Interval Forecasting of Carbon Futures Prices Using a Novel Hybrid Approach with Exogenous Variables

Zhang, Lu; Zhang, Junbiao; Xiong, Tao; Su, Chiao

doi:10.1155/2017/5730295

Cited by 11 publications

(4 citation statements)

References 39 publications

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“…According to the experimental design in Section 2.1.3, we summarize the use of models as follows: for the homogeneous ensemble framework, FAR was chosen as the base model and filtered by MMS (FAR+MMS); for the heterogeneous ensemble framework, MMA, LASSO, Ridge regression, E-net, SVR, RF, and XGBoost were chosen as the base model. The selection of these base models was based on relevant research on carbon market prediction for better comparison (FAR [19], SVR [43,44], RF [45,46], XGBoost [47,48]). Meanwhile, SVR, RF, and XGBoost are also used as the meta-model for two ensemble frameworks to form six ensemble models (homo_svr,homo_rf,homo_xgb;hete_svr,hete_rf,hete_xgb).…”

Section: Forecasting Modelsmentioning

confidence: 99%

Forecasting the Return of Carbon Price in the Chinese Market Based on an Improved Stacking Ensemble Algorithm

Siddik

2023

Energies

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Recently, carbon price forecasting has become critical for financial markets and environmental protection. Due to their dynamic, nonlinear, and high noise characteristics, predicting carbon prices is difficult. Machine learning forecasting often uses stacked ensemble algorithms. As a result, common stacking has many limitations when applied to time series data, as its cross-validation process disrupts the temporal sequentiality of the data. Using a double sliding window scheme, we proposed an improved stacking ensemble algorithm that avoided overfitting risks and maintained temporal sequentiality. We replaced cross-validation with walk-forward validation. Our empirical experiment involved the design of two dynamic forecasting frameworks utilizing the improved algorithm. This incorporated forecasting models from different domains as base learners. We used three popular machine learning models as the meta-model to integrate the predictions of each base learner, further narrowing the gap between the final predictions and the observations. The empirical part of this study used the return of carbon prices from the Shenzhen carbon market in China as the prediction target. This verified the enhanced accuracy of the modified stacking algorithm through the use of five statistical metrics and the model confidence set (MCS). Furthermore, we constructed a portfolio to examine the practical usefulness of the improved stacking algorithm. Empirical results showed that the improved stacking algorithm could significantly and robustly improve model prediction accuracy. Support vector machines (SVR) aggregated results better than the other two meta-models (Random forest and XGBoost) in the aggregation step. In different volatility states, the modified stacking algorithm performed differently. We also found that aggressive investment strategies can help investors achieve higher investment returns with carbon option assets.

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Section: Forecasting Modelsmentioning

confidence: 99%

Forecasting the Return of Carbon Price in the Chinese Market Based on an Improved Stacking Ensemble Algorithm

Siddik

2023

Energies

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“…e third category is a combination prediction model based on multiple methods, such as an integrated model of group method of data handling (GMDH), particle swarm optimization (PSO), and least squares support vector machines (LSSVM), that is, GMDH-PSO-LSSVM [19], the integrated model of empirical mode decomposition (EMD), particle swarm optimization (PSO), and support vector machines (SVM), that is, EMD-PSO-SVM [20], the hybrid ARIMA and LSSVM methodology [21], the hybrid approach with exogenous variables [22], the combination of the model based on phase space reconstruction (PSR) and the least squares support vector regression (LSSVR) model [23], the multiscale nonlinear ensemble leaning paradigm [24], the variational mode decomposition and optimal combined model [25], the model based on secondary decomposition algorithm and optimized back propagation neural network [26], the particle swarm optimization (PSO) and radial basis function (RBF) algorithm model [27], the prediction model based on extremum point symmetric mode decomposition, the extreme learning machine and Grey Wolf optimization algorithm [28], and the hybrid method based on empirical wavelet transform (EWT) and Gated Recursive Unit Neural Network (GRU) [29]. is kind of model effectively integrates and utilizes the advantages of the single prediction model, and its prediction accuracy is significantly higher than that of the single prediction model.…”

Section: Introductionmentioning

confidence: 99%

A Novel Carbon Price Fluctuation Trend Prediction Method Based on Complex Network and Classification Algorithm

Wang

2021

Complexity

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Carbon price fluctuation is affected by both internal market mechanisms and the heterogeneous environment. Moreover, it is a complex dynamic evolution process. This paper focuses on carbon price fluctuation trend prediction. In order to promote the accuracy of the forecasting model, this paper proposes the idea of integrating network topology information into carbon price data; that is, carbon price data are mapped into a complex network through a visibility graph algorithm, and the network topology information is extracted. The extracted network topology structure information is used to reconstruct the data, which are used to train the model parameters, thus improving the prediction accuracy of the model. Five prediction models are selected as the benchmark model, and the price data of the EU and seven pilot carbon markets in China from June 19, 2014, to October 9, 2020, are chosen as the sample for empirical analysis. The research finds that the integration of network topology information can significantly improve the price trend prediction of the five benchmark models for the EU carbon market. However, there are great differences in the accuracy improvement effects of China’s seven pilot carbon market price forecasts. Moreover, the forecasting accuracy of the four carbon markets (i.e., Guangdong, Chongqing, Tianjin, and Shenzhen) has improved slightly, but the prediction accuracy of the carbon price trend in Beijing, Shanghai, and Hubei has not improved. We analyze the reasons leading to this result and offer suggestions to improve China’s pilot carbon market.

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“…Whereas time series forecasting only uses the historical data of carbon price, and time-lagged factors can characterize the development trend and law of original data, which has been researched [5][6][7][8]. Incorporating external and internal influencing factors into account comprehensively enables us to extract sufficient information as for precise predictions and countermeasures formulation [9,10]. However, as far as is known, few relevant papers comprehensively consider external factors and internal factors when predicting carbon price.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Carbon Price Forecasting Model with External and Internal Influencing Factors Considered Comprehensively: A Case Study from China

Sun¹,

Cui-ping²,

Li³

2020

Pol. J. Environ. Stud.

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With the continuous emission of greenhouse gases, the carbon trading market has become a powerful weapon to contain it. It is indispensable to analyze the carbon price of China that acts as the largest emitter of carbon dioxide worldwide. Therefore, this paper proposes an innovative hybrid carbon price forecasting model that incorporates fast ensemble empirical mode decomposition (FEEMD) and extreme learning machine optimized by particle swarm optimization (PSO-ELM) with external and internal influencing factors considered. The original carbon price series are disassembled into several intrinsic mode functions (IMFs) and one residual via FEEMD. The PSO-ELM is then employed to forecast the sub-series. It's remarkable that the inputs of the PSO-ELM model are divided into external and internal influencing factors. Factor analysis is used to extract potential factors from energy prices, macroeconomics and other influencing factors associated with the original carbon price as external influencing factors, and the partial autocorrelation function (PACF) is exploited to select internal influencing factors. A case study in Hubei Province, China shows that the proposed carbon price forecasting model is superior to the contrast models in terms of the smallest prediction error (MAE = 0.1274 yuan, MAPE = 0.8368%) and the strongest stability (RMSE = 0.0116 yuan). And the forecasting results demonstrate that the developed model with external and internal influencing factors considered can highly improve carbon price prediction performance and have potential in a wider range of carbon price forecasting. In addition, accurate carbon price forecasting can help the government realize macro control and the investors fulfill risk minimization.

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Interval Forecasting of Carbon Futures Prices Using a Novel Hybrid Approach with Exogenous Variables

Cited by 11 publications

References 39 publications

Forecasting the Return of Carbon Price in the Chinese Market Based on an Improved Stacking Ensemble Algorithm

Forecasting the Return of Carbon Price in the Chinese Market Based on an Improved Stacking Ensemble Algorithm

A Novel Carbon Price Fluctuation Trend Prediction Method Based on Complex Network and Classification Algorithm

A Hybrid Carbon Price Forecasting Model with External and Internal Influencing Factors Considered Comprehensively: A Case Study from China

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