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Carbon price analysis using empirical mode decomposition

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Author Info

  • Bangzhu Zhu
  • Ping Wang
  • Julien Chevallier
  • Yiming Wei

Abstract

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.

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Bibliographic Info

Paper provided by Department of Research, Ipag Business School in its series Working Papers with number 2014-156.

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Length: 12 pages
Date of creation: 25 Feb 2014
Date of revision:
Handle: RePEc:ipg:wpaper:2014-156

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Related research

Keywords: Carbon Price; Empirical Mode Decomposition; Multiscale Analysis; Forecasting; EU ETS;

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  1. Moghtaderi, Azadeh & Flandrin, Patrick & Borgnat, Pierre, 2013. "Trend filtering via empirical mode decompositions," Computational Statistics & Data Analysis, Elsevier, vol. 58(C), pages 114-126.
  2. Chevallier, Julien & Ielpo, Florian & Mercier, Ludovic, 2009. "Risk aversion and institutional information disclosure on the European carbon market : a case-study of the 2006 compliance event," Economics Papers from University Paris Dauphine 123456789/4221, Paris Dauphine University.
  3. Montagnoli, Alberto & de Vries, Frans P., 2010. "Carbon trading thickness and market efficiency," Energy Economics, Elsevier, vol. 32(6), pages 1331-1336, November.
  4. Crossland, Jarrod & Li, Bin & Roca, Eduardo, 2013. "Is the European Union Emissions Trading Scheme (EU ETS) informationally efficient? Evidence from momentum-based trading strategies," Applied Energy, Elsevier, vol. 109(C), pages 10-23.
  5. Seifert, Jan & Uhrig-Homburg, Marliese & Wagner, Michael, 2008. "Dynamic behavior of CO2 spot prices," Journal of Environmental Economics and Management, Elsevier, vol. 56(2), pages 180-194, September.
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