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A novel grey wave forecasting method for predicting metal prices

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  • Chen, Yanhui
  • He, Kaijian
  • Zhang, Chuan

Abstract

The evolution of metal prices shows severe fluctuations and irregular cycles which bring difficulties to accurate forecasting. This paper proposes a novel grey wave forecasting method with unequal-interval contour lines and contour time sequences filtrating to predict metal prices. In the proposed model unequal-interval contour lines are determined by the quantiles of data, which considers the intensity of data. Contour time sequences are filtrated based on autocorrelation characteristics of time series. Furthermore, we use monthly prices of two metals - aluminum and nickel-to assess the performance of our novel grey wave forecasting model with a multi-step-ahead prediction. The empirical analysis indicates the modified grey wave forecasting method is much better than basic grey wave forecasting method in terms of prediction accuracy and it can also achieve better forecasting results than ARMA and random walk.

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  • 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.
    • Handle: RePEc:eee:jrpoli:v:49:y:2016:i:c:p:323-331
    DOI: 10.1016/j.resourpol.2016.06.012
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    1. Dooley, Gillian & Lenihan, Helena, 2005. "An assessment of time series methods in metal price forecasting," Resources Policy, Elsevier, vol. 30(3), pages 208-217, September.
    2. Sánchez Lasheras, Fernando & de Cos Juez, Francisco Javier & Suárez Sánchez, Ana & Krzemień, Alicja & Riesgo Fernández, Pedro, 2015. "Forecasting the COMEX copper spot price by means of neural networks and ARIMA models," Resources Policy, Elsevier, vol. 45(C), pages 37-43.
    3. Labys, W C & Lesourd, J B & Badillo, D, 1998. "The existence of metal price cycles," Resources Policy, Elsevier, vol. 24(3), pages 147-155, September.
    4. Olson, David L. & Wu, Desheng, 2006. "Simulation of fuzzy multiattribute models for grey relationships," European Journal of Operational Research, Elsevier, vol. 175(1), pages 111-120, November.
    5. Leduc, Sylvain & Sill, Keith, 2004. "A quantitative analysis of oil-price shocks, systematic monetary policy, and economic downturns," Journal of Monetary Economics, Elsevier, vol. 51(4), pages 781-808, May.
    6. 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.
    7. Bai, Chunguang & Sarkis, Joseph, 2010. "Integrating sustainability into supplier selection with grey system and rough set methodologies," International Journal of Production Economics, Elsevier, vol. 124(1), pages 252-264, March.
    8. Arouri, Mohamed El Hedi & Hammoudeh, Shawkat & Lahiani, Amine & Nguyen, Duc Khuong, 2012. "Long memory and structural breaks in modeling the return and volatility dynamics of precious metals," The Quarterly Review of Economics and Finance, Elsevier, vol. 52(2), pages 207-218.
    9. Li, Der-Chiang & Chang, Che-Jung & Chen, Chien-Chih & Chen, Wen-Chih, 2012. "Forecasting short-term electricity consumption using the adaptive grey-based approach—An Asian case," Omega, Elsevier, vol. 40(6), pages 767-773.
    10. Xiong, Tao & Bao, Yukun & Hu, Zhongyi, 2013. "Beyond one-step-ahead forecasting: Evaluation of alternative multi-step-ahead forecasting models for crude oil prices," Energy Economics, Elsevier, vol. 40(C), pages 405-415.
    11. Erwin Bulte & Joost Pennings & Wim Heijman, 1996. "Futures markets, price stabilization and efficient exploitation of exhaustible resources," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 8(3), pages 351-366, October.
    12. Sadorsky, Perry, 1999. "Oil price shocks and stock market activity," Energy Economics, Elsevier, vol. 21(5), pages 449-469, October.
    13. Abarbanell, Jeffrey S & Bernard, Victor L, 1992. "Tests of Analysts' Overreaction/Underreaction to Earnings Information as an Explanation for Anomalous Stock Price Behavior," Journal of Finance, American Finance Association, vol. 47(3), pages 1181-1207, July.
    14. Roberts, Mark C., 2009. "Duration and characteristics of metal price cycles," Resources Policy, Elsevier, vol. 34(3), pages 87-102, September.
    15. Kriechbaumer, Thomas & Angus, Andrew & Parsons, David & Rivas Casado, Monica, 2014. "An improved wavelet–ARIMA approach for forecasting metal prices," Resources Policy, Elsevier, vol. 39(C), pages 32-41.
    16. Jerrett, Daniel & Cuddington, John T., 2008. "Broadening the statistical search for metal price super cycles to steel and related metals," Resources Policy, Elsevier, vol. 33(4), pages 188-195, December.
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    22. Chen, Yanhui & Zhang, Chuan & He, Kaijian & Zheng, Aibing, 2018. "Multi-step-ahead crude oil price forecasting using a hybrid grey wave model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 98-110.
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