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Understanding volatility dynamics in the EU-ETS market

Author

Listed:
  • Maria Eugenia Sanin

    () (Université d'Evry Val d'Essonne)

  • Maria Mansanet-Bataller

    () (Université Franche-Comté)

  • Francesco Violante

    () (Aarhus University and CREATES)

Abstract

We study the short-term price behavior of Phase 2 EU emission allowances. We model returns and volatility dynamics, and we demonstrate that a standard ARMAX-GARCH framework is inadequate for this modeling and that the gaussianity assumption is rejected due to a number of outliers. To improve the fitness of the model, we combine the underlying price process with an additive stochastic jump process. We improve the model's performance by introducing a time-varying jump probability that is explained by two variables: the daily relative change in the volume of transactions and the European Commission's announcements regarding the supply of permits. We show that (i) sharp increases in volume have led to increased volatility during the April 2005{December 2007period but not for the period beginning in January 2008, and (ii) announcements induce jumps in the process that tend to increase volatility across both periods. Thus, authorities face a trade off between disseminating information effectively and promoting market stability.

Suggested Citation

  • Maria Eugenia Sanin & Maria Mansanet-Bataller & Francesco Violante, 2015. "Understanding volatility dynamics in the EU-ETS market," CREATES Research Papers 2015-04, Department of Economics and Business Economics, Aarhus University.
  • Handle: RePEc:aah:create:2015-04
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    References listed on IDEAS

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    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.
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    10. Reckling, Dennis, 2016. "Variance risk premia in CO2 markets: A political perspective," Energy Policy, Elsevier, vol. 94(C), pages 345-354.
    11. Balietti, Anca Claudia, 2016. "Trader types and volatility of emission allowance prices. Evidence from EU ETS Phase I," Energy Policy, Elsevier, vol. 98(C), pages 607-620.
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    14. Alexander C. M. Zeitlberger & Alexander Brauneis, 2016. "Modeling carbon spot and futures price returns with GARCH and Markov switching GARCH models," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 24(1), pages 149-176, March.
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    17. Cristina Sattarhoff & Marc Gronwald, 2018. "How to Measure Financial Market Efficiency? A Multifractality-Based Quantitative Approach with an Application to the European Carbon Market," CESifo Working Paper Series 7102, CESifo Group Munich.
    18. Zhao, Xin-gang & Jiang, Gui-wu & Nie, Dan & Chen, Hao, 2016. "How to improve the market efficiency of carbon trading: A perspective of China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 1229-1245.
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    20. Friedrich, Marina & Mauer, Eva-Maria & Pahle, Michael & Tietjen, Oliver, 2019. "From fundamentals to financial assets: the evolution of understanding price formation in the EU ETS," EconStor Preprints 196150, ZBW - Leibniz Information Centre for Economics.

    More about this item

    Keywords

    EUA market; EU ETS; carbon emission trading; Garch model; normal mixture;

    JEL classification:

    • C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Econometric and Statistical Methods; Specific Distributions
    • 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
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q52 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Pollution Control Adoption and Costs; Distributional Effects; Employment Effects
    • Q53 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Air Pollution; Water Pollution; Noise; Hazardous Waste; Solid Waste; Recycling

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