A hybrid model for carbon price forecasting using GARCH and long short-term memory network

Huang, Yumeng; Dai, Xingyu; Wang, Qunwei; Zhou, Dequn

doi:10.1016/j.apenergy.2021.116485

Cited by 192 publications

(63 citation statements)

References 58 publications

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“…Employing the models of ARIMA, CNN, GARCH and LSTM to extract the linear characteristics, hierarchical data structure, long memory characteristics and volatility characteristics of carbon return, respectively, the conclusion suggests that the hybrid model of ARIMA–CNN–LSTM and GARCH-LSTM contribute a lower prediction error [ 35 , 36 ]. Based on similar modeling ideas, the integrated models of EMD–LSTM and that composed of total average EMD with LSTM (MEEMD–LSTM) have also proven to have significant superiority in carbon price prediction [ 37 , 38 ].…”

Section: Literature Reviewmentioning

confidence: 99%

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

Yun

Zhang

et al. 2022

IJERPH

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The carbon market is recognized as the most effective means for reducing global carbon dioxide emissions. Effective carbon price forecasting can help the carbon market to solve environmental problems at a lower economic cost. However, the existing studies focus on the carbon premium explanation from the perspective of return and volatility spillover under the framework of the mean-variance low-order moment. Specifically, the time-varying, high-order moment shock of market asymmetry and extreme policies on carbon price have been ignored. The innovation of this paper is constructing a new hybrid model, NAGARCHSK-GRU, that is consistent with the special characteristics of the carbon market. In the proposed model, the NAGARCHSK model is designed to extract the time-varying, high-order moment parameter characteristics of carbon price, and the multilayer GRU model is used to train the obtained time-varying parameter and improve the forecasting accuracy. The results conclude that the NAGARCHSK-GRU model has better accuracy and robustness for forecasting carbon price. Moreover, the long-term forecasting performance has been proved. This conclusion proves the rationality of incorporating the time-varying impact of asymmetric information and extreme factors into the forecasting model, and contributes to a powerful reference for investors to formulate investment strategies and assist a reduction in carbon emissions.

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Section: Literature Reviewmentioning

confidence: 99%

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

Yun

Zhang

et al. 2022

IJERPH

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“…The carbon price data from the opening date of each pilot until December 31, 2020 were downloaded from the Wind Database ( http://www.wind.com.cn/ ). According to extant literature (e.g., Huang et al, 2021 ; Sun & Huang, 2020 ; Sun & Zhang, 2018 ), the training set and testing set are divided on the basis of an approximate proportion 4:1. Table 1 summarizes the basic situation of each dataset.…”

Section: Datamentioning

confidence: 99%

“…Hence, it is necessary to forecast carbon prices accurately so as to enhance the enthusiasm of market participants. Evidence shows that carbon prices exhibit non-stationarity, nonlinearity, multi-scale and chaos (Fan et al, 2019 ; Huang et al, 2021 ; Manaf et al, 2016 ; Tian & Hao, 2020 ; Zhu et al, 2017 ; Zou & Zhang, 2020 ) and these make predictions difficult. Therefore, capturing features of carbon prices in order to maximise the forecasting precision is of great significance and it has become a challenging task for academic researchers.…”

Section: Introductionmentioning

confidence: 99%

Carbon price prediction for China's ETS pilots using variational mode decomposition and optimized extreme learning machine

Chai

Zhang

2021

Ann Oper Res

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“…The hybrid model is usually more robust than the single models, as it combines and complements the advantages of a single model to improve the forecasting accuracy. The hybrid model is widely used in different domains for dynamic pricing schemes, including the domain of stock [83][84][85]94,95], crude oil [86,88,89], energy [90], carbon [87,91], electricity [92], and gold [93] from 2004 to 2021. Different combinations have been proven to successfully integrate the advantages of different models to improve the performance.…”

Section: Hybrid Modelmentioning

confidence: 99%

Exploring Time-Series Forecasting Models for Dynamic Pricing in Digital Signage Advertising

et al. 2021

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Audience attention is vital in Digital Signage Advertising (DSA), as it has a significant impact on the pricing decision to advertise on those media. Various environmental factors affect the audience attention level toward advertising signage. Fixed-price strategies, which have been applied in DSA for pricing decisions, are generally inefficient at maximizing the potential profit of the service provider, as the environmental factors that could affect the audience attention are changing fast and are generally not considered in the current pricing solutions in a timely manner. Therefore, the time-series forecasting method is a suitable pricing solution for DSA, as it improves the pricing decision by modeling the changes in the environmental factors and audience attention level toward signage for optimal pricing. However, it is difficult to determine an optimal price forecasting model for DSA with the increasing number of available time-series forecasting models in recent years. Based on the 84 research articles reviewed, the data characteristics analysis in terms of linearity, stationarity, volatility, and dataset size is helpful in determining the optimal model for time-series price forecasting. This paper has reviewed the widely used time-series forecasting models and identified the related data characteristics of each model. A framework is proposed to demonstrate the model selection process for dynamic pricing in DSA based on its data characteristics analysis, paving the way for future research of pricing solutions for DSA.

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A hybrid model for carbon price forecasting using GARCH and long short-term memory network

Cited by 192 publications

References 58 publications

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

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

Carbon price prediction for China's ETS pilots using variational mode decomposition and optimized extreme learning machine

Exploring Time-Series Forecasting Models for Dynamic Pricing in Digital Signage Advertising

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