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Forecasting metal prices with a curvelet based multiscale methodology

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  • He, Kaijian
  • Lu, Xingjing
  • Zou, Yingchao
  • Keung Lai, Kin

Abstract

Metal price movement is a complicated process with significant fluctuations, exhibiting nonlinear characteristics. With the proposed Heterogeneous Market Hypothesis (HMH), we argue that the macroscale nonlinear price movement consists of a diverse range of data components such as chaotic and multi-scale data characteristics at the microscopic level. In order to model the hidden data features, we propose a new Curvelet based forecasting algorithm. To model the dynamic chaotic data characteristics in the original time series, we project the original time series into the reconstructed phase space using the time delay method. The Curvelet denoising method is used for separating and reducing the disruption from noise in the transformed state space. Results from empirical studies conducted in the major metal markets suggest that the proposed model achieves statistically more robust and superior performance than traditional benchmark model.

Suggested Citation

  • He, Kaijian & Lu, Xingjing & Zou, Yingchao & Keung Lai, Kin, 2015. "Forecasting metal prices with a curvelet based multiscale methodology," Resources Policy, Elsevier, vol. 45(C), pages 144-150.
  • Handle: RePEc:eee:jrpoli:v:45:y:2015:i:c:p:144-150
    DOI: 10.1016/j.resourpol.2015.03.011
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    Cited by:

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    5. He, Kaijian & Chen, Yanhui & Tso, Geoffrey K.F., 2018. "Forecasting exchange rate using Variational Mode Decomposition and entropy theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 15-25.
    6. Du, Pei & Wang, Jianzhou & Yang, Wendong & Niu, Tong, 2020. "Point and interval forecasting for metal prices based on variational mode decomposition and an optimized outlier-robust extreme learning machine," Resources Policy, Elsevier, vol. 69(C).
    7. Lin, Yu & Liao, Qidong & Lin, Zixiao & Tan, Bin & Yu, Yuanyuan, 2022. "A novel hybrid model integrating modified ensemble empirical mode decomposition and LSTM neural network for multi-step precious metal prices prediction," Resources Policy, Elsevier, vol. 78(C).
    8. He, Kaijian & Chen, Yanhui & Tso, Geoffrey K.F., 2017. "Price forecasting in the precious metal market: A multivariate EMD denoising approach," Resources Policy, Elsevier, vol. 54(C), pages 9-24.
    9. Pincheira Brown, Pablo & Hardy, Nicolás, 2019. "Forecasting base metal prices with the Chilean exchange rate," Resources Policy, Elsevier, vol. 62(C), pages 256-281.
    10. Yifei Zhao & Jianhong Chen & Hideki Shimada & Takashi Sasaoka, 2023. "Non-Ferrous Metal Price Point and Interval Prediction Based on Variational Mode Decomposition and Optimized LSTM Network," Mathematics, MDPI, vol. 11(12), pages 1-16, June.
    11. Tapia Cortez, Carlos A. & Hitch, Michael & Sammut, Claude & Coulton, Jeff & Shishko, Robert & Saydam, Serkan, 2018. "Determining the embedding parameters governing long-term dynamics of copper prices," Chaos, Solitons & Fractals, Elsevier, vol. 111(C), pages 186-197.
    12. Rubaszek, Michał & Karolak, Zuzanna & Kwas, Marek, 2020. "Mean-reversion, non-linearities and the dynamics of industrial metal prices. A forecasting perspective," Resources Policy, Elsevier, vol. 65(C).
    13. C. A. Tapia Cortez & J. Coulton & C. Sammut & S. Saydam, 2018. "Determining the chaotic behaviour of copper prices in the long-term using annual price data," Palgrave Communications, Palgrave Macmillan, vol. 4(1), pages 1-13, December.
    14. Pincheira, Pablo & Hardy, Nicolas, 2018. "Forecasting Base Metal Prices with Commodity Currencies," MPRA Paper 83564, University Library of Munich, Germany.
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    16. Chen, Yanhui & He, Kaijian & Zhang, Chuan, 2016. "A novel grey wave forecasting method for predicting metal prices," Resources Policy, Elsevier, vol. 49(C), pages 323-331.

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    More about this item

    Keywords

    Curvelet analysis; Metal price forecast; Time delay embedding; ARMA model;
    All these keywords.

    JEL classification:

    • Q32 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Nonrenewable Resources and Conservation - - - Exhaustible Resources and Economic Development
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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