Risk Measurement of Chinese Carbon Emissions Trading Market Based on DCS-Type Models

The Hubei carbon emissions trading market presents significant price volatility driven by energy price fluctuations, macroeconomic conditions and policy changes. Accurate price risk measurement is critically important for market participants. This study adopts Value at Risk (VaR) and Expected Shortfall (ES) to quantify market risk, and constructs a set of DCS-type models by combining the dynamic conditional score framework with the skewed Student-t distribution. Model evaluation covers unconditional coverage test, conditional coverage test, dynamic quantile test, the Actual-to-Expected ratio, the mean and the maximum absolute deviation, quantile loss and FZ loss. Empirical analysis based on daily HBEA spot prices from 3 April 2014 to 4 December 2024 shows that: (1) The DCS-ST model provides better data fitting performance and can effectively measure the market risk of China’s carbon trading market. (2) The parameter updating frequency has little impact on the prediction accuracy of the model. The results enriches the quantitative methodology for carbon market risk measurement and provide a reliable technical scheme for tail risk management in China’s carbon emissions trading market.