Volatility Risk Spillovers Across China’s Electricity Spot, Carbon and Financial Markets: Implications for Low-Carbon Transition with Evidence from BEKK–GARCH and Nonlinear Granger Causality Tests

To support coordinated risk management of electricity and carbon markets under the new power system, this paper clarifies the volatility transmission mechanism between Guangdong electricity spot prices and Guangdong carbon allowance prices, and investigates risk spillover effects among the Guangdong electricity spot market, Guangdong carbon market and power-related financial market. The results help identify regional systemic risks in the low-carbon energy transition and provide references for regional energy security and financial stability. This paper adopts the logarithmic return of day-ahead average prices in the Guangdong electricity spot market (RE), the logarithmic return of Guangdong carbon emission allowance prices (RC), and the logarithmic return of the FTSE China A600 Electricity Index (RF). A VAR(2)-BEKK-GARCH(1,1) framework is constructed to analyze risk linkages from mean and volatility dimensions. News impact surfaces, volatility impulse response functions, the Diebold–Yilmaz volatility spillover index and the Diks–Panchenko nonlinear causality test are further applied for comprehensive inference. The results show that the electricity market has significant inherent baseline volatility stemming from real-time supply-demand balance and the non-storability of electricity. Volatility persistence ranks as financial market > carbon market > electricity market. In terms of volatility transmission, there exists robust unidirectional volatility spillover from the electricity market to the carbon market; the carbon market only exerts a marginally significant short-run shock on the electricity market, while the joint test for this direction is insignificant. News impact surfaces and impulse responses reveal a negative co-movement pattern between electricity and carbon volatilities, implying potential volatility buffering effects rather than a straightforward natural hedging relationship. No significant volatility spillover exists between the financial market and the other two markets, meaning risk isolation holds during the sample period. Linear transmission in the mean dimension is weak, while the Diks–Panchenko nonlinear Granger causality test demonstrates that both the electricity market and the power financial index have significant nonlinear predictive power for carbon prices. This study provides empirical evidence for Guangdong power generators to optimize bidding strategies with carbon cost signals and establish a regional joint risk monitoring framework. It offers policy implications for coordinated regulation of the three regional markets and prevention of cross-market risk contagion.