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The prediction for London gold price: improved empirical mode decomposition

Author

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  • Qiuling Hua
  • Tingfeng Jiang

Abstract

Gold has a strong anti-risk ability, and the price trend attracts much attention for most investors all over the world. This letter investigates and predicts the price of London gold by using the improved empirical mode decomposition (EMD) method. The prediction results after decomposition approximate well to the real ones and suggest more accurate gold price trend that can help investors in choosing better strategies in the big data era. Moreover, the forecasting methods and techniques used in the study provide inspiring new thoughts for high-frequency data analysis in theory.

Suggested Citation

  • Qiuling Hua & Tingfeng Jiang, 2015. "The prediction for London gold price: improved empirical mode decomposition," Applied Economics Letters, Taylor & Francis Journals, vol. 22(17), pages 1404-1408, November.
    • Handle: RePEc:taf:apeclt:v:22:y:2015:i:17:p:1404-1408
    DOI: 10.1080/13504851.2015.1034835
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    Cited by:

    1. Ya-Wen Lai, 2017. "Output gaps and the New Keynesian Phillips curve: An application of the Empirical Mode Decomposition," Economics Bulletin, AccessEcon, vol. 37(2), pages 952-961.
    2. Lin, Ling & Jiang, Yong & Xiao, Helu & Zhou, Zhongbao, 2020. "Crude oil price forecasting based on a novel hybrid long memory GARCH-M and wavelet analysis model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 543(C).
    3. Zhu, Yongguang & Xu, Deyi & Cheng, Jinhua & Ali, Saleem Hassan, 2018. "Estimating the impact of China's export policy on tin prices: a mode decomposition counterfactual analysis method," Resources Policy, Elsevier, vol. 59(C), pages 250-264.

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