Framework for multivariate carbon price forecasting: A novel hybrid model

The complex characteristics of volatility and non-linearity of carbon price pose a serious challenge to accurately predict carbon price. Therefore, this study proposes a new hybrid model for multivariate carbon price forecasting, including feature selection, deep learning, intelligent optimization a...

Full description

Saved in:
Bibliographic Details
Published in:Journal of environmental management Vol. 369; p. 122275
Main Authors: Zhang, Xuankai, Zong, Ying, Du, Pei, Wang, Shubin, Wang, Jianzhou
Format: Journal Article
Language:English
Published: England Elsevier Ltd 01.10.2024
Subjects:
Algorithms
Carbon
Carbon price prediction
China
Commerce
Deep learning
Forecasting
Hybrid forecasting model
Intelligent optimization algorithm
Models, Theoretical
China
Deep learning
Carbon price prediction
Intelligent optimization algorithm
Hybrid forecasting model
ISSN:0301-4797, 1095-8630, 1095-8630
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The complex characteristics of volatility and non-linearity of carbon price pose a serious challenge to accurately predict carbon price. Therefore, this study proposes a new hybrid model for multivariate carbon price forecasting, including feature selection, deep learning, intelligent optimization algorithms, model combination and evaluation indicators. First, this study collects and organizes the historical carbon price series of Hubei and Shanghai as well as the influencing factors in five dimensions including structured and unstructured data, totaling twenty variables. Second, data dimensionality reduction is performed and input variables are obtained using the least absolute shrinkage and selection operator, followed by the introduction of nine advanced deep learning models to predict carbon price and compare the prediction effects. Then, through the combination of models, three models with the best performance are combined with Pelican optimization algorithm to construct a hybrid forecasting model. Finally, the experimental results show that the developed forecasting model outperforms other comparation models in terms of prediction accuracy, stability and statistical hypothesis testing, and exhibits excellent prediction performance. Furthermore, this study also applies the developed model to European carbon market price prediction and uses the Hubei carbon market as an example for quantitative trading simulation, and the empirical results further verify its robust prediction performance and investment application value. In conclusion, the proposed hybrid prediction model can not only provide high-precision carbon market price prediction for the government and corporate decision makers, but also help investors optimize their trading strategies and improve their returns. •A novel multivariate carbon price forecasting hybrid model is proposed.•Five types of twenty influencing factors are used into carbon price forecasting.•The proposed hybrid model outperforms twelve benchmark models in several ways.•Investment trading of the proposed hybrid model can increase returns at low risks.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0301-4797
1095-8630
1095-8630
DOI:10.1016/j.jenvman.2024.122275