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Multi-scale pattern causality of the price fluctuation in energy stock market

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Abstract

Pattern causality, as a nonlinear causality test method, can reveal the positive, negative and dark causal relations between time series and evaluate the causal strength quantitatively. In this study, we provide a comprehensive understanding about the nonlinear pattern causality of energy stock market from a multiscale perspective. By synthetically adopting the maximal overlap discrete wavelet transformation (MODWT) method, pattern causality (PC) theory and network analysis method, a systemic framework is constructed, and some interesting results are obtained. (1) Three types of causal strength exhibit inverted U-shape with the increase of time scales, and dark causality accounts for a relatively high percentage. (2) The dark causal strength is strongest in D5 (32–64 days) and D6 (64–128 days), meaning that the uncertainty of the causal relationships in energy stock market obviously increases in the long term. (3) The coal-related stocks have high influence, sensitivity and intermediary, which deserve more attention from investors. This work can provide valuable suggestions for regulators and investors with different risk preferences.

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Data availability

The datasets are available from the corresponding author on reasonable request.

References

  1. Jiang, Y.H., Wang, J.R., Lie, J.Y., Mo, B.: Dynamic dependence nexus and causality of the renewable energy stock markets on the fossil energy markets. Energy 233, 14 (2021)

    Google Scholar 

  2. Shao, L.G., Zhang, H., Chen, J.Y., Zhu, X.H.: Effect of oil price uncertainty on clean energy metal stocks in China: evidence from a nonparametric causality-in-quantiles approach. Int. Rev. Econ. Finance 73, 407–419 (2021)

    Google Scholar 

  3. Stavroglou, S.K., Pantelous, A.A., Stanley, H.E., Zuev, K.M.: Hidden interactions in financial markets. Proc. Natl. Acad. Sci. U.S.A. 116, 10646–10651 (2019)

    Google Scholar 

  4. Dong, M.Y., Chang, C.P., Gong, Q., Chu, Y.: Revisiting global economic activity and crude oil prices: a wavelet analysis. Econ. Model. 78, 134–149 (2019)

    Google Scholar 

  5. Barunik, J., Kocenda, E., Vacha, L.: Gold, oil, and stocks: dynamic correlations. Int. Rev. Econ. Finance 42, 186–201 (2016)

    Google Scholar 

  6. Huang, S.P., An, H.Z., Huang, X., Wang, Y.: Do all sectors respond to oil price shocks simultaneously? Appl. Energ. 227, 393–402 (2018)

    Google Scholar 

  7. Nie, D., Li, Y.B., Li, X.Y.: Dynamic spillovers and asymmetric spillover effect between the carbon emission trading market, fossil energy market, and new energy stock market in China. Energies 14(19), 1–22 (2021)

    Google Scholar 

  8. Liu, X.Y., Jiang, C.: Multi-scale features of volatility spillover networks: a case study of China’s energy stock market. Chaos 30, 10 (2020). https://doi.org/10.1063/1.5131066

    Article  MathSciNet  Google Scholar 

  9. Zheng, B.A., Zhang, Y.Q.W., Yin, H.T., Geng, Y.: The limited role of stock market in financing new energy development in China: an investigation using firms’ high-frequency data. Econ. Anal. Pol. 72, 651–667 (2021)

    Google Scholar 

  10. Espinosa-Paredes, G., Rodriguez, E., Alvarez-Ramirez, J.: A singular value decomposition entropy approach to assess the impact of Covid-19 on the informational efficiency of the WTI crude oil market. Chaos Soliton. Fract. 160, 112238 (2022)

    Google Scholar 

  11. Peng, Y.F., Chen, W.D., Wei, P.B., Yu, G.Y.: Spillover effect and Granger causality investigation between China’s stock market and international oil market: A dynamic multiscale approach. J. Comput. Appl. Math. 367, 112460 (2020)

    MathSciNet  Google Scholar 

  12. Zhang, H., Cai, G.X., Yang, D.X.: The impact of oil price shocks on clean energy stocks: fresh evidence from multi-scale perspective. Energy 196, 117099 (2020)

    Google Scholar 

  13. Bekiros, S., Nguyen, D.K., Sandoval, L., Uddin, G.S.: Information diffusion, cluster formation and entropy-based network dynamics in equity and commodity markets. Eur. J. Oper. Res. 256, 945–961 (2017)

    Google Scholar 

  14. Nie, C.X.: Dynamics of the price-volume information flow based on surrogate time series. Chaos 31, 11 (2021)

    MathSciNet  Google Scholar 

  15. Caporin, M., Costola, M.: Time-varying Granger causality tests in the energy markets: A study on the DCC-MGARCH Hong test. Energy Econ. 111, 106088 (2022)

    Google Scholar 

  16. Lv, X., Dong, X.Y., Dong, W.J.: Oil prices and stock prices of clean energy: new evidence from Chinese subsectoral data. Emerg. Mark. Finance Trade 57, 1088–1102 (2021)

    Google Scholar 

  17. Li, S.F., Zhang, H., Yuan, D.: Investor attention and crude oil prices: Evidence from nonlinear Granger causality tests. Energy Econ. 84, 104494 (2019). https://doi.org/10.1016/j.eneco.2019.104494

    Article  Google Scholar 

  18. Vyrost, T., Lyocsa, S., Baumohl, E.: Granger causality stock market networks: temporal proximity and preferential attachment. Physica A 427, 262–276 (2015)

    Google Scholar 

  19. Qiao, Z., Lam, K.S.J.A.: Granger causal relations among Greater China stock markets: a nonlinear perspective. Appl. Finance Econ. 21, 1437–1450 (2011)

    Google Scholar 

  20. Breitung, J., Candelon, B.: Testing for short- and long-run causality: a frequency-domain approach. J. Econom. 132, 363–378 (2006)

    MathSciNet  Google Scholar 

  21. He, Z.F.: Dynamic impacts of crude oil price on Chinese investor sentiment: nonlinear causality and time-varying effect. Int. Rev. Econ. Finance 66, 131–153 (2020)

    MathSciNet  Google Scholar 

  22. Tao, C.Y., Feng, J.F.: Nonlinear association criterion, nonlinear Granger causality and related issues with applications to neuroimage studies. J. Neurosci. Methods 262, 110–132 (2016)

    Google Scholar 

  23. Zhao, L.L., Wen, F.H., Wang, X.: Interaction among China carbon emission trading markets: nonlinear Granger causality and time-varying effect. Energy Econ. 91, 104901 (2020)

    Google Scholar 

  24. Xiao, D., Wang, J.: Dynamic complexity and causality of crude oil and major stock markets. Energy 193, 747–766 (2020)

    Google Scholar 

  25. Lin, B.Q., Chen, Y.F.: Dynamic linkages and spillover effects between CET market, coal market and stock market of new energy companies: a case of Beijing CET market in China. Energy 172, 1198–1210 (2019)

    Google Scholar 

  26. Liu, X.Y., An, H.Z., Huang, S.P., Wen, S.B.: The evolution of spillover effects between oil and stock markets across multi-scales using a wavelet-based GARCH-BEKK model. Physica A 465, 374–383 (2017)

    Google Scholar 

  27. He, F., Liu, Z.F., Chen, S.C.: Industries return and volatility spillover in Chinese stock market: An early warning signal of systemic risk. IEEE Access 7, 9046–9056 (2019)

    Google Scholar 

  28. Wang, Z., Gao, X.Y., An, H.Z., Tang, R.W., Sun, Q.R.: Identifying influential energy stocks based on spillover network. Int. Rev. Finan. Anal. 68, 101277 (2020)

    Google Scholar 

  29. Wu, T., Gao, X.Y., An, S.F., Liu, S.Y.: Time-varying pattern causality inference in global stock markets. Int. Rev. Finan. Anal. 77, 101806 (2021)

    Google Scholar 

  30. Wu, T., Gao, X.Y., An, S.F., Liu, S.Y.: Diverse causality inference in foreign exchange markets. Int. J. Bifurc. Chaos 31, 2150070 (2021)

    Google Scholar 

  31. Sun, X.T., Fang, W., Gao, X.Y., An, H.Z., Liu, S.Y., Wu, T.: Complex causalities between the carbon market and the stock markets for energy intensive industries in China. Int. Rev. Econ. Finance 78, 404–417 (2022)

    Google Scholar 

  32. Goupillaud, P., Grossmann, A., Morlet, J.J.G.: Cycle-octave and related transforms in seismic signal analysis. Geoexploration 23, 85–102 (1984)

    Google Scholar 

  33. Grossmann, A., Morlet, J.J.: Sjoma: decomposition of Hardy functions into square integrable wavelets of constant shape. SIAM J. Math. Anal. 15, 723–736 (1984)

    MathSciNet  Google Scholar 

  34. Wang, X.Y., Han, X.J., Chen, Z.Y., Bi, Q.S., Guan, S.G., Zou, Y.: Multi-scale transition network approaches for nonlinear time series analysis. Chaos Soliton. Fract. 159, 112026 (2022)

    MathSciNet  Google Scholar 

  35. Reboredo, J.C., Rivera-Castro, M.A., Ugolini, A.: Wavelet-based test of co-movement and causality between oil and renewable energy stock prices. Energy Econ. 61, 241–252 (2017)

    Google Scholar 

  36. Feng, S.D., Sun, Q.R., Liu, X.Y., Xu, T.R.: Spillover network features from the industry chain view in multi-time scales. Entropy 24, 1108 (2022)

    Google Scholar 

  37. Martinez, J.M.P., Abadie, L.M., Fernandez-Macho, J.: A multi-resolution and multivariate analysis of the dynamic relationships between crude oil and petroleum-product prices. Appl. Energ. 228, 1550–1560 (2018)

    Google Scholar 

  38. Cao, G.X., Xu, W.: Nonlinear structure analysis of carbon and energy markets with MFDCCA based on maximum overlap wavelet transform. Physica A 444, 505–523 (2016)

    Google Scholar 

  39. Boubaker, H., Raza, S.A.: A wavelet analysis of mean and volatility spillovers between oil and BRICS stock markets. Energy Econ. 64, 105–117 (2017)

    Google Scholar 

  40. Tiwari, A.K., Oros, C., Albulescu, C.T.: Revisiting the inflation-output gap relationship for France using a wavelet transform approach. Econ. Modelling 37, 464–475 (2014)

    Google Scholar 

  41. Feng, S.D., Huang, S.P., Qi, Y.B., Liu, X.Y., Sun, Q.R., Wen, S.B.: Network features of sector indexes spillover effects in China: a multi-scale view. Physica A 496, 461–473 (2018)

    Google Scholar 

  42. Takens, F.: Detecting strange attractors in turbulence. In: Rand, D., Young, L.-S. (eds.) Dynamical systems in turbulence. Springer, Berlin (1981)

    Google Scholar 

  43. Sugihara, G., May, R.M.: Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series. Nature 344, 734–741 (1990)

    Google Scholar 

  44. Freeman, L.C.J.S.N.: Centrality in social networks conceptual clarification. Soc. Netw. 3, 215–239 (1978)

    Google Scholar 

  45. Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., Hwang, D.U.: Complex networks: structure and dynamics. Phys. Rep. 424, 175–308 (2006)

    MathSciNet  Google Scholar 

  46. Sui, G., Li, H.J., Feng, S.D., Liu, X.Y., Jiang, M.H.: Correlations of stock price fluctuations under multi-scale and multi-threshold scenarios. Physica A 490, 1501–1512 (2018)

    Google Scholar 

  47. Gao, X.Y., An, H.Z., Zhong, W.Q.: Features of the correlation structure of price indices. PLoS ONE 8, 61091 (2013)

    Google Scholar 

Download references

Funding

This research is funded by the Science Research Project of Hebei Education Department (SQ2024104); the Scientific Research Initiation Project for High-level Talents of Hebei University (521000981396) and the Social Science Cultivation Project of Hebei University.

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Correspondence to Zenglei Xi.

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Appendix

Appendix

Table A1 Descriptive statistics for 55 stocks

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Sun, Q., Zhao, W., Bai, Z. et al. Multi-scale pattern causality of the price fluctuation in energy stock market. Nonlinear Dyn 112, 7291–7307 (2024). https://doi.org/10.1007/s11071-024-09279-3

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