Key points are not available for this paper at this time.
Purpose This paper aims to identify the macro variables that affect China’s ETS market through a mixed-frequency sampling data with a variable selection model. Design/methodology/approach This paper focuses on the Hubei, Guangdong and Shenzhen ETS in China. It integrates exogenous factors, in aspect of economic, financial, energy and environment, to identify key drivers of ETS market volatility. First, this study applies the GARCH-MIDAS model to process mixed-frequency data. Second, this paper employs the Lasso method to select the most predictive factors and enhance volatility forecasting. Finally, this paper evaluates the effectiveness of the conclusions through model parameter estimation, out-of-sample prediction, robustness test and economic value evaluation. Findings First, China’s ETS market volatility is primarily driven by the energy sector, with limited influence from policy and environmental factors. Second, ETS market volatility varies across regions. The power sector strongly influences the Hubei ETS market, whereas the Guangdong and Shenzhen ETS markets are more affected by the energy market. Third, out-of-sample analysis and robustness tests statistically indicate that the GARCH-MIDAS-Adaptive-Lasso model enhances forecasting accuracy. Originality/value First, this paper integrates multidimensional factors into the model. Second, this paper combines the adaptive Lasso method with the GARCH-MIDAS model to analyze the volatility of China’s ETS market. This method addresses both multicollinearity and variable selection challenges in mixed-frequency data. Third, this paper offers valuable insights for other developing countries seeking to establish or enhance ETS systems.
Wang et al. (Fri,) studied this question.