IDEAS home Printed from https://ideas.repec.org/a/eee/eneeco/v139y2024ics0140988324006601.html
   My bibliography  Save this article

Forecasting interval carbon price through a multi-scale interval-valued decomposition ensemble approach

Author

Listed:
  • Yang, Kun
  • Sun, Yuying
  • Hong, Yongmiao
  • Wang, Shouyang

Abstract

This paper proposes a novel Multi-scale Interval-valued Decomposition Ensemble (MIDE) framework for forecasting European Union Allowance (EUA) carbon futures prices, which integrates Noise-assisted Multivariate Empirical Mode Decomposition (NAMEMD), Interval-valued Vector Auto-Regressive (IVAR) model, Interval Event Analysis (IEA) method, and Interval Multi-Layer Perceptron (IMLP). First, the original interval-valued carbon prices with other interval-valued control variables are decomposed and integrated into high, medium, and low-frequency components by NAMEMD. Second, IVAR is used to investigate the dynamics of the interval-valued vector system in low-frequency components, while IMLP is employed to characterize the high-frequency components. Besides, the interval event analysis investigates typical events that significantly impact carbon prices in the medium-frequency component. Furthermore, empirical findings indicate that our proposed MIDE learning approach significantly outperforms some other benchmark models in out-of-sample forecasting.

Suggested Citation

  • Yang, Kun & Sun, Yuying & Hong, Yongmiao & Wang, Shouyang, 2024. "Forecasting interval carbon price through a multi-scale interval-valued decomposition ensemble approach," Energy Economics, Elsevier, vol. 139(C).
    • Handle: RePEc:eee:eneeco:v:139:y:2024:i:c:s0140988324006601
    DOI: 10.1016/j.eneco.2024.107952
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0140988324006601
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.eneco.2024.107952?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Wei Yang & Ai Han & Yongmiao Hong & Shouyang Wang, 2016. "Analysis of crisis impact on crude oil prices: a new approach with interval time series modelling," Quantitative Finance, Taylor & Francis Journals, vol. 16(12), pages 1917-1928, December.
    2. Eslahi, Mohammadehsan & Mazza, Paolo, 2023. "Can weather variables and electricity demand predict carbon emissions allowances prices? Evidence from the first three phases of the EU ETS," Ecological Economics, Elsevier, vol. 214(C).
    3. Huang, Yumeng & Dai, Xingyu & Wang, Qunwei & Zhou, Dequn, 2021. "A hybrid model for carbon price forecastingusing GARCH and long short-term memory network," Applied Energy, Elsevier, vol. 285(C).
    4. Sun, Yuying & Han, Ai & Hong, Yongmiao & Wang, Shouyang, 2018. "Threshold autoregressive models for interval-valued time series data," Journal of Econometrics, Elsevier, vol. 206(2), pages 414-446.
    5. Jiqian Wang & Xiaozhu Guo & Xueping Tan & Julien Chevallier & Feng Ma, 2023. "Which exogenous driver is informative in forecasting European carbon volatility: Bond, commodity, stock or uncertainty?," Post-Print halshs-04250316, HAL.
    6. Yu, Jongmin & Mallory, Mindy L., 2014. "Exchange rate effect on carbon credit price via energy markets," Journal of International Money and Finance, Elsevier, vol. 47(C), pages 145-161.
    7. Jiménez-Rodríguez, Rebeca, 2019. "What happens to the relationship between EU allowances prices and stock market indices in Europe?," Energy Economics, Elsevier, vol. 81(C), pages 13-24.
    8. Bangzhu Zhu & Shunxin Ye & Kaijian He & Julien Chevallier & Rui Xie, 2019. "Measuring the risk of European carbon market: an empirical mode decomposition-based value at risk approach," Annals of Operations Research, Springer, vol. 281(1), pages 373-395, October.
    9. Lei, Heng & Xue, Minggao & Liu, Huiling, 2022. "Probability distribution forecasting of carbon allowance prices: A hybrid model considering multiple influencing factors," Energy Economics, Elsevier, vol. 113(C).
    10. Yu, Lean & Li, Jingjing & Tang, Ling & Wang, Shuai, 2015. "Linear and nonlinear Granger causality investigation between carbon market and crude oil market: A multi-scale approach," Energy Economics, Elsevier, vol. 51(C), pages 300-311.
    11. Qiao, Kenan & Sun, Yuying & Wang, Shouyang, 2019. "Market inefficiencies associated with pricing oil stocks during shocks," Energy Economics, Elsevier, vol. 81(C), pages 661-671.
    12. Wang, Piao & Tao, Zhifu & Liu, Jinpei & Chen, Huayou, 2023. "Improving the forecasting accuracy of interval-valued carbon price from a novel multi-scale framework with outliers detection: An improved interval-valued time series analysis mode," Energy Economics, Elsevier, vol. 118(C).
    13. Zhang, Xun & Yu, Lean & Wang, Shouyang & Lai, Kin Keung, 2009. "Estimating the impact of extreme events on crude oil price: An EMD-based event analysis method," Energy Economics, Elsevier, vol. 31(5), pages 768-778, September.
    14. Byun, Suk Joon & Cho, Hangjun, 2013. "Forecasting carbon futures volatility using GARCH models with energy volatilities," Energy Economics, Elsevier, vol. 40(C), pages 207-221.
    15. Duan, Kun & Wang, Rui & Chen, Shun & Ge, Lei, 2023. "Exploring the predictability of attention mechanism with LSTM: Evidence from EU carbon futures prices," Research in International Business and Finance, Elsevier, vol. 66(C).
    16. Jonathan Berrisch & Sven Pappert & Florian Ziel & Antonia Arsova, 2022. "Modeling Volatility and Dependence of European Carbon and Energy Prices," Papers 2208.14311, arXiv.org, revised Feb 2023.
    17. Mohammadehsan Eslahi & Paolo Mazza, 2023. "Can weather variables and electricity demand predict carbon emissions allowances prices? Evidence from the first three phases of the EU ETS," Post-Print hal-04282454, HAL.
    18. Zhao, Xin & Han, Meng & Ding, Lili & Kang, Wanglin, 2018. "Usefulness of economic and energy data at different frequencies for carbon price forecasting in the EU ETS," Applied Energy, Elsevier, vol. 216(C), pages 132-141.
    19. Zhu, Bangzhu & Wan, Chunzhuo & Wang, Ping, 2022. "Interval forecasting of carbon price: A novel multiscale ensemble forecasting approach," Energy Economics, Elsevier, vol. 115(C).
    20. Dabin Zhang & Qian Li & Amin W. Mugera & Liwen Ling, 2020. "A hybrid model considering cointegration for interval‐valued pork price forecasting in China," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1324-1341, December.
    21. Aslan, Aydin & Posch, Peter N., 2022. "Does carbon price volatility affect European stock market sectors? A connectedness network analysis," Finance Research Letters, Elsevier, vol. 50(C).
    22. Bangzhu Zhu & Shunxin Ye & Kaijian He & Julien Chevallier & Rui Xie, 2019. "Measuring the risk of European carbon market: an empirical mode decomposition-based value at risk approach," Post-Print halshs-04250221, HAL.
    23. Guo, Xiaozhu & Huang, Yisu & Liang, Chao & Umar, Muhammad, 2022. "Forecasting volatility of EUA futures: New evidence," Energy Economics, Elsevier, vol. 110(C).
    24. Chang-Jing Ji & Yu-Jie Hu & Bao-Jun Tang, 2018. "Research on carbon market price mechanism and influencing factors: a literature review," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 92(2), pages 761-782, June.
    25. Gao, Feng & Shao, Xueyan, 2022. "A novel interval decomposition ensemble model for interval carbon price forecasting," Energy, Elsevier, vol. 243(C).
    26. Lu, Quanying & Li, Yuze & Chai, Jian & Wang, Shouyang, 2020. "Crude oil price analysis and forecasting: A perspective of “new triangle”," Energy Economics, Elsevier, vol. 87(C).
    27. Bekiroglu, Korkut & Duru, Okan & Gulay, Emrah & Su, Rong & Lagoa, Constantino, 2018. "Predictive analytics of crude oil prices by utilizing the intelligent model search engine," Applied Energy, Elsevier, vol. 228(C), pages 2387-2397.
    28. Kumar, Pawan & Singh, Vipul Kumar, 2022. "Does crude oil fire the emerging markets currencies contagion spillover? A systemic perspective," Energy Economics, Elsevier, vol. 116(C).
    29. Yanan He & Ai Han & Yongmiao Hong & Yuying Sun & Shouyang Wang, 2021. "Forecasting crude oil price intervals and return volatility via autoregressive conditional interval models," Econometric Reviews, Taylor & Francis Journals, vol. 40(6), pages 584-606, July.
    30. Engle, Robert F, 1974. "Band Spectrum Regression," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 15(1), pages 1-11, February.
    31. Ai Han & Yongmiao Hong & Shouyang Wang & Xin Yun, 2016. "A Vector Autoregressive Moving Average Model for Interval-Valued Time Series Data," Advances in Econometrics, in: Essays in Honor of Aman Ullah, volume 36, pages 417-460, Emerald Group Publishing Limited.
    32. Quanying Lu & Yuying Sun & Yongmiao Hong & Shouyang Wang, 2022. "Forecasting interval-valued crude oil prices using asymmetric interval models," Quantitative Finance, Taylor & Francis Journals, vol. 22(11), pages 2047-2061, November.
    33. Mittal, Mandeep & Sarkar, Biswajit, 2023. "Stochastic behavior of exchange rate on an international supply chain under random energy price," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 205(C), pages 232-250.
    34. Berrisch, Jonathan & Pappert, Sven & Ziel, Florian & Arsova, Antonia, 2023. "Modeling volatility and dependence of European carbon and energy prices," Finance Research Letters, Elsevier, vol. 52(C).
    35. Sun, Shaolong & Sun, Yuying & Wang, Shouyang & Wei, Yunjie, 2018. "Interval decomposition ensemble approach for crude oil price forecasting," Energy Economics, Elsevier, vol. 76(C), pages 274-287.
    36. Wang, Jiqian & Guo, Xiaozhu & Tan, Xueping & Chevallier, Julien & Ma, Feng, 2023. "Which exogenous driver is informative in forecasting European carbon volatility: Bond, commodity, stock or uncertainty?," Energy Economics, Elsevier, vol. 117(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yiling Ye & Xiaowen Zhuang & Cai Yi & Dinggao Liu & Zhenpeng Tang, 2025. "Enhancing Agricultural Futures Return Prediction: Insights from Rolling VMD, Economic Factors, and Mixed Ensembles," Agriculture, MDPI, vol. 15(11), pages 1-33, May.
    2. Rulin Gao & Jingyun Sun, 2025. "A Novel Forecasting Framework for Carbon Emission Trading Price Based on Nonlinear Integration," Mathematics, MDPI, vol. 13(10), pages 1-22, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Piao Wang & Shahid Hussain Gurmani & Zhifu Tao & Jinpei Liu & Huayou Chen, 2024. "Interval time series forecasting: A systematic literature review," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 249-285, March.
    2. Yan, Zichun & Tian, Fangzhu & Sun, Yuying & Wang, Shouyang, 2024. "A time-frequency-based interval decomposition ensemble method for forecasting gasoil prices under the trend of low-carbon development," Energy Economics, Elsevier, vol. 134(C).
    3. Liu, Shuihan & Li, Mingchen & Yang, Kun & Wei, Yunjie & Wang, Shouyang, 2025. "From forecasting to trading: A multimodal-data-driven approach to reversing carbon market losses," Energy Economics, Elsevier, vol. 144(C).
    4. Wang, Zhengzhong & Wei, Yunjie & Wang, Shouyang, 2025. "Forecasting the carbon price of China's national carbon market: A novel dynamic interval-valued framework," Energy Economics, Elsevier, vol. 141(C).
    5. Zhao, Yang & Wang, Jianzhou & Wang, Shuai & Zheng, Jingwei & Lv, Mengzheng, 2025. "Using explainable deep learning to improve decision quality: Evidence from carbon trading market," Omega, Elsevier, vol. 133(C).
    6. Zheng, Li & Sun, Yuying & Wang, Shouyang, 2024. "A novel interval-based hybrid framework for crude oil price forecasting and trading," Energy Economics, Elsevier, vol. 130(C).
    7. Zhu, Mengrui & Xu, Hua & Wang, Minggang & Tian, Lixin, 2024. "Carbon price interval prediction method based on probability density recurrence network and interval multi-layer perceptron," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 636(C).
    8. Lei, Heng & Xue, Minggao & Liu, Huiling, 2022. "Probability distribution forecasting of carbon allowance prices: A hybrid model considering multiple influencing factors," Energy Economics, Elsevier, vol. 113(C).
    9. Wang, Xuerui & Wang, Lin & An, Wuyue, 2024. "Probability density prediction for carbon allowance prices based on TS2Vec and distribution Transformer," Energy Economics, Elsevier, vol. 140(C).
    10. Xingyu Dai & Roy Cerqueti & Qunwei Wang & Ling Xiao, 2025. "Volatility forecasting: a new GARCH-type model for fuzzy sets-valued time series," Annals of Operations Research, Springer, vol. 348(1), pages 735-775, May.
    11. Wang, Piao & Tao, Zhifu & Liu, Jinpei & Chen, Huayou, 2023. "Improving the forecasting accuracy of interval-valued carbon price from a novel multi-scale framework with outliers detection: An improved interval-valued time series analysis mode," Energy Economics, Elsevier, vol. 118(C).
    12. Katarzyna Rudnik & Anna Hnydiuk-Stefan & Aneta Kucińska-Landwójtowicz & Łukasz Mach, 2022. "Forecasting Day-Ahead Carbon Price by Modelling Its Determinants Using the PCA-Based Approach," Energies, MDPI, vol. 15(21), pages 1-23, October.
    13. Cristiano Salvagnin & Aldo Glielmo & Maria Elena De Giuli & Antonietta Mira, 2024. "Investigating the price determinants of the European Emission Trading System: a non-parametric approach," Quantitative Finance, Taylor & Francis Journals, vol. 24(10), pages 1529-1544, October.
    14. Liu, Shuihan & Xie, Gang & Wang, Zhengzhong & Wang, Shouyang, 2024. "A secondary decomposition-ensemble framework for interval carbon price forecasting," Applied Energy, Elsevier, vol. 359(C).
    15. Sun, Yuying & Zhang, Xinyu & Wan, Alan T.K. & Wang, Shouyang, 2022. "Model averaging for interval-valued data," European Journal of Operational Research, Elsevier, vol. 301(2), pages 772-784.
    16. Zhao, Yuan & Gong, Xue & Zhang, Weiguo & Xu, Weijun, 2024. "Forecasting carbon futures returns using feature selection and Markov chain with sample distribution," Energy Economics, Elsevier, vol. 140(C).
    17. Dinggao Liu & Liuqing Wang & Shuo Lin & Zhenpeng Tang, 2025. "A Novel Multi-Task Learning Framework for Interval-Valued Carbon Price Forecasting Using Online News and Search Engine Data," Mathematics, MDPI, vol. 13(3), pages 1-23, January.
    18. Chu, Wen-Jun & Fan, Li-Wei & Zhou, P., 2024. "Extreme spillovers across carbon and energy markets: A multiscale higher-order moment analysis," Energy Economics, Elsevier, vol. 138(C).
    19. Li, Jingmiao & Liu, Dehong, 2023. "Carbon price forecasting based on secondary decomposition and feature screening," Energy, Elsevier, vol. 278(PA).
    20. Wenjie Xu & Jujie Wang & Yue Zhang & Jianping Li & Lu Wei, 2025. "An optimized decomposition integration framework for carbon price prediction based on multi-factor two-stage feature dimension reduction," Annals of Operations Research, Springer, vol. 345(2), pages 1229-1266, February.

    More about this item

    Keywords

    Interval carbon price; Decomposed; Event analysis; Machine learning; Forecasting;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:eneeco:v:139:y:2024:i:c:s0140988324006601. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eneco .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.