Forecasting Carbon Dioxide Price Using a Time-Varying High-Order Moment Hybrid Model of NAGARCHSK and Gated Recurrent Unit Network

Yun, Po; Zhang, Chen; Wu, Yaqi; Yu, Yang

doi:10.3390/ijerph19020899

Cited by 12 publications

(9 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e time-varying and directional spillover between carbon and energy markets have detected the electricity market is the main net information receiver affected by the carbon market [27]. Additionally, the complex time-frequency and neural network mechanism between carbon and oil markets has been explored by the model of novelty partial wavelet and deep learning models [28][29][30].…”

Section: Research On Spillover Effect Between Carbon Market and Energymentioning

confidence: 99%

High-Order Moment Contagion of the Carbon Market: A Heterogeneity Analysis of Market Volatility Trend

Yun

Sun

2022

Security and Communication Networks

Self Cite

View full text Add to dashboard Cite

Information asymmetry and extreme events shocks can lead to the phenomenon of significant carbon market contagion. However, the existing studies mainly focus on the low-order moment of carbon price, making it difficult to reveal the risk contagion characteristic caused by irrational behaviors and policy shocks. This article takes market skewness and kurtosis into the research framework and constructs the FR, CS, and CK statistical model to detect the contagion in correlation channel, coskewness channel, and cokurtosis channel, respectively. The contribution of this article is to reveal the significant high-order moment contagion channel and strength of carbon market to its infected market under different market volatility trends. The results show significant contagion is widespread from the carbon market to its infected markets through the channels of coskewness and cokurtosis in different volatility trends. Additionally, the contagion strength in volatility rapid and slowly rise trend is generally higher than in the volatility rapid and slowly decline trend. That is to say, the shock of market irrationality and external events in the carbon market measured by the high-order moment contagion channels are essential risk factors that affect its infected markets. Those results convince that the acceptance of significant contagion sourced from the carbon market varies for different infected markets.

show abstract

Section: Research On Spillover Effect Between Carbon Market and Energymentioning

confidence: 99%

High-Order Moment Contagion of the Carbon Market: A Heterogeneity Analysis of Market Volatility Trend

Yun

Sun

2022

Security and Communication Networks

Self Cite

View full text Add to dashboard Cite

show abstract

“…Numerous studies on predicting and analyzing the correlations of carbon emission trading are being conducted worldwide. As mentioned in the literature review section, GARCH models have shown great potential in conducting such analysis [34][35][36][37][38]. Hence, they must be considered in future studies predicting and analyzing the correlations of KAU and different sources as different markets, environmental issues, and status in Korea.…”

Section: Discussionmentioning

confidence: 99%

“…On the other hand, it also makes it very complicated when conducting multivariate GARCH analysis due to the extremely high number of parameters. Yun et al [38] suggested a new hybrid model, NAGARCHSK-GRU, with better accuracy and robustness for forecasting carbon price than ordinal prediction models. Those recent studies illustrate a branch of the literature using GARCH and hybrid models for analyzing correlations and predicting carbon emission prices.…”

Section: Literature Reviews Relevant To the Prediction Of Carbon Emis...mentioning

confidence: 99%

Performance Comparison of Predictive Methodologies for Carbon Emission Credit Price in the Korea Emission Trading System

Kim

et al. 2022

Sustainability

View full text Add to dashboard Cite

Research related to the carbon-emission credit-price prediction model has only considered the effects of specific indicators, such as coal and oil prices, and only long-term prediction studies have been conducted. Recently, carbon emission credits have been recognized as investment assets, such as stocks and real estate. Accordingly, a carbon-emission credit prediction method is needed to establish an industrial strategy with low risk. In this study, an attempt was made to model the behavior of market participants in the time series model by analyzing the correlation between the search query volume data and the Korean Allowance Unit (KAU). Multiple Linear Regression Analysis (MRA) and Auto-Regressive Integrated Moving Average models were developed. In all price prediction models, the error of the prediction model at the 4th time was low. In the case of MRA, the error in the predicted near future price was small, but the error rate increased with increasing analysis period and prediction time. The error rate of ARIMA was lower than that of MRA, but it did not show a rapid change. These research findings will be beneficial to investigating and finding more rigid and reliable methodologies that can be used to predict various important values in similar fields in the future.

show abstract

“…More neuron nodes can improve the training and generalization ability of the proposed model, but it may also cost more training time and produce an overfitting phenomenon. 47 Therefore, referring to the experience of Shen et al 48 and Yun et al, 49 this paper uses the step-by-step experimental method to determine the optimal network parameters, specifically, we calculate the model training error when the iteration are 50, 100, 200, 300 and the neuron nodes of the proposed model are 4, 8, 16, 32, 64, and 128 separately. The results conclude that when the iteration number is 200 and the neuron node is 128, the error indicators of RMSE, MAE, and RMSE are 0.851783, 0.601614, and 0.021578, respectively (as shown in Table 3), that is the minimum value of the whole test sample.…”

Section: Mode Characteristic Analysis Of Carbon Price Signalsmentioning

confidence: 99%

Forecasting carbon dioxide emission price using a novel mode decomposition machine learning hybrid model of CEEMDAN‐LSTM

Yun

Huang

et al. 2022

Energy Science & Engineering

Self Cite

View full text Add to dashboard Cite

Global carbon dioxide emissions have become a great threat to economic sustainability and human health. The carbon market is recognized as the most promising mean to curb carbon emissions, furthermore, carbon price forecasting will promote the role of the carbon market in emissions reduction and achieve reduction targets at lower economic costs for emission entities. However, there are still some technical problems in carbon price prediction, such as mode mixing and larger reconstruction error for the traditional empirical mode decomposition-type models. Therefore, the innovation of this paper is constructing a novel carbon price prediction model of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)-long short-term memory (LSTM), that combines the advantages of CEEMDAN in decomposing the multiscale time-frequency carbon price signals and the LSTM model in fitting the financial signals. The results show the proposed CEEMDAN-LSTM model has significant accuracy in predicting the complex carbon price signals. The prediction error and expectation indicators of root mean square error, mean absolute error, mean absolute percentage error, and direction accuracy are 0.638342, 0.448695, 0.015666, and 0.687631, respectively, which is better than other benchmark models. Further evidence convince that the short-term forecasting performance is superior to the long-term and medium-term performance. That evidence concludes that the proposed model is a reliable method to reveal the carbon price-driving mechanism from the point of multiscale time-frequency characteristics. Particularly, short-term forecasting is more accurate and can provide a valuable technical reference for reduction entities and green financial companies to judge the market situation and formulate quantitative transactions.

show abstract

Forecasting Carbon Dioxide Price Using a Time-Varying High-Order Moment Hybrid Model of NAGARCHSK and Gated Recurrent Unit Network

Cited by 12 publications

References 44 publications

High-Order Moment Contagion of the Carbon Market: A Heterogeneity Analysis of Market Volatility Trend

High-Order Moment Contagion of the Carbon Market: A Heterogeneity Analysis of Market Volatility Trend

Performance Comparison of Predictive Methodologies for Carbon Emission Credit Price in the Korea Emission Trading System

Forecasting carbon dioxide emission price using a novel mode decomposition machine learning hybrid model of CEEMDAN‐LSTM

Contact Info

Product

Resources

About