Probability distribution forecasting of carbon allowance prices: A hybrid model considering multiple influencing factors

Lei, Heng; Xue, Minggao; Liu, Huiling

doi:10.1016/j.eneco.2022.106189

Cited by 13 publications

(5 citation statements)

References 73 publications

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“…Trading features of CEA are lacking to form well-designed structure and pricing theory of derivatives. 68 , 69 A more profound study of the nature and characteristics carbon market is needed for elegant derivatives.…”

Section: Discussionmentioning

confidence: 99%

Influence of carbon derivatives on carbon capture investments in coal-based power sector, a China perspective

Wang,

Wang

2023

iScience

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Section: Discussionmentioning

confidence: 99%

Influence of carbon derivatives on carbon capture investments in coal-based power sector, a China perspective

Wang,

Wang

2023

iScience

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“…These factors include the stock market, the exchange rate, and so forth. 55 Changing stock market prices may reflect changes in the market's expectations of economic development, which in turn affects the investment and production behavior of businesses, ultimately affecting carbon prices. Changes in exchange rates can affect supply and demand on international energy and carbon markets, which affect carbon prices.…”

Section: Primary Index System Establishmentmentioning

confidence: 99%

“…">Economic factors: Economic factors play an important role in determining carbon prices. These factors include the stock market, the exchange rate, and so forth 55 . Changing stock market prices may reflect changes in the market's expectations of economic development, which in turn affects the investment and production behavior of businesses, ultimately affecting carbon prices.…”

Section: Data and Evaluating Indicatorsmentioning

confidence: 99%

Forecasting carbon price using signal processing technology and extreme gradient boosting optimized by the whale optimization algorithm

Duan,

Zhang,

Wang

et al. 2024

Energy Science & Engineering

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Predicting carbon prices is crucial for the growth of China's carbon trading industry. This paper proposes a residual correction model that considers multiple influencing factors. First, the best historical data and main external factors input by the model are determined by using the partial autocorrelation function and Spearman correlation analysis, and the carbon price forecasting index system is constructed. Second, the whale optimization algorithm (WOA) is utilized to determine the optimal parameters of the extreme gradient boosting (XGBoost), and the WOA‐XGBoost model is built to perform preliminary carbon price forecasts and obtain the residual series. Finally, the carbon price residual series undergoes decomposition into multiple components utilizing the complete ensemble empirical mode decomposition for subsequent forecasting and the aggregation of outcomes. Experiments are conducted to predict two carbon trading markets in Hubei and Guangzhou, and a feature importance analysis is performed. The results indicate that the proposed hybrid model consistently outperforms the comparative models in terms of prediction accuracy. Furthermore, it is revealed that historical carbon prices and European Union carbon prices are the key factors influencing the prediction of carbon market prices.

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“…of the uncertainty of the carbon price and can provide useful information related to carbon price forecasting [23][24][25]. Therefore, fluctuations in regional carbon price in China depend not only on their historical time series, but also on lots of external factors.…”

Section: Plos Onementioning

confidence: 99%

“…The above research ignored the role of external factors on carbon price. External factors, including economic activities, energy price, and environmental factors, are significant sources of the uncertainty of the carbon price and can provide useful information related to carbon price forecasting [ 23 – 25 ]. Therefore, fluctuations in regional carbon price in China depend not only on their historical time series, but also on lots of external factors.…”

Section: Literature Reviewmentioning

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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Probability distribution forecasting of carbon allowance prices: A hybrid model considering multiple influencing factors

Cited by 13 publications

References 73 publications

Influence of carbon derivatives on carbon capture investments in coal-based power sector, a China perspective

Influence of carbon derivatives on carbon capture investments in coal-based power sector, a China perspective

Forecasting carbon price using signal processing technology and extreme gradient boosting optimized by the whale optimization algorithm

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

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