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Intrinsic decompositions in gold forecasting

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  • Plakandaras, Vasilios
  • Ji, Qiang

Abstract

A quasi-stylized fact in the literature is that gold is a safe haven for investors when there is turbulence in the financial markets. Although investing in gold is not new, the relevant literature fails to reach a consensus regarding the forces that drive gold prices or a universally accepted forecasting model that can be used in the evaluation of asset allocation in gold portfolios. In this paper, we depart from the typical econometric approaches in the field and re-evaluate gold forecasting using a hybrid method. The proposed model is based on short- and long-run decomposition of input variables using the Ensemble Empirical Mode Decomposition algorithm and forecasting each component separately based on the Support Vector Regression method. Compared to previous methods in the field, our empirical findings suggest that the proposed method significantly increases the economic returns of a trading strategy based on the forecasts of the proposed scheme.

Suggested Citation

  • Plakandaras, Vasilios & Ji, Qiang, 2022. "Intrinsic decompositions in gold forecasting," Journal of Commodity Markets, Elsevier, vol. 28(C).
    • Handle: RePEc:eee:jocoma:v:28:y:2022:i:c:s2405851322000034
    DOI: 10.1016/j.jcomm.2022.100245
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    More about this item

    Keywords

    Machine learning; Support vector regression; Ensemble empirical mode decomposition;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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