Improving liquidity in emission trading schemes

Kim, Jihun; Park, Kwangwoo

doi:10.1002/fut.22220

Cited by 10 publications

(2 citation statements)

References 30 publications

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“…Our research aims to forecast carbon futures volatility using the return spillovers of fossil energy futures. Following the numerous research (see, e.g., Dutta, 2018; Gong et al, 2023; Guo et al, 2022; Jiang & Zhang, 2023; Kim & Park, 2021; Liu et al, 2021; Reboredo, 2014; Zhang, Luo, et al, 2022), we choose the European Union Allowances (EUA) carbon futures, which is the earliest and most active carbon futures throughout the world. We collect the daily price data of EUA carbon futures traded in the International Continental Exchange (ICE) on Investing.…”

Section: Datamentioning

confidence: 99%

The predictability of carbon futures volatility: New evidence from the spillovers of fossil energy futures returns

Zhang,

Wang

et al. 2024

Journal of Futures Markets

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In this paper, we find new evidence for the carbon futures volatility prediction by using the spillovers of fossil energy futures returns as a powerful predictor. The in‐sample results show that the spillovers have a significantly positive effect on carbon futures volatility. From the out‐of‐sample analysis with various loss functions, we find that fossil energy return spillovers significantly outperform the benchmark and show better forecasting performance than the competing models using dimension reduction, variable selection, and combination approaches. The predictive ability of the spillovers also holds in long‐term forecasting and does not derive from other carbon‐related variables. It can bring substantial economic gains in the portfolio exercise within carbon futures. Finally, we provide economic explanations on the predictive ability of the fossil energy return spillover by the channels of the carbon emission uncertainty and the investor sentiment on the warming climate.

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Section: Datamentioning

confidence: 99%

The predictability of carbon futures volatility: New evidence from the spillovers of fossil energy futures returns

Zhang,

Wang

et al. 2024

Journal of Futures Markets

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“…In this light, energy security policies may be designed to, for example, increase the consumption of fossil fuels. Certainly, increases in the production and consumption of energy from hydrocarbons mean more carbon emission (Kim and Park, 2021; Hassan and Kouhy, 2014). This suggests that oil and gas companies may exhaust or exceed their carbon emission allowances and, in the latter case, will have to purchase emission credits from carbon markets to continue to produce hydrocarbons.…”

Section: Introductionmentioning

confidence: 99%

External energy security elements and the riskiness of clean energy stocks: a volatility analysis

Hassan¹

2023

SAMPJ

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Purpose Clean energy stocks are exhibiting signs of increasing volatility reflecting the varied and conflicting strategies employed by nations to pursue energy security objectives. In this regard, this paper aims to examine the response of NASDAQ clean energy stock returns volatility to the influences of external energy security elements including oil price, natural gas price, coal price, carbon price and green information technology stock price. Design/methodology/approach The paper uses symmetric and asymmetric generalised autoregressive conditional heteroskedasticity models (GARCH and TGARCH, respectively), which incorporate external energy security elements as exogenous variables, to estimate volatility models for clean energy stock returns. Findings Although, prices of oil, coal and natural gas are negatively associated with NASDAQ clean energy returns volatility, only the effect of natural gas price is significant. While carbon price affects NASDAQ clean energy returns volatility positively, green information technology price affects the volatility negatively. These results are robust to exponential GARCH and lead-and-lag robust ordinary least-squares as alternative estimation methods. Research limitations/implications The study lumps the effects of all other external and internal factors, including internal energy security elements, in the autoregressive conditional heteroscedasticity (ARCH) term to predict NASDAQ clean energy returns conditional variance. GARCH method does not disentangle individual roles of the factors captured in the ARCH term in predicting volatility. Practical implications Results documented imply that natural gas appears a closer substitute for renewable energy sources than crude oil and coal, such that its price rise is perceived as good news in the NASDAQ clean energy financial market, while a fall is considered bad news. Furthermore, both an increase in carbon price and a decrease in green information technology stock performance are perceived as negative shocks. Social implications In assessing risks associated with clean energy stocks, investors and fund managers should carefully consider the effects of external energy security elements. Originality/value To the best of the author’s knowledge, the paper is the first to identify external energy security elements and examine their effects on clean energy stock volatility.

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Effect of Chinese pilots carbon emission trading scheme on enterprises' total factor productivity: The moderating role of government participation and carbon trading market efficiency

Pan

Yuan

et al. 2022

Journal of Environmental Management

120

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Improving liquidity in emission trading schemes

Cited by 10 publications

References 30 publications

The predictability of carbon futures volatility: New evidence from the spillovers of fossil energy futures returns

The predictability of carbon futures volatility: New evidence from the spillovers of fossil energy futures returns

External energy security elements and the riskiness of clean energy stocks: a volatility analysis

Effect of Chinese pilots carbon emission trading scheme on enterprises' total factor productivity: The moderating role of government participation and carbon trading market efficiency

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