Carbon Price Multiscale Decomposition Using an Improved Hilbert-Huang Transform Algorithm

As a novel adaptive multiscale processing algorithm for non-linear and non-stationary data, Hilbert-Huang Transform (HHT) has been widely used in the fields of natural sciences and engineering, and has been applied in the field of humanities and social sciences in recent years. Empirical mode decomposition(EMD)is the core of HHT algorithm. In the processing of EMD, there may exist the phenomena of mode-mixing and end effect, which can lead to the decomposition results distortion. Aiming at carbon price multiscale decomposition, the HHT algorithm was improved to enhance the quality of EMD decomposition. Firstly, the ensemble EMD(EEMD)algorithm was built by introducing the Gaussian white noises into the EMD decomposition, which was applied to solve the mode-mixing phenomenon during EMD. Next, aiming at the end effect phenomenon, the extension method which was appropriate for dealing with carbon price was obtained by comparing five different kinds of extension methods for EEMD. Finally, taking two carbon future prices with different maturities called DEC12 and DEC14 under the European Union Emissions Trading System(EU ETS)as samples, empirical results showed that the improved HHT algorithm could effectively improve decomposition accuracy, and the application of HHT was extended.