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Understanding volatility dynamics in the EU-ETS market

Author

Listed:
  • Maria Eugenia Sanin

    (EPEE - Centre d'Etudes des Politiques Economiques - UEVE - Université d'Évry-Val-d'Essonne)

  • Francesco Violante

    (UNIBO - Alma Mater Studiorum Università di Bologna = University of Bologna)

  • Maria Mansanet-Bataller

    (Mission Climat Caisse des Dépôts - UP1 - Université Paris 1 Panthéon-Sorbonne)

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 2007 period 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. (c) 2015 Elsevier Ltd. All rights reserved.

Suggested Citation

  • Maria Eugenia Sanin & Francesco Violante & Maria Mansanet-Bataller, 2015. "Understanding volatility dynamics in the EU-ETS market," Post-Print hal-02878047, HAL.
    • Handle: RePEc:hal:journl:hal-02878047
    DOI: 10.1016/j.enpol.2015.02.024
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    References listed on IDEAS

    as
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    More about this item

    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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