Downside Tail Risk Spillover Effects Among High-Carbon-Emission Industries Under Carbon Financial Regulatory Policy Uncertainty: Implications for Sustainable Transition

This study adopts the Conditional Autoregressive Value at Risk (CAViaR) model to measure the downside tail risks of high-carbon-emission industries. Based on the Time-Varying Parameter Vector Autoregressive-BK (TVP-VAR-BK) model, it investigates the downside tail risk spillover effects across these industries. Meanwhile, this study constructs the Carbon Financial Regulatory Policy Uncertainty (CFRPU) index, and applies the Time-Varying Parameter Vector Autoregression with Stochastic Volatility (TVP-SV-VAR) model to examine the dynamic association between changes in CFRPU and changes in the total connectedness index. The results from the empirical study demonstrate that: (1) For the six sampled high-carbon-emission industries, downside tail risk spillover effects exhibit notable time-varying patterns alongside clear frequency heterogeneity. Specifically, the petrochemical sector serves as a net receiver of risk shocks over short-term horizons, but switches to a net transmitter of risk spillovers in the long term. (2) Bidirectional downside tail risk spillovers are observed across all six industries, with pronounced heterogeneity across investment horizons. Under the baseline 30-day frequency partition, the longer-horizon component is larger than the shorter-horizon component and broadly follows the temporal pattern of total connectedness. (3) The posterior mean response of ΔTCI to a positive ΔCFRPU innovation is largest at the two-month horizon and approaches zero by the six-month horizon. However, the 95% posterior credible intervals include zero, indicating that the short-horizon association is suggestive rather than statistically conclusive. These findings provide useful evidence for monitoring cross-industry financial risk and maintaining a predictable regulatory environment during the low-carbon transition.