Quantile time-frequency spillovers among climate policy uncertainty, energy markets, and stock markets

From a spillover network perspective, this study employs the QVAR-DY and QVAR-BK models to detect how global climate policy uncertainty (CPU) interacts with energy markets, encompassing coal, crude oil, natural gas, and clean energy, as well as stock markets in both the US and China within a quantile time-frequency framework. Results reveal that spillovers among CPU, energy and stock markets show heterogeneity under quantile time-frequency domains. The total connectedness is dominated by short-term shocks and becomes more significant within extreme market conditions, particularly during the COVID-19 pandemic. Moreover, in the normal market, clean energy and the US stock market act as net shock exporters, while the roles of the other variables within the system vary significantly across different frequencies. It is noteworthy that CPU changes from a short run net shock receiver to a medium and long run net shock transmitter. The spillover from Chinese stock market to CPU is more significant than that from other markets, especially during the short run. In extreme markets, the coal market exports spillovers, whereas the clean energy market imports them. These findings are crucial for controlling the spread of cross-market risk in the context of climate policy uncertainty. Relevant regulatory authorities should establish a short-term risk early warning mechanisms for climate policy, energy and stock markets under extreme markets, especially strengthening cross-market risk monitoring when major events occur. As CPU evolves into a spillover exporter over the medium to long run, strengthening the co-design of climate policy and clean energy market and guiding capital flows to low-carbon sectors will be critical for mitigating cross-market risk contagion and ensuring systemic stability.