Dynamic Spillovers between Green Finance and Energy Markets and Their Association with Financial Market Uncertainty: Evidence from China_Vol. 3 No. 4 (JBM 2026)_Journal of Business and Marketing (ISSN: 3005-5717)

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Dynamic Spillovers between Green Finance and Energy Markets and Their Association with Financial Market Uncertainty: Evidence from China

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DOI: https://doi.org/10.62517/jbm.202609412

Author(s)

Jingyao Sa

Affiliation(s)

Zhaotong University, Zhaotong, Yunnan, China

Abstract

This study examines how return spillovers between China's green finance and energy markets vary across market states and investment horizons, and how those spillovers co-move with financial market uncertainty. The analysis uses 1,539 matched daily observations from June 2017 to January 2025 for carbon allowances, green equities, green bonds, crude oil, liquefied natural gas, coking coal futures, and a new energy index. A quantile vector autoregression model is combined with generalized forecast error variance decompositions, the Diebold-Yilmaz connectedness framework, the Baruník-Křehlík frequency decomposition, and wavelet coherence. Changes in CSI 300 conditional volatility, estimated using a GARCH (1,1) model, serve as the financial market uncertainty proxy. At the median quantile, total connectedness is 21.63%; the corresponding lower- and upper-tail values are 69.06% and 69.15%. The largest share of spillovers occurs within one to five trading days. For this short horizon, connectedness reaches 16.82% at the median, 53.66% in the lower tail, and 55.53% in the upper tail. The respective longer-horizon values are 4.80%, 15.40%, and 13.62%. Green equities and new energy stocks are recurrent transmitters, while the coking coal market changes from a receiver under typical conditions to a transmitter in both tails. Green bonds become an important short-term transmitter in the upper tail. Wavelet evidence shows that the association between connectedness and financial uncertainty is episodic, quantile dependent, and concentrated at different frequencies around the COVID-19 pandemic and the Russia-Ukraine war. The results support state-contingent risk monitoring and horizon-specific portfolio management.

Keywords

Green Finance; Energy Markets; Financial Market Uncertainty; Quantile Vector Autoregression; Connectedness; Wavelet Coherence

References

[1] Lee, J. W. (2020). Green finance and sustainable development goals: The case of China. The Journal of Asian Finance, Economics and Business, 7(7), 577-586. https://doi.org/10.13106/jafeb.2020.vol7.no7.577 [2] Sun, G., Li, G., Dilanchiev, A., & Kazimova, A. (2023). Promotion of green financing: Role of renewable energy and energy transition in China. Renewable Energy, 210, 769-775. https://doi.org/ 10.1016/j.renene.2023.04.044 [3] Henriques, I., & Sadorsky, P. (2008). Oil prices and the stock prices of alternative energy companies. Energy Economics, 30(3), 998-1010. https://doi.org/10.1016/ j.eneco.2007.11.001 [4] Sadorsky, P. (2012). Correlations and volatility spillovers between oil prices and the stock prices of clean energy and technology companies. Energy Economics, 34(1), 248-255. https://doi.org/10.1016/ j.eneco.2011.03.006 [5] Reboredo, J. C. (2015). Is there dependence and systemic risk between oil and renewable energy stock prices? Energy Economics, 48, 32-45. https://doi.org/10.1016/j.eneco. 2014.12.009 [6] Pham, L. (2016). Is it risky to go green? A volatility analysis of the green bond market. Journal of Sustainable Finance & Investment, 6(4), 263-291. https://doi.org/ 10.1080/20430795.2016.1237244 [7] Reboredo, J. C., & Ugolini, A. (2020). Price connectedness between green bond and financial markets. Economic Modelling, 88, 25-38. https://doi.org/10.1016/j.econmod. 2019.09.004 [8] Duan, X., Xiao, Y., Ren, X., Taghizadeh-Hesary, F., & Duan, K. (2023). Dynamic spillover between traditional energy markets and emerging green markets: Implications for sustainable development. Resources Policy, 82, 103483. https:// doi.org/10.1016/j.resourpol.2023.103483 [9] Zhao, L., He, W., Wang, A., & Zhu, F. (2023). Time and frequency spillovers between the green economy and traditional energy markets. Systems, 11(3), 153. https://doi.org/10.3390/systems11030153 [10] Bloom, N. (2009). The impact of uncertainty shocks. Econometrica, 77(3), 623-685. https://doi.org/10.3982/ECTA62 48 [11] Carrière-Swallow, Y., & Céspedes, L. F. (2013). The impact of uncertainty shocks in emerging economies. Journal of International Economics, 90(2), 316-325. https://doi.org/10.1016/j.jinteco.2013.03.003 [12] Gabauer, D., & Gupta, R. (2018). On the transmission mechanism of country-specific and international economic uncertainty spillovers: Evidence from a TVP-VAR connectedness decomposition approach. Economics Letters, 171, 63-71. https:// doi.org/10.1016/j.econlet.2018.07.007 [13] Long, S., & Li, Z. (2023). Dynamic spillover effects of global financial stress: Evidence from the quantile VAR network. International Review of Financial Analysis, 90, 102945. https://doi.org/10.1016/j.irfa. 2023.102945 [14] Wang, J., Mishra, S., Sharif, A., & Chen, H. (2024). Dynamic spillover connectedness among green finance and policy uncertainty: Evidence from QVAR network approach. Energy Economics, 131, 107330. https:// doi.org/10.1016/j.eneco.2024.107330 [15] Uddin, M. A., Chang, B. H., Aldawsari, S. H., & Li, R. (2025). The interplay between green finance, policy uncertainty and carbon market volatility: A time frequency approach. Sustainability, 17(3), 1198. https://doi.org/10.3390/su17031198 [16] Diebold, F. X., & Yilmaz, K. (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 28(1), 57-66. https://doi.org/ 10.1016/j.ijforecast.2011.02.006 [17] White, H., Kim, T.-H., & Manganelli, S. (2015). VAR for VaR: Measuring tail dependence using multivariate regression quantiles. Journal of Econometrics, 187(1), 169-188. https://doi.org/10.1016/j.jeconom. 2015.02.004 [18] Baruník, J., & Křehlík, T. (2018). Measuring the frequency dynamics of financial connectedness and systemic risk. Journal of Financial Econometrics, 16(2), 271-296. https://doi.org/10.1093/jjfinec/ nby001 [19] Yang, C., Niu, Z., & Gao, W. (2022). The time-varying effects of trade policy uncertainty and geopolitical risks shocks on commodity market prices: Evidence from the TVP-VAR-SV approach. Resources Policy, 76, 102600. https://doi.org/10.1016/ j.resourpol.2022.102600 [20] Zhang, D., Hu, M., & Ji, Q. (2020). Financial markets under the global pandemic of COVID-19. Finance Research Letters, 36, 101528. https://doi.org/ 10.1016/j.frl.2020.101528 [21] Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327. https:// doi.org/10.1016/0304-4076 (86)90063-1 [22] Koop, G., Pesaran, M. H., & Potter, S. M. (1996). Impulse response analysis in nonlinear multivariate models. Journal of Econometrics, 74(1), 119-147. https:// doi.org/10.1016/0304-4076 (95)01753-4 [23] Pesaran, M. H., & Shin, Y. (1998). Generalized impulse response analysis in linear multivariate models. Economics Letters, 58(1), 17-29. https://doi.org/ 10.1016/S0165-1765(97)00214-0 [24] Torrence, C., & Compo, G. P. (1998). A practical guide to wavelet analysis. Bulletin of the American Meteorological Society, 79(1), 61-78. https://doi.org/10.1175/1520-0477(1998)079<0061:APGTWA>2.0.CO;2