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Regular Articles

A Multiple Timescales Conditional Causal Analysis on the Carbon-Energy Relationship: Evidence from European and Emerging Markets

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References

  • Alberola, E., J. Chevallier, and B. Chèze. 2008. Price drivers and structural breaks in European carbon prices 2005–2007. Energy Policy 36 (2):787–97. doi:10.1016/j.enpol.2007.10.029.
  • Balcılar, M., R. Demirer, S. Hammoudeh, and D. Nguyen. 2016. Risk spillovers across the energy and carbon markets and hedging strategies for carbon risk. Energy Economics 54:159–72. doi:10.1016/j.eneco.2015.11.003.
  • Barnett, L., and A. K. Seth. 2014. The MVGC multivariate Granger causality toolbox: A new approach to Granger-causal inference. Journal of Neuroscience Methods 223:50–68. doi:10.1016/j.jneumeth.2013.10.018.
  • Barrett, A. B., L. Barnett, and A. K. Seth. 2010. Multivariate Granger causality and generalized variance. Physical Review: Part E 81 (4):41907. doi:10.48550/arXiv.1002.0299.
  • Bel, G., and S. Joseph. 2015. Emission abatement: Untangling the impacts of the EU ETS and the economic crisis. Energy Economics 49:531–39. doi:10.1016/j.eneco.2015.03.014.
  • Billio, M., M. Getmansky, A. W. Lo, and L. Pelizzon. 2012. Econometric measures of connectedness and systemic risk in the finance and insurance sectors. Journal of Financial Economics 104 (3):535–59. doi:10.1016/j.jfineco.2011.12.010.
  • Chen, Y., S. L. Bressler, K. H. Knuth, W. A. Truccolo, and M. Ding. 2006. Stochastic modeling of neurobiological time series: Power, coherence, Granger causality, and separation of evoked responses from ongoing activity. Stochastic Modeling of Neurobiological Time Series: Chaos 16 (2):26113. doi:10.1063/1.2208455.
  • Diebold, F. X., and K. Yilmaz. 2016. Trans-Atlantic equity volatility connectedness: U.S. and European financial institutions, 2004–2014. Journal of Financial Econometrics 14:81–127. doi:10.2139/ssrn.3680198.
  • Geweke, J. F. 1984. Measures of conditional linear dependence and feedback between time series. Journal of the American Statistical Association 79 (388):907–15. doi:10.1080/01621459.1984.10477110.
  • Huang, N. E., Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N. C. Yen, C. C. Tung, and H. H. Liu. 1998. The empirical mode decomposition and the Hilbert Spectrum for nonlinear and non-stationary Time Series. 454. Proceedings of the Royal Society of London Series A 454 (1971):903–95. doi:10.1098/rspa.1998.0193.
  • Ji, Q., J. Geng, and A. K. Tiwari. 2018. Information spillovers and connectedness networks in the oil and gas markets. Energy Economics 75:71–84. doi:10.1016/j.eneco.2018.08.013.
  • Jiménez-Rodríguez, R. 2019. What happens to the relationship between EU allowances prices and stock market indices in Europe? Energy Economics 81:13–24. doi:10.1016/j.eneco.2019.03.002.
  • Koch, N., S. Fuss, G. Grosjean, and O. Edenhofer. 2014. Causes of the EU ETS price drop: Recession, CDM, renewable policies or a bit of everything? New evidence Energy Policy 73:676–85. doi:10.1016/j.enpol.2014.06.024.
  • Koop, G., and L. Tole. 2013. Forecasting the European carbon market. Journal of the Royal Statistical Society: Series A 176 (3):723–41. doi:10.1111/j.1467-985X.2012.01060.x.
  • Li, H., Q. Li, X. Huang, and L. Guo. 2023. Do green bonds and economic policy uncertainty matter for carbon price? New insights from a TVP-VAR framework. International Review of Financial Analysis 86:102502. doi:10.1016/j.irfa.2023.102502.
  • Nordhaus, W. D. 1991. To slow or not to slow: The economics of the greenhouse effect. The Economic Journal 101 (407):920–37. doi:10.2307/2233864.
  • Olijslagers, S., F. Ploeg, and S. Wijnbergen. 2023. On current and future carbon prices in a risky world. Journal of Economic Dynamics & Control 146:104569. doi:10.1016/j.jedc.2022.104569.
  • Ordu, B. M., and U. Soytaş. 2016. The relationship between energy commodity prices and electricity and market index performances: Evidence from an emerging market. Emerging Markets Finance and Trade 52 (9):2149–64. doi:10.1080/1540496X.2015.1068067.
  • Polbin, A., A. Skrobotov, and A. Zubarev. 2020. How the oil price and other factors of real exchange rate dynamics affect real GDP in Russia. Emerging Markets Finance and Trade 56 (15):3732–45. doi:10.1080/1540496X.2019.1573667.
  • Uddin, G. S., J. A. Hernandez, S. J. H. Shahzad, and A. Hedström. 2018. Multivariate dependence and spillover effects across energy commodities and diversification potentials of carbon assets. Energy Economics 71:35–46. doi:10.1016/j.eneco.2018.01.035.
  • Yan, W., and A. Cheung. 2022. The dynamic spillover effects of climate policy uncertainty and coal price on carbon price: Evidence from China. Finance Research Letters 53:103400. doi:10.1016/j.frl.2022.103400.
  • Yang, L. 2019. Connectedness of economic policy uncertainty and oil price shocks in a time domain perspective. Energy Economics 80:219–33. doi:10.1016/j.eneco.2019.01.006.
  • Yang, L. 2022. Idiosyncratic information spillover and contentedness network between the electricity and carbon markets in Europe. Journal of Commodity Market 25:100185. doi:10.1016/j.jcomm.2021.100185.
  • Yang, L., X. J. Cai, and S. Hamori. 2017. Does the crude oil price influence the exchange rates of oil-importing and oil-exporting countries differently? A wavelet coherence analysis. International Review of Economics and Finance 49:536–47. doi:10.1016/j.iref.2017.03.015.
  • Yang, L., and S. Hamori. 2021. The role of the carbon market in relation to the cryptocurrency market: Only diversification or more. International Review of Financial Analysis 72:101594. doi:10.1016/j.irfa.2021.101864.
  • Yin, J., Y. Zhu, and X. Fan. 2021. Correlation analysis of China’s carbon market and coal market based on multi-scale entropy. Resources Policy 72:102065. doi:10.1016/j.resourpol.2021.102065.
  • Yu, L., J. Li, L. Tang, and S. Wang. 2015. Linear and nonlinear Granger causality investigation between carbon market and crude oil market: A multi-scale approach. Energy Economics 51:300–11. doi:10.1016/j.eneco.2015.07.005.
  • Yuan, N., and L. Yang. 2020. Asymmetric risk spillover between financial market uncertainty and the carbon market: A GAS–DCS–copula approach. Journal of Cleaner Production 59:25920. doi:10.1016/j.jclepro.2020.120750.
  • Zhang, Y., and Y. Sun. 2016. The dynamic volatility spillover between European carbon trading market and fossil energy market. Journal of Cleaner Production 112:2654–63. doi:10.1016/j.jclepro.2015.09.118.
  • Zhu, B., D. Han, J. Chevallier, and Y. M. Wei. 2017. Dynamic multiscale interactions between European carbon and electricity markets during 2005–2016. Energy Policy 107:309–22. doi:10.1016/j.enpol.2017.04.051.
  • Zhu, B., S. Ye, D. Han, P. Wang, K. He, Y. M. Wei, and R. Xie. 2019. A multiscale analysis for carbon price drivers. Energy Economics 78:202–16. doi:10.1016/j.eneco.2018.11.007.

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