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本文基于2005年4月～2011年9月欧洲气候交易所碳期货价格数据,运用经验模态分解(EMD)模型,将碳价序列从高频到低频分解成若个独立的、不同尺度的内在模态函数(IMF)和一个残差项,并赋予它们相应的物理含义;应用fine-to-coarse reconstruction算法将分解得到的IMF和残差项重构成高频分量、低频分量和趋势分量。这三个分量依次被辨识为短期供需失衡和市场随机活动影响、中期重大事件影响以及长期趋势。最后,针对提高国际碳市场价格预测的准确性,作者提出了一系列建议。
Based on the data of the European Climate Exchange’s carbon futures prices from April 2005 to September 2011, the empirical mode decomposition (EMD) model is used to decompose carbon price series from high frequency to low frequency into independent and different scales of internal Modal function (IMF) and a residual term, and give them the corresponding physical meanings. The IMFs and residuals resulting from the decomposition are reconstructed into high-frequency components, low-frequency components and trending components using a fine-to-coarse reconstruction algorithm. These three components were identified in turn as short-term supply and demand imbalances and market random effects, medium-term major events and long-term trends. Finally, in order to improve the accuracy of the international carbon market price forecast, the author put forward a series of suggestions.