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Understanding volatility dynamics in the EU-ETS market: lessons from the future

Author

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  • SANIN, Maria Eugenia
  • VIOLANTE, Francesco

Abstract

In this paper we study the short term price behavior of December 2008 future prices for EU emission allowances. We model returns and volatility dynamics of this price showing that a standard ARMA-GARCH framework is not adequate and that the gaussianity assumption is rejected due to the occurrence of a number of level and volatility outliers. To improve the fitness of the model, we combine the underlying price process with an additive stochastic jump process. The resulting distribution, a mixture of Gaussians, allows for endogenously determined jumps in the process governing the returns, while the mixing law determines the jump probability. The performance of the model is improved by introducing a time varying jump probability explained by two variables. The first one is the daily relative change in the volume of transactions and suggests that sharp increases in volume increase volatility even in the absence of changes in what recent literature considers as market fundamentals. The second one accounts for changes in the jump probability associated to the European Commission's announcements regarding the NAPs for Phase II. We find that announcements concerning the NAPs induce jumps in the process and tend to increase volatility. This result suggests authorities should advocate to increase stability in the regulatory environment which is crucial to allow traders to realize efficient trading strategies and informed investment decisions regarding pollution reduction.

Suggested Citation

  • SANIN, Maria Eugenia & VIOLANTE, Francesco, 2009. "Understanding volatility dynamics in the EU-ETS market: lessons from the future," LIDAM Discussion Papers CORE 2009024, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  • Handle: RePEc:cor:louvco:2009024
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    File URL: https://sites.uclouvain.be/core/publications/coredp/coredp2009.html
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    Citations

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    Cited by:

    1. Sklavos, Konstantinos & Dam, Lammertjan & Scholtens, Bert, 2013. "The liquidity of energy stocks," Energy Economics, Elsevier, vol. 38(C), pages 168-175.
    2. Marc Gronwald & Janina Ketterer, 2009. "Evaluating emissions trading as a policy instrument," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 62(11), pages 22-25, June.
    3. Marc Gronwald & Janina Ketterer, 2009. "Evaluating Emission Trading as a Policy Tool - Evidence from Conditional Jump Models," CESifo Working Paper Series 2682, CESifo.
    4. Boersen, Arieke & Scholtens, Bert, 2014. "The relationship between European electricity markets and emission allowance futures prices in phase II of the EU (European Union) emission trading scheme," Energy, Elsevier, vol. 74(C), pages 585-594.
    5. Alexander 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.
    6. Marc Gronwald & Janina Ketterer & Stefan Trück, 2011. "The Dependence Structure between Carbon Emission Allowances and Financial Markets - A Copula Analysis," CESifo Working Paper Series 3418, CESifo.

    More about this item

    Keywords

    EUA market; EU-ETS; carbon emission trading; Garch model; normal mixture;
    All these keywords.

    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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