Carbon price analysis using empirical mode decomposition
AbstractMastering 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.
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Bibliographic InfoPaper provided by Department of Research, Ipag Business School in its series Working Papers with number 2014-156.
Length: 12 pages
Date of creation: 25 Feb 2014
Date of revision:
Carbon Price; Empirical Mode Decomposition; Multiscale Analysis; Forecasting; EU ETS;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2014-03-30 (All new papers)
- NEP-ENE-2014-03-30 (Energy Economics)
- NEP-ENV-2014-03-30 (Environmental Economics)
- NEP-FOR-2014-03-30 (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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Economics Papers from University Paris Dauphine
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