Carbon price volatility: The case of China

Abstract

Based on carbon spot prices selected from seven carbon pilots, we assess the financial performances related to carbon volatility in China on the overall perspective. According to the results, the Chinese carbon market fluctuated severely at the beginning of carbon trading, but has stabilised in general, despite several dramatic changes related to ‘yearly compliance events’. Long-term memory exists in the volatility series. Moreover, asymmetry exists in the Chinese carbon market, and volatility reacts more severely to good news than to bad news. Finally, we discuss our empirical results, and make certain suggestions regarding firms’ awareness, international cooperation and individual investors not only for policy makers in China but also for other developing countries who are contemplating either commencing carbon trading or improving the current market.

Assessing the risk of the European Union carbon allowance market

“…In relation to the market of carbon emission allowance, our findings add to those of Benz and Truck (2009), Chevallier (2011), Byun and Cho (2013), Spiesová (2016), Dhamija et al (2017) and Dutta (2018) by showing that accounting for structural breaks in emission variance improves the performances of the volatility prediction models. In that sense, our findings complement those of other papers that focus on the forecasting of carbon prices (Zhang, Li, Hao and Tan, 2018;Zhang, Liu and Xu, 2018), or the forecasting of carbon price volatility based on the volatility of energy markets (Zhang and Sun, 2016;Ji et al, 2018). Moreover, earlier studies on the EUA market (e.g.…”

“…These results suggest that for the TGARCH model, for instance, the effect of bad news amounts to 0.145 when breaks are overlooked and 0.186 when breaks are considered. Studying the Chinese carbon market, Zhang, Li, Hao and Tan (2018) and Zhang, Liu and Xu (2018) find evidence of asymmetry, but volatility seems to be more sensitive to good news than bad news. The significance of considering the presence of structural breaks while modeling EUA price returns is further supported by the log likelihood statistic along with other penalized-likelihood criteria such as the AIC and BIC.…”

“…The second considers the dynamic behavior of carbon prices (e.g. Zhang, Li, Hao and Tan, 2018;Zhang, Liu and Xu, 2018). In contrast, this study attempts to refine the modeling and forecasting of carbon price volatility by considering the presence of structural breaks emanating from extreme events.…”

“…In contrast, this study attempts to refine the modeling and forecasting of carbon price volatility by considering the presence of structural breaks emanating from extreme events. Importantly, unlike Zhang, Li, Hao and Tan (2018) and Zhang, Liu and Xu (2018), our focus is on volatility, which represents the second component of price return distribution. As argued by Christoffersen and Diebold (2000), volatility plays a more important role in asset pricing and risk management than the first moment of the price return distribution.…”

The Linkages of Carbon Spot-Futures: Evidence from EU-ETS in the Third Phase

“…Based on an efficient market hypothesis, Daskalakis and Markellos [16] found that the three predominant EUA exchanges, i.e., ECX, Nord Pool, and Powernext, were not efficient during the initial phase of the EU-ETS from 2005 to 2007, which may be attributed to such factors as immaturity, banking and short-selling. Wei and Zhang [17,18] investigated the carbon price volatility in EU-ETS and China, respectively; the empirical results of both studies indicated the long-term memory in the carbon market. Based on the characteristics of carbon spot and futures, the research on the correlation between the two markets became a new research hotspot, as it is in the traditional financial markets, i.e., the oil market [19][20][21].…”