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Forecasting carbon prices in the Shenzhen market, China: The role of mixed-frequency factors

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Cited by:

  1. Chen, Yingqi & Ba, Shusong & Yang, Qing & Yuan, Tian & Zhao, Haibo & Zhou, Ming & Bartocci, Pietro & Fantozzi, Francesco, 2021. "Efficiency of China’s carbon market: A case study of Hubei pilot market," Energy, Elsevier, vol. 222(C).
  2. Zhao, Lili & Wen, Fenghua, 2022. "Risk-return relationship and structural breaks: Evidence from China carbon market," International Review of Economics & Finance, Elsevier, vol. 77(C), pages 481-492.
  3. Gao, Feng & Shao, Xueyan, 2022. "A novel interval decomposition ensemble model for interval carbon price forecasting," Energy, Elsevier, vol. 243(C).
  4. Chang, Kai & Ye, Zhifang & Wang, Weihong, 2019. "Volatility spillover effect and dynamic correlation between regional emissions allowances and fossil energy markets: New evidence from China’s emissions trading scheme pilots," Energy, Elsevier, vol. 185(C), pages 1314-1324.
  5. Xie, Qiwei & Hao, Jingjing & Li, Jingyu & Zheng, Xiaolong, 2022. "Carbon price prediction considering climate change: A text-based framework," Economic Analysis and Policy, Elsevier, vol. 74(C), pages 382-401.
  6. Liao, Haolan & Wu, Di & Wang, Yuhan & Lyu, Zeyu & Sun, Hongmei & Nie, Yongyou & He, He, 2022. "Impacts of carbon trading mechanism on closed-loop supply chain: A case study of stringer pallet remanufacturing," Socio-Economic Planning Sciences, Elsevier, vol. 81(C).
  7. Wang, Jujie & Zhuang, Zhenzhen & Gao, Dongming, 2023. "An enhanced hybrid model based on multiple influencing factors and divide-conquer strategy for carbon price prediction," Omega, Elsevier, vol. 120(C).
  8. Wei Sun & Junjian Zhang, 2020. "Carbon Price Prediction Based on Ensemble Empirical Mode Decomposition and Extreme Learning Machine Optimized by Improved Bat Algorithm Considering Energy Price Factors," Energies, MDPI, vol. 13(13), pages 1-22, July.
  9. Wanhai You & Yuming Huang & Chien‐Chiang Lee, 2024. "Forecasting tourist flows in the COVID‐19 era using nonparametric mixed‐frequency VARs," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 473-489, March.
  10. Miguel A. Jaramillo-Morán & Daniel Fernández-Martínez & Agustín García-García & Diego Carmona-Fernández, 2021. "Improving Artificial Intelligence Forecasting Models Performance with Data Preprocessing: European Union Allowance Prices Case Study," Energies, MDPI, vol. 14(23), pages 1-23, November.
  11. Cheng, Fenfen & Yang, Shanlin & Zhou, Kaile, 2020. "Quantile partial adjustment model with application to predicting energy demand in China," Energy, Elsevier, vol. 191(C).
  12. Sun, Wei & Zhang, Junjian, 2022. "A novel carbon price prediction model based on optimized least square support vector machine combining characteristic-scale decomposition and phase space reconstruction," Energy, Elsevier, vol. 253(C).
  13. Jujie Wang & Shiyao Qiu, 2021. "Improved Multi-Scale Deep Integration Paradigm for Point and Interval Carbon Trading Price Forecasting," Mathematics, MDPI, vol. 9(20), pages 1-20, October.
  14. Parviz Sohrabi & Behshad Jodeiri Shokri & Hesam Dehghani, 2023. "Predicting coal price using time series methods and combination of radial basis function (RBF) neural network with time series," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 36(2), pages 207-216, June.
  15. Beibei Hu & Yunhe Cheng, 2023. "Prediction of Regional Carbon Price in China Based on Secondary Decomposition and Nonlinear Error Correction," Energies, MDPI, vol. 16(11), pages 1-22, May.
  16. Bangzhu Zhu & Jingyi Zhang & Chunzhuo Wan & Julien Chevallier & Ping Wang, 2023. "An evolutionary cost‐sensitive support vector machine for carbon price trend forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 741-755, July.
  17. Qi, Shaozhou & Cheng, Shihan & Tan, Xiujie & Feng, Shenghao & Zhou, Qi, 2022. "Predicting China's carbon price based on a multi-scale integrated model," Applied Energy, Elsevier, vol. 324(C).
  18. Laura Böhm & Sebastian Kolb & Thomas Plankenbühler & Jonas Miederer & Simon Markthaler & Jürgen Karl, 2023. "Short-Term Natural Gas and Carbon Price Forecasting Using Artificial Neural Networks," Energies, MDPI, vol. 16(18), pages 1-25, September.
  19. Li, Houjian & Li, Qingman & Huang, Xinya & Guo, Lili, 2023. "Do green bonds and economic policy uncertainty matter for carbon price? New insights from a TVP-VAR framework," International Review of Financial Analysis, Elsevier, vol. 86(C).
  20. Yunhe Cheng & Beibei Hu, 2022. "Forecasting Regional Carbon Prices in China Based on Secondary Decomposition and a Hybrid Kernel-Based Extreme Learning Machine," Energies, MDPI, vol. 15(10), pages 1-18, May.
  21. Li, Guohui & Ning, Zhiyuan & Yang, Hong & Gao, Lipeng, 2022. "A new carbon price prediction model," Energy, Elsevier, vol. 239(PD).
  22. Jianguo Zhou & Dongfeng Chen, 2021. "Carbon Price Forecasting Based on Improved CEEMDAN and Extreme Learning Machine Optimized by Sparrow Search Algorithm," Sustainability, MDPI, vol. 13(9), pages 1-20, April.
  23. Wen, Fenghua & Zhao, Haocen & Zhao, Lili & Yin, Hua, 2022. "What drive carbon price dynamics in China?," International Review of Financial Analysis, Elsevier, vol. 79(C).
  24. Zhao, Lili & Wen, Fenghua & Wang, Xiong, 2020. "Interaction among China carbon emission trading markets: Nonlinear Granger causality and time-varying effect," Energy Economics, Elsevier, vol. 91(C).
  25. Wu, Siping & Xia, Guilin & Liu, Lang, 2023. "A novel decomposition integration model for power coal price forecasting," Resources Policy, Elsevier, vol. 80(C).
  26. Jianguo Zhou & Qiqi Wang, 2021. "Forecasting Carbon Price with Secondary Decomposition Algorithm and Optimized Extreme Learning Machine," Sustainability, MDPI, vol. 13(15), pages 1-17, July.
  27. Alameer, Zakaria & Fathalla, Ahmed & Li, Kenli & Ye, Haiwang & Jianhua, Zhang, 2020. "Multistep-ahead forecasting of coal prices using a hybrid deep learning model," Resources Policy, Elsevier, vol. 65(C).
  28. 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).
  29. Po Yun & Chen Zhang & Yaqi Wu & Yu Yang, 2022. "Forecasting Carbon Dioxide Price Using a Time-Varying High-Order Moment Hybrid Model of NAGARCHSK and Gated Recurrent Unit Network," IJERPH, MDPI, vol. 19(2), pages 1-19, January.
  30. Zhang, Lihong & Wang, Jun & Wang, Bin, 2020. "Energy market prediction with novel long short-term memory network: Case study of energy futures index volatility," Energy, Elsevier, vol. 211(C).
  31. Weng, Zhixiong & Liu, Tingting & Wu, Yufeng & Cheng, Cuiyun, 2022. "Air quality improvement effect and future contributions of carbon trading pilot programs in China," Energy Policy, Elsevier, vol. 170(C).
  32. Jianguo Zhou & Shiguo Wang, 2021. "A Carbon Price Prediction Model Based on the Secondary Decomposition Algorithm and Influencing Factors," Energies, MDPI, vol. 14(5), pages 1-20, March.
  33. Li, Zheng-Zheng & Su, Chi-Wei & Chang, Tsangyao & Lobonţ, Oana-Ramona, 2022. "Policy-driven or market-driven? Evidence from steam coal price bubbles in China," Resources Policy, Elsevier, vol. 78(C).
  34. Li, Jingmiao & Liu, Dehong, 2023. "Carbon price forecasting based on secondary decomposition and feature screening," Energy, Elsevier, vol. 278(PA).
  35. Ding, Lili & Zhao, Zhongchao & Han, Meng, 2021. "Probability density forecasts for steam coal prices in China: The role of high-frequency factors," Energy, Elsevier, vol. 220(C).
  36. Wen Zhang & Zhibin Wu, 2022. "Optimal hybrid framework for carbon price forecasting using time series analysis and least squares support vector machine," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 615-632, April.
  37. Zhang, Wen & Wu, Zhibin & Zeng, Xiaojun & Zhu, Changhui, 2023. "An ensemble dynamic self-learning model for multiscale carbon price forecasting," Energy, Elsevier, vol. 263(PC).
  38. Wang, Minggang & Zhu, Mengrui & Tian, Lixin, 2022. "A novel framework for carbon price forecasting with uncertainties," Energy Economics, Elsevier, vol. 112(C).
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