To meet the Kyoto Protocol 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 financial futures). As a consequence, the carbon market has properties that are quite different from conventional financial assets traded in mature markets.
UK Researchers Gary Koop and Lise Tole have used dynamic model averaging (DMA) in order to forecast in this newly-developing market. DMA is a recently-developed statistical method which has 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 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.
Empirical results indicate that there are both important policy and statistical benefits with the new approach. Strong statistical evidence is present 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.