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Pricing and Forecasting Carbon Markets

Author

Listed:
  • Bangzhu Zhu

    (Jinan University School of Management)

  • Julien Chevallier

    (University Paris 8 (LED) UFR AES Economie Gestion)

Abstract

No abstract is available for this item.

Suggested Citation

  • Bangzhu Zhu & Julien Chevallier, 2017. "Pricing and Forecasting Carbon Markets," Springer Books, Springer, number 978-3-319-57618-3, September.
    • Handle: RePEc:spr:sprbok:978-3-319-57618-3
    DOI: 10.1007/978-3-319-57618-3
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    Citations

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    Cited by:

    1. Karpf, Andreas & Mandel, Antoine & Battiston, Stefano, 2018. "Price and network dynamics in the European carbon market," Journal of Economic Behavior & Organization, Elsevier, vol. 153(C), pages 103-122.
    2. Jian Liu & Ziting Zhang & Lizhao Yan & Fenghua Wen, 2021. "Forecasting the volatility of EUA futures with economic policy uncertainty using the GARCH-MIDAS model," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-19, December.
    3. Zheng, Yan & Yin, Hua & Zhou, Min & Liu, Wenhua & Wen, Fenghua, 2021. "Impacts of oil shocks on the EU carbon emissions allowances under different market conditions," Energy Economics, Elsevier, vol. 104(C).
    4. Bangzhu Zhu & Shunxin Ye & Ping Wang & Julien Chevallier & Yi‐Ming Wei, 2022. "Forecasting carbon price using a multi‐objective least squares support vector machine with mixture kernels," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(1), pages 100-117, January.
    5. Liudmila Reshetnikova & Natalia Boldyreva & Anton Devyatkov & Zhanna Pisarenko & Danila Ovechkin, 2023. "Carbon Pricing in Current Global Institutional Changes," Sustainability, MDPI, vol. 15(4), pages 1-19, February.
    6. Ren, Xiaohang & Duan, Kun & Tao, Lizhu & Shi, Yukun & Yan, Cheng, 2022. "Carbon prices forecasting in quantiles," Energy Economics, Elsevier, vol. 108(C).
    7. Emmanuel Senyo Fianu, 2022. "Analyzing and Forecasting Multi-Commodity Prices Using Variants of Mode Decomposition-Based Extreme Learning Machine Hybridization Approach," Forecasting, MDPI, vol. 4(2), pages 1-27, June.
    8. Weijia Shao & Lukas Friedemann Radke & Fikret Sivrikaya & Sahin Albayrak, 2021. "Adaptive Online Learning for the Autoregressive Integrated Moving Average Models," Mathematics, MDPI, vol. 9(13), pages 1-30, June.
    9. Hyeonho Kim & Yujin Kim & Yongho Ko & Seungwoo Han, 2022. "Performance Comparison of Predictive Methodologies for Carbon Emission Credit Price in the Korea Emission Trading System," Sustainability, MDPI, vol. 14(13), pages 1-20, July.
    10. Madeline Hui Li Lee & Yee Chee Ser & Ganeshsree Selvachandran & Pham Huy Thong & Le Cuong & Le Hoang Son & Nguyen Trung Tuan & Vassilis C. Gerogiannis, 2022. "A Comparative Study of Forecasting Electricity Consumption Using Machine Learning Models," Mathematics, MDPI, vol. 10(8), pages 1-23, April.

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