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Hedging with CO2 allowances: the ECX market


  • Carlos Pinho

    () (Departamento de Economia e Gestão Industrial, Universidade de Aveiro, GOVCOPP)

  • Mara Madaleno

    () (Departamento de Economia e Gestão Industrial, Universidade de Aveiro, GOVCOPP)


We investigate and empirically estimate optimal hedge ratios, for the first time, in the EU ETS carbon market. Minimum variance hedge ratios are conditionally estimated with multivariate GARCH models, and unconditionally by OLS and the naïve strategy for the European Climate Exchange (ECX) market in the period 2005-2009. Also, utility gains are considered in order to take into account risk-return considerations. Empirical results indicate that dynamic hedging provides superior gains (in reducing the variance portfolio) compared to those obtained from static hedging, when adjustment costs are not taken into account. Moreover, results improve when the leptokurtic characteristics of the data are into consideration through distributions. Results are always compared in and out of sample, suggesting also that utility gains increase with investor's increased preference over risk.

Suggested Citation

  • Carlos Pinho & Mara Madaleno, 2010. "Hedging with CO2 allowances: the ECX market," Working Papers de Economia (Economics Working Papers) 55, Departamento de Economia, Gestão e Engenharia Industrial, Universidade de Aveiro.
  • Handle: RePEc:ave:wpaper:552010

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    References listed on IDEAS

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    Cited by:

    1. Balcılar, Mehmet & Demirer, Rıza & Hammoudeh, Shawkat & Nguyen, Duc Khuong, 2016. "Risk spillovers across the energy and carbon markets and hedging strategies for carbon risk," Energy Economics, Elsevier, vol. 54(C), pages 159-172.
    2. repec:ipg:wpaper:2014-552 is not listed on IDEAS
    3. Tan, Xue-Ping & Wang, Xin-Yu, 2017. "Dependence changes between the carbon price and its fundamentals: A quantile regression approach," Applied Energy, Elsevier, vol. 190(C), pages 306-325.

    More about this item


    CO2 Emission Allowances; Dynamic Hedging; Futures Prices; Risk Management; Spot Prices;

    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
    • G19 - Financial Economics - - General Financial Markets - - - Other
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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