.; Solovieva, Viktoria V.; Семеріков, Сергій Олексійович; Radin, Michael A.; Білінський, Андрій; Соловйов, Володимир Миколайович; Соловйова, Вікторія Володимирівна; Радін, Майкл

URI: https://ceur-ws.org/Vol-3465/paper14.pdf
http://elibrary.kdpu.edu.ua/xmlui/handle/123456789/7733
https://doi.org/10.31812/123456789/7733

Date: 2023-08-28

Abstract:

The energy market is characterized by unstable price dynamics, which challenge the quantitative models of pricing processes and result in abnormal shocks and crashes. We use recurrence quantification analysis (RQA) to analyze and construct indicators of intermittent events in energy indices, where regular patterns are interrupted by chaotic fluctuations, which could signal the onset of crisis events. We apply RQA to daily data of Henry Hub natural gas spot prices, WTI spot prices, and Europe Brent spot prices. Our empirical results show that the recurrence measures capture the distinctive features of crashes and can be used for effective risk management strategies.

Description:

[1] A. Bielinskyi, V. Soloviev, V. Solovieva, S. Semerikov, M. Radin, Recurrence Measures of Complexity in Energy Market Dynamics, in: S. Semerikov, V. Soloviev, A. Matviychuk, V. Kobets, L. Kibalnyk, H. Danylchuk, A. Kiv (Eds.), Proceedings of 10th International Conference on Monitoring, Modeling & Management of Emergent Economy - M3E2, INSTICC, SciTePress, 2023, pp. 122–133. doi:10.5220/0011931800003432. [2] Y.-J. Zhang, Y.-B. Wu, The time-varying spillover effect between WTI crude oil futures returns and hedge funds, International Review of Economics & Finance 61 (2019) 156–169. doi:10.1016/j.iref.2019.02.006. [3] Y. Dong, M. Zhang, R. Zhou, Classification of Network Game Traffic Using Machine Learning, in: H. Yuan, J. Geng, C. Liu, F. Bian, T. Surapunt (Eds.), Geo-Spatial Knowledge and Intelligence, Springer, Singapore, 2018, pp. 134–145. doi:10.1007/978- 981- 13- 0893- 2_15. [4] G. Wu, Y.-J. Zhang, Does China factor matter? An econometric analysis of international crude oil prices, Energy Policy 72 (2014) 78–86. doi:10.1016/j.enpol.2014.04.026. [5] Q. Ji, E. Bouri, D. Roubaud, L. Kristoufek, Information interdependence among energy, cryptocurrency and major commodity markets, Energy Economics 81 (2019) 1042–1055. doi:10.1016/j.eneco.2019.06.005. [6] L. Coleman, Explaining crude oil prices using fundamental measures, Energy Policy 40 (2012) 318–324. doi:10.1016/j.enpol.2011.10.012. [7] R. Sari, U. Soytas, E. Hacihasanoglu, Do global risk perceptions influence world oil prices?, Energy Economics 33 (2011) 515–524. doi:10.1016/j.eneco.2010.12.006. [8] S. Dées, P. Karadeloglou, R. K. Kaufmann, M. Sánchez, Modelling the world oil market: Assessment of a quarterly econometric model, Energy Policy 35 (2007) 178–191. doi:10. 1016/j.enpol.2005.10.017. [9] Y.-J. Zhang, J. Wang, Exploring the WTI crude oil price bubble process using the Markov regime switching model, Physica A: Statistical Mechanics and its Applications 421 (2015) 377–387. doi:10.1016/j.physa.2014.11.051. [10] U. Shahzad, S. K. Jena, A. K. Tiwari, B. Doğan, C. Magazzino, Time-frequency analysis between Bloomberg Commodity Index (BCOM) and WTI crude oil prices, Resources Policy 78 (2022) 102823. doi:10.1016/j.resourpol.2022.102823. [11] T. Yin, Y. Wang, Predicting the price of WTI crude oil futures using artificial intelligence model with chaos, Fuel 316 (2022) 122523. doi:10.1016/j.fuel.2021.122523. [12] Y. Zhang, M. He, D. Wen, Y. Wang, Forecasting crude oil price returns: Can nonlinearity help?, Energy 262 (2023) 125589. doi:10.1016/j.energy.2022.125589. [13] L. Kilian, Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market, American Economic Review 99 (2009) 1053–69. doi:10.1257/ aer.99.3.1053. [14] R. P. Flood, R. J. Hodrick, On Testing for Speculative Bubbles, Journal of Economic Perspectives 4 (1990) 85–101. doi:10.1257/jep.4.2.85. [15] J. Li, Q. Wu, Y. Tian, L. Fan, Monthly Henry Hub natural gas spot prices forecasting using variational mode decomposition and deep belief network, Energy 227 (2021) 120478. doi:10.1016/j.energy.2021.120478. [16] H. Poincaré, The Three-Body Problem and the Equations of Dynamics: Poincaré’s Foundational Work on Dynamical Systems Theory, Astrophysics and Space Science Library, 1 ed., Springer, Cham, 2017. doi:10.1007/978- 3- 319- 52899- 1. [17] F. Takens, Detecting strange attractors in turbulence, in: D. Rand, L.-S. Young (Eds.), Dynamical Systems and Turbulence, Warwick 1980, Springer Berlin Heidelberg, Berlin, Heidelberg, 1981, pp. 366–381. doi:10.1007/BFb0091924. [18] J.-P. Eckmann, D. Ruelle, Ergodic theory of chaos and strange attractors, Rev. Mod. Phys. 57 (1985) 617–656. doi:10.1103/RevModPhys.57.617. [19] H. Kantz, T. Schreiber, Nonlinear Time Series Analysis, 2 ed., Cambridge University Press, 2003. doi:10.1017/CBO9780511755798. [20] E. Ott, T. Sauer, J. A. Yorke, Coping with Chaos, Wiley Series in Nonlinear Science, Wiley, 1994. [21] J.-P. Eckmann, S. O. Kamphorst, D. Ruelle, Recurrence Plots of Dynamical Systems, Europhysics Letters (EPL) 4 (1987) 973–977. doi:10.1209/0295- 5075/4/9/004. [22] C. L. Webber, J. P. Zbilut, Recurrence quantification analysis of nonlinear dynamical systems, in: M. A. Riley, G. C. V. Orden (Eds.), Tutorials in Contemporary Nonlinear Methods for the Behavioral Sciences, National Science Foundation (NSF), 2005, pp. 26–94. [23] L. W. Charles, C. Ioana, N. Marwan (Eds.), Recurrence Plots and Their Quantifications: Expanding Horizons: Proceedings of the 6th International Symposium on Recurrence Plots, Grenoble, France, 17-19 June 2015, volume 180 of Springer Proceedings in Physics, Springer, Cham, 2015. doi:10.1007/978- 3- 319- 29922- 8. [24] N. Marwan, M. Carmen Romano, M. Thiel, J. Kurths, Recurrence plots for the analysis of complex systems, Physics Reports 438 (2007) 237–329. doi:10.1016/j.physrep.2006.11. 001. [25] C. L. Webber, J. P. Zbilut, Dynamical assessment of physiological systems and states using recurrence plot strategies, Journal of Applied Physiology 76 (1994) 965–973. doi:10.1152/ jappl.1994.76.2.965. [26] J. P. Zbilut, C. L. Webber, Embeddings and delays as derived from quantification of recurrence plots, Physics Letters A 171 (1992) 199–203. doi:10.1016/0375- 9601(92)90426- M. [27] N. Marwan, N. Wessel, U. Meyerfeldt, A. Schirdewan, J. Kurths, Recurrence-plot-based measures of complexity and their application to heart-rate-variability data, Phys. Rev. E 66 (2002) 026702. doi:10.1103/PhysRevE.66.026702. [28] A. L. Goldberger, L. A. N. Amaral, L. Glass, J. M. Hausdorff, P. C. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C.-K. Peng, H. E. Stanley, PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals, Circulation 101 (2000) e215–e220. doi:10.1161/01.CIR.101.23.e215. [29] M. Kirchner, P. Schubert, M. Liebherr, C. T. Haas, Detrended Fluctuation Analysis and Adaptive Fractal Analysis of Stride Time Data in Parkinson’s Disease: Stitching Together Short Gait Trials, PLOS ONE 9 (2014) 1–6. doi:10.1371/journal.pone.0085787. [30] N. Marwan, C. L. Webber, Mathematical and Computational Foundations of Recurrence Quantifications, in: C. L. Webber, N. Marwan (Eds.), Recurrence Quantification Analysis: Theory and Best Practices, Springer International Publishing, Cham, 2015, pp. 3–43. doi:10. 1007/978- 3- 319- 07155- 8_1. [31] U.S. Energy Information Administration, Natural gas spot and futures prices (nymex), 1997. URL: https://www.eia.gov/dnav/ng/NG_PRI_FUT_S1_W.htm. [32] U.S. Energy Information Administration, Spot prices for crude oil and petroleum products, 1986. URL: https://www.eia.gov/dnav/pet/pet_pri_spt_s1_d.htm. [33] V. N. Soloviev, A. O. Bielinskyi, N. A. Kharadzjan, Coverage of the coronavirus pandemic through entropy measures, CEUR Workshop Proceedings 2832 (2020) 24–42. URL: https: //ceur-ws.org/Vol-2832/paper02.pdf. [34] A. O. Bielinskyi, V. N. Soloviev, Complex network precursors of crashes and critical events in the cryptocurrency market, CEUR Workshop Proceedings 2292 (2018) 37 – 45. URL: https://ceur-ws.org/Vol-2292/paper02.pdf. [35] A. O. Bielinskyi, A. V. Matviychuk, O. A. Serdyuk, S. O. Semerikov, V. V. Solovieva, V. N. Soloviev, Correlational and Non-extensive Nature of Carbon Dioxide Pricing Market, in: O. Ignatenko, V. Kharchenko, V. Kobets, H. Kravtsov, Y. Tarasich, V. Ermolayev, D. Esteban, V. Yakovyna, A. Spivakovsky (Eds.), ICTERI 2021 Workshops, volume 1635 CCIS of Communications in Computer and Information Science, Springer International Publishing, Cham, 2022, pp. 183–199. doi:10.1007/978- 3- 031- 14841- 5_12. [36] A. O. Bielinskyi, O. A. Serdyuk, S. O. Semerikov, V. N. Soloviev, Econophysics of cryptocurrency crashes: a systematic review, in: A. E. Kiv, V. N. Soloviev, S. O. Semerikov (Eds.), 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, CEUR-WS.org, 2021, pp. 31–133. URL: http://ceur-ws.org/Vol-3048/paper03.pdf. [37] A. O. Bielinskyi, S. V. Hushko, A. V. Matviychuk, O. A. Serdyuk, S. O. Semerikov, V. N. Soloviev, Irreversibility of financial time series: a case of crisis, in: A. E. Kiv, V. N. Soloviev, S. O. Semerikov (Eds.), 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, CEUR-WS.org, 2021, pp. 134–150. URL: http://ceur-ws.org/Vol-3048/paper04. pdf. [38] A. Bielinskyi, S. Semerikov, O. Serdyuk, V. Solovieva, V. N. Soloviev, L. Pichl, Econophysics of sustainability indices, in: A. Kiv (Ed.), 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, volume 2713 of CEUR Workshop Proceedings, CEUR-WS.org, 2020, pp. 372–392. URL: http://ceur-ws.org/Vol-2713/ paper41.pdf. [39] T. Fang, C. Zheng, D. Wang, Forecasting the crude oil prices with an EMD-ISBM-FNN model, Energy 263 (2023) 125407. doi:10.1016/j.energy.2022.125407. [40] Y. Zou, L. Yu, K. He, Forecasting crude oil risk: A multiscale bidirectional generative adversarial network based approach, Expert Systems with Applications 212 (2023) 118743. doi:10.1016/j.eswa.2022.118743. [41] H. Guliyev, E. Mustafayev, Predicting the changes in the WTI crude oil price dynamics using machine learning models, Resources Policy 77 (2022) 102664. doi:10.1016/j.resourpol. 2022.102664. [42] A. E. Kiv, V. N. Soloviev, S. O. Semerikov, H. B. Danylchuk, L. O. Kibalnyk, A. V. Matviychuk, A. M. Striuk, Machine learning for prediction of emergent economy dynamics III, in: A. E. Kiv, V. N. Soloviev, S. O. Semerikov (Eds.), 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, CEUR-WS.org, 2021, pp. i–xxxi. URL: http://ceur-ws.org/Vol-3048/ paper00.pdf. [43] A. Bielinskyi, V. Soloviev, S. Semerikov, V. Solovieva, Identifying stock market crashes by fuzzy measures of complexity, Neiro-Nechitki Tekhnolohii Modelyuvannya v Ekonomitsi 2021 (2021) 3–45. doi:10.33111/nfmte.2021.003. [44] M. Bondarenko, Modeling relation between at-the-money local volatility and realized volatility of stocks, Neiro-Nechitki Tekhnolohii Modelyuvannya v Ekonomitsi 2021 (2021) 46–66. doi:10.33111/nfmte.2021.046. [45] T. Kmytiuk, G. Majore, Time series forecasting of agricultural product prices using Elman and Jordan recurrent neural networks, Neiro-Nechitki Tekhnolohii Modelyuvannya v Ekonomitsi 2021 (2021) 67–85. doi:10.33111/nfmte.2021.067. [46] V. Kobets, O. Novak, EU countries clustering for the state of food security using machine learning techniques, Neiro-Nechitki Tekhnolohii Modelyuvannya v Ekonomitsi 2021 (2021) 86–118. doi:10.33111/nfmte.2021.086. [47] H. Kucherova, Y. Honcharenko, D. Ocheretin, O. Bilska, 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 (2021) 119–135. doi:10.33111/nfmte.2021.119. [48] D. Lukianenko, I. Strelchenko, Neuromodeling of features of crisis contagion on financial markets between countries with different levels of economic development, Neiro-Nechitki Tekhnolohii Modelyuvannya v Ekonomitsi 2021 (2021) 136–163. doi:10.33111/nfmte. 2021.136. [49] I. Miroshnychenko, T. Kravchenko, Y. Drobyna, Forecasting electricity generation from renewable sources in developing countries (on the example of Ukraine), Neiro-Nechitki Tekhnolohii Modelyuvannya v Ekonomitsi 2021 (2021) 164–198. doi:10.33111/nfmte.2021.164. [50] S. Ashe, P. Egan, Examining financial and business cycle interaction using cross recurrence plot analysis, Finance Research Letters 51 (2023) 103461. doi:10.1016/j.frl.2022. 103461. [51] Q. He, J. Huang, A method for analyzing correlation between multiscale and multivariate systems—Multiscale multidimensional cross recurrence quantification (MMDCRQA), Chaos, Solitons & Fractals 139 (2020) 110066. doi:10.1016/j.chaos.2020.110066. [52] M. C. Romano, M. Thiel, J. Kurths, W. von Bloh, Multivariate recurrence plots, Physics Letters A 330 (2004) 214–223. doi:10.1016/j.physleta.2004.07.066.

Show full item record