Dependence changes between the carbon price and its fundamentals: A quantile regression approach

Tan, Xueping; Wang, Xinyu

doi:10.1016/j.apenergy.2016.12.116

Cited by 124 publications

(86 citation statements)

References 75 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…More specifically, economic environment is significant, but the impact of energy prices does not have the same conclusion pending further examination, unexpected events bring shocks to the carbon price and even lead to suspension of trading. Tan and Wang (2017) focused on the quantile-based dependence and influence paths between European Union allowance (EUA) and its drivers (energy prices and macroeconomic risk factors) during the three phases of the European Union Emissions Trading Scheme (EU ETS) and showed that the reaction in fluctuation in carbon price in relation to its drivers across its conditional distribution in different phases is highly heterogeneous. Fan et al (2017) used the event study method to assess the impacts of different policy adjustments on the EUA returns in the European Union Emissions Trading Scheme (EU ETS) since 2005.…”

Section: The Relationship Between Carbon Price and Energy Pricementioning

confidence: 99%

“…China's pilot carbon emission trading programs began operating in the second half of 2013 in Beijing, Tianjin, Shanghai, Chongqing, Hubei, Guangdong and Shenzhen (Zeng et al 2017) The carbon emission trading schemes in 7 regions in China marked a watershed in the history of Chinese climate policy (Ren and Lo 2017;Tan and Wang 2017). At the end of 2015, the cumulative turnover in seven pilot carbon trading markets was nearly 80 million tons and the cumulative payment was more than 2.5 billion RMB (Zhou et al 2016).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Factors affecting the pilot trading market of carbon emissions in China

et al. 2018

View full text Add to dashboard Cite

Climate change and carbon emissions are major problems which are attracting worldwide attention. China has had its pilot carbon emission trading markets in seven regions for more than 3 years. What affects carbon emission trading market in China is a big question. More attention is paid to how China promotes the carbon emission trading schemes in the whole country. This paper addresses concerns about the functioning of carbon emission trading schemes in seven pilot regions and takes the weekly data from November 25, 2013, to March 19, 2017. We employ a vector autoregressive model to study how coal price, oil price and stock index have affected the carbon price in China. The results indicate that carbon price is mainly affected by its own historical price; coal price and stock index have negative effects on carbon price, while oil price has a negative effect on carbon price during the first 3 weeks and then has a positive effect on carbon price. More regulatory attention and economic measures are needed to improve market efficiency, and the mechanisms of carbon emission trading schemes should be improved.

show abstract

Section: The Relationship Between Carbon Price and Energy Pricementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Factors affecting the pilot trading market of carbon emissions in China

et al. 2018

View full text Add to dashboard Cite

show abstract

“…They stated that the 0.01, 0.05, and 0.95 quantiles should be used to describe the extreme volatility that can be considered as value at risk (VaR) at a 99% significance level, that is, as the maximum loss in portfolio value for a given time period. 6 The research focused on the dynamic of CO 2 prices is extensive -Aatola et al performed an analysis which showed the relationships between the price of EUA and fundamentals such as German electricity price and gas and coal prices. Moreover, they approximated that 40% of the changes in the EUA forward price can be correlated by these parameters.…”

Section: Research Related To Energy and Carbon Marketsmentioning

confidence: 99%

“…Tan and Wang used a quantile regression approach to investigate the relationship between the carbon price and its determinants for all three phases of the EU ETS. They stated that the 0.01, 0.05, and 0.95 quantiles should be used to describe the extreme volatility that can be considered as value at risk (VaR) at a 99% significance level, that is, as the maximum loss in portfolio value for a given time period …”

Section: Introductionmentioning

confidence: 99%

Forecast of long‐term EUA price probability using momentum strategy and GBM simulation

2020

View full text Add to dashboard Cite

CO2 storage projects are financially intensive and, without the European Union emission trading scheme that encourages CO2 emission reduction projects, they will not be cost effective, that is, feasible. Similar to the huge uncertainties involved in long‐term energy price prediction, CO2 market price is volatile. In this paper, indicators for key CO2 price changes are detected by moving average analysis. To prove the validity of the methods, geometric Brownian motion (GBM) simulations were performed for different drift and volatility values. Drift, the standard deviation of CO2 price changes and CO2 volatility were used on a daily, weekly and monthly basis for short term simulations and on a weekly basis for long term simulations. GBM simulation can help determine the probability of a European emission allowances (EUA) price, which is featured by great variations, depending on the period. Signals for periods were determined by momentum strategy, with 60 days for short and 365 days for the long moving average. They show correlation with natural gas prices, that is, EUA signals appear 13–17 months after the signal in natural gas price. Among the vast number of proposed hybrid prediction models, this correlation allows the prediction of a long‐term price trend. It seems that back‐loading measures obstructed risk estimates because they resulted in extreme price drift in some periods, as the consequence of rebalancing in supply and demand. © 2020 Society of Chemical Industry and John Wiley & Sons, Ltd.

show abstract

“…The market needs a gradually increasing carbon price to provide long-term signals to market participants. The carbon price is essentially affected by factors from the demand side, such as energy prices [4], the macroeconomic environment [5], policy factors [6,7], prices of related products [8], climate change [9], and foreign direct investment [10]. Energy price and economic growth are regarded as the most important influencing factors.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Analysis of a Three-Dimensional Carbon Price Dynamic System

2018

View full text Add to dashboard Cite

The carbon market is the least-cost tool to reduce carbon emissions. This study explores the evolution of the carbon price in the carbon market from a dynamic system perspective. A three-dimensional carbon price dynamic system is established to quantify the interactions among the carbon price, energy price, and economic growth. The system built in this study presents various dynamic characteristics including chaotic attractors and stable equilibria. Specifically, the existence of chaos in the system is verified by Lyapunov exponents spectrum and bifurcation diagram. In contrast, the system tends to be stable in the case of China after identifying the system parameters through the genetic algorithm. Furthermore, evolutionary trends of the carbon price are analyzed when the system parameters are perturbed. The results show that the carbon price is positively correlated with energy price as well as energy price policy. Besides, the level of the carbon price is negatively correlated with government control in the short term and positively correlated in the long term. This study can help analyze trends in the carbon price in the mid-term to long-term.

show abstract

Dependence changes between the carbon price and its fundamentals: A quantile regression approach

Cited by 124 publications

References 75 publications

Factors affecting the pilot trading market of carbon emissions in China

Factors affecting the pilot trading market of carbon emissions in China

Forecast of long‐term EUA price probability using momentum strategy and GBM simulation

Evolutionary Analysis of a Three-Dimensional Carbon Price Dynamic System

Contact Info

Product

Resources

About