Carbon price analysis using empirical mode decomposition

Mastering the underlying characteristics of carbon price changes can help governments formulate correct policies to keep efficient operation of carbon markets, and investors take effective measures to evade their investment risks. Empirical mode decomposition (EMD), a self-adaption data analysis approach for nonlinear and non-stationary time series, can accurately explain the formation mechanism of carbon price by decomposing it into several intrinsic mode functions (IMFs) and one residue from different scales. In this study, we apply EMD to the European Union Emissions Trading Scheme (EU ETS) carbon price analysis. First, the carbon price is decomposed into eight IMFs and one residue. Moreover, these IMFs and residue are reconstructed into a high frequency component, a low frequency component and a trend component using hierarchical clustering method. The economic meanings of these three components are identified as short term market fluctuations, effects of significant trend breaks, and a long-term trend, respectively. Finally, some strategies are proposed for carbon price forecasting.