Can energy predict the regional prices of carbon emission allowances in China?

Guo, Li-Yang; Feng, Chao; Yang, Jun

doi:10.1016/j.irfa.2022.102210

Cited by 7 publications

(3 citation statements)

References 39 publications

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“…Hence, the LHAR–RV–JV process outperforms all other specifications implying that using leverage effects and the information on crude oil volatility jumps would improve the accuracy of the HAR‐type models. Our finding aligns with the work of Guo et al (2022), which established the predictive content of crude oil prices for the emission market of Hubei.…”

Section: Empirical Findingssupporting

confidence: 93%

“…They conclude that both oil supply shock and oil demand shock lead to an increase in carbon allowance prices, while the oil risk shock causes a drop in emission prices. In addition, Guo et al (2022) assess if energy prices can predict the emission markets in Beijing, Guangdong, Hubei, and Shanghai. The study concludes that crude oil has some predictive contents only for the Hubei market.…”

Section: Introductionmentioning

confidence: 99%

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Impact of crude oil volatility jumps on sustainable investments: Evidence from India

Dutta

Kanjilal

Ghosh

et al. 2023

Journal of Futures Markets

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This study examines the impact of crude oil volatility jumps on the realized volatility (RV) of green and dirty stocks in India. In doing so, we first estimate the time‐varying jumps in oil market implied volatility index (OVX) and then augment the heterogeneous autoregressive (HAR) process with the information on such jumps. Our sample runs from December 2012 to April 2022, which includes 2328 data points. Comparing a range of HAR‐type models, we find that crude oil volatility jumps provide additional information, which is not contained even in the OVX index itself. In particular, the HAR–RV model that considers both leverage effects and the information on volatility jumps produces superior forecasts compared with the existing approaches. The economic significance of these results is also supported by a simple value‐at‐risk analysis.

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Section: Empirical Findingssupporting

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Impact of crude oil volatility jumps on sustainable investments: Evidence from India

Dutta

Kanjilal

Ghosh

et al. 2023

Journal of Futures Markets

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“…Therefore, fluctuations in regional carbon price in China depend not only on their historical time series, but also on lots of external factors. Guo et al(2022) [26] found that energy price can be utilized to predict regional carbon price. Based on the structural VAR model, Zeng et al [27] stated that the carbon price was correlated with its own historical price, domestic energy prices and economic factors.…”

Section: Plos Onementioning

confidence: 99%

Predicting regional carbon price in China based on multi-factor HKELM by combining secondary decomposition and ensemble learning

Hu,

Cheng

2023

PLoS ONE

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Accurately predicting carbon price is crucial for risk avoidance in the carbon financial market. In light of the complex characteristics of the regional carbon price in China, this paper proposes a model to forecast carbon price based on the multi-factor hybrid kernel-based extreme learning machine (HKELM) by combining secondary decomposition and ensemble learning. Variational mode decomposition (VMD) is first used to decompose the carbon price into several modes, and range entropy is then used to reconstruct these modes. The multi-factor HKELM optimized by the sparrow search algorithm is used to forecast the reconstructed subsequences, where the main external factors innovatively selected by maximum information coefficient and historical time-series data on carbon prices are both considered as input variables to the forecasting model. Following this, the improved complete ensemble-based empirical mode decomposition with adaptive noise and range entropy are respectively used to decompose and reconstruct the residual term generated by VMD. Finally, the nonlinear ensemble learning method is introduced to determine the predictions of residual term and final carbon price. In the empirical analysis of Guangzhou market, the root mean square error(RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of the model are 0.1716, 0.1218 and 0.0026, respectively. The proposed model outperforms other comparative models in predicting accuracy. The work here extends the research on forecasting theory and methods of predicting the carbon price.

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Assessing the extreme risk spillovers to carbon markets from energy markets: evidence from China

Qin

2022

Environ Sci Pollut Res

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Can energy predict the regional prices of carbon emission allowances in China?

Cited by 7 publications

References 39 publications

Impact of crude oil volatility jumps on sustainable investments: Evidence from India

Impact of crude oil volatility jumps on sustainable investments: Evidence from India

Predicting regional carbon price in China based on multi-factor HKELM by combining secondary decomposition and ensemble learning

Assessing the extreme risk spillovers to carbon markets from energy markets: evidence from China

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