Dmitriy O. Afanasyev, Elena A. Fedorova, 2016
Energy Economics 56, 432-442
Article on ScienceDirect
Preprint available for download on Munich Personal RePEc Archive
This paper proposes an improved approach to electricity prices trend-cyclical component filtering, which is based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). A combined criterion for determining the modes to be included into the trend component is introduced. The performance of the proposed approach is compared with the ordinary empirical mode decomposition (EMD), as well as with the method of wavelet-decomposition well-known in the energy economics literature. We test it on four day-ahead electricity markets: the Europe-Ural and the Siberia price zones of the Russian ATS exchange, the PJM exchange of the USA and the APX exchange of the United Kingdom. Our results show that the proposed approach based on CEEMDAN and the combined criterion outperforms the standard EMD on all the four electricity markets, and on two of the studied markets (PJM, APX) it outperforms the wavelet-smoothing, while on the other two (ATS Europe-Ural and Siberia) it performs at least not worse than the wavelet-smoothing. At the same time, the proposed approach does not require a prior choice of the smoothing parameter, as in the case of the wavelet-decomposition, and demonstrates a certain degree of versatility on the studied markets.
electricity market; trend-filtering; long-term seasonal component; empirical mode decomposition; wavelet-decomposition
The authors are grateful to Evgenii V. Gilenko (PhD, St. Petersburg State University, Russia) and two anonymous reviewers for their fruitful and stimulating comments. This paper has also greatly benefited from conversations with the participants of the II International Conference “Modern Econometric Tools and Applications – EC2015” (Nizhny Novgorod, Russia) where the revised version of paper was presented. Please read the disclaimer.
Source code used for article is available in the program library of adaptive data analysis ADAnalysis for Matlab™.