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Pricing and Forecasting Carbon Markets: Models and Empirical Analyses

Author

Listed:
  • Zhu Bangzhu
  • Julien Chevallier

    (LED - Laboratoire d'Economie Dionysien - UP8 - Université Paris 8 Vincennes-Saint-Denis)

Abstract

No abstract is available for this item.

Suggested Citation

  • Zhu Bangzhu & Julien Chevallier, 2017. "Pricing and Forecasting Carbon Markets: Models and Empirical Analyses," Post-Print hal-02879366, HAL.
    • Handle: RePEc:hal:journl:hal-02879366
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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.

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