Forecasting the European Carbon Market
AbstractIn 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 modelaveraging (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 coeÃƒâ€šÃ‚Â¢ cients on the predictors in a forecasting model to change over time. Second, it allows for the entire forecasting 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 benefits 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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Bibliographic InfoPaper provided by University of Strathclyde Business School, Department of Economics in its series Working Papers with number 1110.
Length: 40 pages
Date of creation: Apr 2011
Date of revision:
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Bayesian; carbon permit trading; financial markets; state space model; model averaging;
Other versions of this item:
- G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models
This paper has been announced in the following NEP Reports:
- NEP-ALL-2011-06-11 (All new papers)
- NEP-ENE-2011-06-11 (Energy Economics)
- NEP-ENV-2011-06-11 (Environmental Economics)
- NEP-EUR-2011-06-11 (Microeconomic European Issues)
- NEP-FOR-2011-06-11 (Forecasting)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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