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Nonparametric modeling of carbon prices

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  • Chevallier, Julien

Abstract

This paper constitutes the first exercise of nonparametric modeling applied to carbon markets. The framework of analysis is carefully detailed, and the empirical application unfolds in the case of BlueNext spot and ECX futures prices. The data is gathered in daily frequency from April 2005 to April 2010. First, we document the presence of strong nonlinearities in the conditional mean functions. Second, the conditional volatility functions reveal an asymmetric and heteroskedastic behavior which is dramatically different between carbon spot and futures logreturns. The results for spot prices are also robust to subsamples' decomposition. Third, we show in an out-of-sample forecasting exercise that nonparametric modeling allows reducing the prediction error by almost 15% compared to linear AR models. This latter result is confirmed by the Diebold–Mariano pairwise test statistic.

Suggested Citation

  • Chevallier, Julien, 2011. "Nonparametric modeling of carbon prices," Energy Economics, Elsevier, vol. 33(6), pages 1267-1282.
  • Handle: RePEc:eee:eneeco:v:33:y:2011:i:6:p:1267-1282
    DOI: 10.1016/j.eneco.2011.03.003
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    References listed on IDEAS

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    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. 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.
    3. Tol, Richard S.J., 2012. "A cost–benefit analysis of the EU 20/20/2020 package," Energy Policy, Elsevier, vol. 49(C), pages 288-295.
    4. Zachmann, Georg, 2013. "A stochastic fuel switching model for electricity prices," Energy Economics, Elsevier, vol. 35(C), pages 5-13.
    5. Zhu, Bangzhu & Han, Dong & Wang, Ping & Wu, Zhanchi & Zhang, Tao & Wei, Yi-Ming, 2017. "Forecasting carbon price using empirical mode decomposition and evolutionary least squares support vector regression," Applied Energy, Elsevier, vol. 191(C), pages 521-530.

    More about this item

    Keywords

    Carbon prices; Nonparametric modeling; Conditional volatility modeling; Out-of-sample forecasting;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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