This study quantifies risk spillover effects from multi-dimensional energy markets to China's Guangdong carbon market by constructing an EGARCH-CQR-based CoVaR model, which integrates the Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) framework to capture volatility leverage effects and clustering, combined with quantile regression for precise characterization of cross-market tail dependencies. Empirical analysis reveals significant structural heterogeneity in energy-to-carbon risk spillovers: traditional energy markets, such as oil and coke, exhibit ‘high-intensity, high-volatility’ shock patterns that transmit abrupt short-term risks during global crises like the COVID-19 outbreak, whereas new energy markets, including new energy vehicles and wind power, demonstrate ‘low-intensity, persistent’ spillover dynamics reflecting stronger market resilience. Additionally, China's ‘Dual Carbon’ policy reinforcement is identified as a critical policy transmission channel that significantly intensifies risk linkages between high-carbon energy sectors and the carbon market, with model validation confirming the robustness and coverage capability of the proposed GARCH-CQR-CoVaR framework.

References

• Adrian, T., & Brunnermeier, M. K. (2016). CoVaR. The American Economic Review, 106(7), 1705–1741. https://doi.org/10.1257/aer.20120555
• Artzner, P., Delbaen, F., Eber, J. M., & Heath, D. (1999). Coherent measures of risk. Mathematical Finance, 9(3), 203–228. https://doi.org/10.1111/mafi.1999.9.issue-3
• Cao, Z. (2013). Multi-CoVaR and shapley value: A systemic risk measure. Working paper, Banque de France.
• Chang, K., Ye, Z., & Wang, W. (2019). Volatility spillover effect and dynamic correlation between regional emissions allowances and fossil energy markets: New evidence from China's emissions trading scheme pilots. Energy, 185, 1314–1324. https://doi.org/10.1016/j.energy.2019.07.132
• Diebold, F. X., & Yilmaz, K. (2009). Measuring financial asset return and volatility spillovers, with application to global equity markets. The Economic Journal, 119(534), 158–171. https://doi.org/10.1111/j.1468-0297.2008.02208.x
• Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of united kingdom inflation. Econometrica, 50(4), 987–1007. https://doi.org/10.2307/1912773
• Fan, Y., Härdle, W. K., Wang, W., & Zhu, L. (2018). Single-index-based CoVaR with very high-dimensional covariates. Journal of Business & Economic Statistics, 36(2), 212–226. https://doi.org/10.1080/07350015.2016.1180990
• Ji, Q., Liu, B. Y., & Fan, Y. (2019). Risk dependence of CoVaR and structural change between oil prices and exchange rates: A time-varying copula model. Energy Economics, 77, 80–92. https://doi.org/10.1016/j.eneco.2018.07.012
• López-Espinosa, G., Moreno, A., Rubia, A., & Valderrama, L. (2012). Short-term wholesale funding and systemic risk: A global CoVaR approach. Journal of Banking & Finance, 36(12), 3150–3162. https://doi.org/10.1016/j.jbankfin.2012.04.020
• Mansanet-Bataller, M., Chevallier, J., Hervé-Mignucci, M., & Alberola, E. (2011). EUA and sCER phase II price drivers: Unveiling the reasons for the existence of the EUA-sCER spread. Energy Policy, 39(3), 1056–1069. https://doi.org/10.1016/j.enpol.2010.10.047
• Mi, Z. F., & Zhang, Y. J. (2011). Estimating the ‘value at risk’ of EUA futures prices based on the extreme value theory. International Journal of Global Energy Issues, 35(2–4), 145–157. https://doi.org/10.1504/IJGEI.2011.045027
• Ren, X., Dou, Y., Dong, K., & Li, Y. (2022). Information spillover and market connectedness: Multi-scale quantile-on-quantile analysis of the crude oil and carbon markets. Applied Economics, 54(38), 4465–4485. https://doi.org/10.1080/00036846.2022.2030855
• Su, C. W., Pang, L. D., Qin, M., Lobonţ, O. R., & Umar, M. (2023). The spillover effects among fossil fuel, renewables and carbon markets: Evidence under the dual dilemma of climate change and energy crises. Energy, 274,127304. https://doi.org/10.1016/j.energy.2023.127304
• Sun, X., Liu, C., Wang, J., & Li, J. (2020). Assessing the extreme risk spillovers of international commodities on maritime markets: A garch-copula-CoVaR approach. International Review of Financial Analysis, 68,101453. https://doi.org/10.1016/j.irfa.2020.101453
• Tang, C., Yang, G., & Liu, X. (2024). Risk spillover within the carbon-energy system-new evidence considering China's national carbon market. Economic Analysis and Policy, 81, 1227–1240. https://doi.org/10.1016/j.eap.2024.02.012
• Wang, Y., & Guo, Z. (2018). The dynamic spillover between carbon and energy markets: New evidence. Energy, 149, 24–33. https://doi.org/10.1016/j.energy.2018.01.145
• Xu, Q., Li, M., Jiang, C., & He, Y. (2019). Interconnectedness and systemic risk network of Chinese financial institutions: A lasso-CoVaR approach. Physica A: Statistical Mechanics and Its Applications, 534,122173. https://doi.org/10.1016/j.physa.2019.122173
• Yuan, N., & Yang, L. (2020). Asymmetric risk spillover between financial market uncertainty and the carbon market: A GAS-DCS-copula approach. Journal of Cleaner Production, 259(1),120750. https://doi.org/10.1016/j.jclepro.2020.120750
• Zhang, C., Yang, Y., & Yun, P. (2020). Risk measurement of international carbon market based on multiple risk factors heterogeneous dependence. Finance Research Letters, 32,101083. https://doi.org/10.1016/j.frl.2018.12.031
• Zhao, J., Cui, L., Liu, W., & Zhang, Q. (2023). Extreme risk spillover effects of international oil prices on the Chinese stock market: A GARCH-EVT-Copula-CoVaR approach. Resources Policy, 86,104142. https://doi.org/10.1016/j.resourpol.2023.104142
• Zheng, Q., Wu, J., & Lin, B. (2024). Geopolitical risk and extreme risk connectedness among energy and other strategic commodities: Fresh sight using the high-dimensional CoVaR model. Journal of Futures Markets, 44(11), 1787–1806. https://doi.org/10.1002/fut.v44.11

To cite this article: Yarong Zhang & Sheng Zhu (06 Apr 2026): Cross-market spillover effects of energy risks: from the perspective of GARCH-CQR-based CoVaR model, Statistical Theory and Related Fields, DOI: 10.1080/24754269.2026.2653289

To link to this article: https://doi.org/10.1080/24754269.2026.2653289