Abstract
This paper focuses on the time-varying correlation among China’s seven emissions trading scheme markets. Correlation analysis shows a weak connection among these markets for the whole sample period, which spans from 9 June 2014 to 30 June 2017. The return rate series of the seven markets show the characteristics of a fat-tailed and skewed distribution, and the Vector Autoregression (VAR) residuals present a significant Autoregressive Conditional Heteroscedasticity (ARCH) effect. Therefore, we adopt Vector Autoregression Generalized ARCH model with Dynamic Conditional Correlation (VAR-DCC-GARCH) to capture the time-varying correlation coefficients. The results of the VAR-DCC-GARCH show that the conditional correlation coefficients fluctuate fiercely over time. At some points, the different markets present a significant correlation with the value of the even peaks of the coefficient at 0.8, which indicates that these markets are closely connected. However, the connection between each market does not last long. According to the actual situation of China’s regional carbon emission markets, policy factors may explain most of the temporary, significant co-movement among markets.
Highlights
To realize the goals of energy saving and emissions reduction policies, China’s seven ETS pilots were established and functioned well from 18 June 2013 to 9 June 2014
What are the exact co-movements among the seven China’s emissions trading schemes (CETSs)? This question has attracted increasing interest from researchers examining linkages among different carbon markets, as their price movements have important implications for investment in the carbon market
This paper studies the dynamic linkages among the seven carbon markets from an overall perspective, which requires the multi-DCC-GARCH model
Summary
To realize the goals of energy saving and emissions reduction policies, China’s seven ETS (emissions trading scheme) pilots were established and functioned well from 18 June 2013 to 9 June 2014. It is very important that investors, speculators, portfolio managers, and policy makers understand the dynamic linkages among the seven CETSs. CERs (certified emission reductions) are helpful for price co-movement from the seven carbon markets. This paper will adopt a multi-Generalized Autoregressive Conditional Heteroscedasticity model with Dynamic Conditional Correlation (DCC-GARCH) model to investigate the dynamic linkage among emission allowance prices in China’s seven ETS pilots.
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