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Volatility forecasting of carbon prices using factor models

Author

Listed:
  • Julien Chevallier

    (Université Paris Dauphine)

Abstract

This article develops a forecasting exercise of the volatility of EUA spot, EUA futures, and CER futures carbon prices (modeled after an AR(1)-GARCH(1,1)) using two dynamic factors as exogenous regressors that were extracted from a Factor Augmented VAR model (Bernanke et al. (2005)). The dataset includes 115 macroeconomic, financial and commodities indicators with daily frequency from April 4, 2008 through January 25, 2010 totalling 463 observations that capture the strong uncertainties emerging on the carbon market. The main result shows that the best forecasting performance for the volatility of carbon prices is achieved for the model including the dynamic factors as exogenous regressors, which can be useful to inform hedging or speculative trading strategies by energy utilities, financial market players and risk managers.

Suggested Citation

  • Julien Chevallier, 2010. "Volatility forecasting of carbon prices using factor models," Economics Bulletin, AccessEcon, vol. 30(2), pages 1642-1660.
    • Handle: RePEc:ebl:ecbull:eb-10-00104
    as

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    File URL: http://www.accessecon.com/Pubs/EB/2010/Volume30/EB-10-V30-I2-P151.pdf
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    References listed on IDEAS

    as
    1. Chevallier, Julien, 2009. "Carbon futures and macroeconomic risk factors: A view from the EU ETS," Energy Economics, Elsevier, vol. 31(4), pages 614-625, July.
    2. Chevallier, Julien, 2010. "Modelling risk premia in CO2 allowances spot and futures prices," Economic Modelling, Elsevier, vol. 27(3), pages 717-729, May.
    3. repec:dau:papers:123456789/4210 is not listed on IDEAS
    4. Daskalakis, George & Psychoyios, Dimitris & Markellos, Raphael N., 2009. "Modeling CO2 emission allowance prices and derivatives: Evidence from the European trading scheme," Journal of Banking & Finance, Elsevier, vol. 33(7), pages 1230-1241, July.
    5. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
    6. repec:dau:papers:123456789/4227 is not listed on IDEAS
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Zhu, Bangzhu & Ma, Shujiao & Chevallier, Julien & Wei, Yiming, 2014. "Modelling the dynamics of European carbon futures price: A Zipf analysis," Economic Modelling, Elsevier, vol. 38(C), pages 372-380.
    2. Quande Qin & Huangda He & Li Li & Ling-Yun He, 2020. "A Novel Decomposition-Ensemble Based Carbon Price Forecasting Model Integrated with Local Polynomial Prediction," Computational Economics, Springer;Society for Computational Economics, vol. 55(4), pages 1249-1273, April.
    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. 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.
    5. 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).
    6. Yumin Li & Ruiqi Yang & Xiaoman Wang & Jiaming Zhu & Nan Song, 2023. "Carbon Price Combination Forecasting Model Based on Lasso Regression and Optimal Integration," Sustainability, MDPI, vol. 15(12), pages 1-26, June.
    7. Jianguo Zhou & Xuejing Huo & Xiaolei Xu & Yushuo Li, 2019. "Forecasting the Carbon Price Using Extreme-Point Symmetric Mode Decomposition and Extreme Learning Machine Optimized by the Grey Wolf Optimizer Algorithm," Energies, MDPI, vol. 12(5), pages 1-22, March.
    8. 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.

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    More about this item

    Keywords

    Volatility Forecasting; Carbon price; Factor models;
    All these keywords.

    JEL classification:

    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy

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