 Intrinsic decompositions in gold forecastingVasilios Plakandaras and Qiang Ji Journal of Commodity Markets, 2022, vol. 28, issue C 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. Keywords: Machine learning; Support vector regression; Ensemble empirical mode decomposition (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:jocoma:v:28:y:2022:i:c:s2405851322000034 DOI: 10.1016/j.jcomm.2022.100245 Access Statistics for this article Journal of Commodity Markets is currently edited by Marcel Prokopczuk, Betty Simkins and Sjur Westgaard More articles in Journal of Commodity Markets from Elsevier |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.