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Gold price analysis based on ensemble empirical model decomposition and independent component analysis

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  • Xian, Lu
  • He, Kaijian
  • Lai, Kin Keung

Abstract

In recent years, the increasing level of volatility of the gold price has received the increasing level of attention from the academia and industry alike. Due to the complexity and significant fluctuations observed in the gold market, however, most of current approaches have failed to produce robust and consistent modeling and forecasting results. Ensemble Empirical Model Decomposition (EEMD) and Independent Component Analysis (ICA) are novel data analysis methods that can deal with nonlinear and non-stationary time series. This study introduces a new methodology which combines the two methods and applies it to gold price analysis. This includes three steps: firstly, the original gold price series is decomposed into several Intrinsic Mode Functions (IMFs) by EEMD. Secondly, IMFs are further processed with unimportant ones re-grouped. Then a new set of data called Virtual Intrinsic Mode Functions (VIMFs) is reconstructed. Finally, ICA is used to decompose VIMFs into statistically Independent Components (ICs). The decomposition results reveal that the gold price series can be represented by the linear combination of ICs. Furthermore, the economic meanings of ICs are analyzed and discussed in detail, according to the change trend and ICs’ transformation coefficients. The analyses not only explain the inner driving factors and their impacts but also conduct in-depth analysis on how these factors affect gold price. At the same time, regression analysis has been conducted to verify our analysis. Results from the empirical studies in the gold markets show that the EEMD–ICA serve as an effective technique for gold price analysis from a new perspective.

Suggested Citation

  • Xian, Lu & He, Kaijian & Lai, Kin Keung, 2016. "Gold price analysis based on ensemble empirical model decomposition and independent component analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 454(C), pages 11-23.
    • Handle: RePEc:eee:phsmap:v:454:y:2016:i:c:p:11-23
    DOI: 10.1016/j.physa.2016.02.055
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