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Forecasting the European Carbon Market

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  • Koop, Gary
  • Tole, Lise

Abstract

In an effort to meet its obligations under the Kyoto Protocol, in 2005 the European Union introduced a cap-and-trade scheme where mandated installations are allocated permits to emit CO2. Financial markets have developed that allow companies to trade these carbon permits. For the EU to achieve reductions in CO2 emissions at a minimum cost, it is necessary that companies make appropriate investments and policymakers design optimal policies. In an effort to clarify the workings of the carbon market, several recent papers have attempted to statistically model it. However, the European carbon market (EU ETS) has many institutional features that potentially impact on daily carbon prices (and associated nancial futures). As a consequence, the carbon market has properties that are quite different from conventional financial assets traded in mature markets. In this paper, we use dynamic model averaging (DMA) in order to forecast in this newly-developing market. DMA is a recently-developed statistical method which has three advantages over conventional approaches. First, it allows the coefficients on the predictors in a forecasting model to change over time. Second, it allows for the entire fore- casting model to change over time. Third, it surmounts statistical problems which arise from the large number of potential predictors that can explain carbon prices. Our empirical results indicate that there are both important policy and statistical bene ts with our approach. Statistically, we present strong evidence that there is substantial turbulence and change in the EU ETS market, and that DMA can model these features and forecast accurately compared to conventional approaches. From a policy perspective, we discuss the relative and changing role of different price drivers in the EU ETS. Finally, we document the forecast performance of DMA and discuss how this relates to the efficiency and maturity of this market.

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File URL: http://repo.sire.ac.uk/handle/10943/261
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Bibliographic Info

Paper provided by Scottish Institute for Research in Economics (SIRE) in its series SIRE Discussion Papers with number 2011-20.

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Date of creation: 2011
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Handle: RePEc:edn:sirdps:261

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Keywords: Bayesian; carbon permit trading; fi nancial markets; state space model; model averaging;

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References

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Citations

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Cited by:
  1. Miguel Belmonte & Gary Koop, 2013. "Model Switching and Model Averaging in Time-Varying Parameter Regression Models," Working Papers 1302, University of Strathclyde Business School, Department of Economics.
  2. Giulio Cainelli & Massimiliano Mazzanti & Simone Borghesi, 2012. "The European Emission Trading Scheme and environmental innovation diffusion: Empirical analyses using Italian CIS data," Working Papers 201201, University of Ferrara, Department of Economics.
  3. Cainelli, Giulio & Mazzanti, Massimiliano, 2013. "Environmental innovations in services: Manufacturing–services integration and policy transmissions," Research Policy, Elsevier, vol. 42(9), pages 1595-1604.
  4. Koop, Gary & Tole, Lise, 2013. "Modeling the relationship between European carbon permits and certified emission reductions," Journal of Empirical Finance, Elsevier, vol. 24(C), pages 166-181.
  5. Simone Borghesi & Giulio Cainelli & Massimiliano Mazzanti, 2012. "Brown Sunsets and Green Dawns in the Industrial Sector: Environmental Innovations, Firm Behavior and the European Emission Trading," Working Papers 2012.03, Fondazione Eni Enrico Mattei.

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