Forecasting metal prices with a curvelet based multiscale methodology. (September 2015)

• Record Type: Journal Article
• Authors: He, Kaijian; Lu, Xingjing; Zou, Yingchao; Keung Lai, Kin
• Is Part Of: Resources policy. Volume 45(2015)
• Page Start: 144
• Page End: 150
• Publication Date: 2015-09
• Subjects: Q32 -- C22 -- C53; Curvelet analysis -- Metal price forecast -- Time delay embedding -- ARMA model; Mines and mineral resources -- Periodicals; Ressources minérales -- Périodiques; Ressources naturelles -- Gestion -- Périodiques; Environnement -- Politique gouvernementale -- Périodiques; 333.8
• DOI: 10.1016/j.resourpol.2015.03.011
• Languages: English
• ISSNs: 0301-4207

Abstract

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. Abstract : Highlights: We model the chaotic metal price using the heterogeneous modeling approach. Curvelet analysis is introduced to separate nonlinear dynamics in metal prices. Underlying nonlinear dynamics are revealed using Time Delay Embedding algorithm. We propose the minimum entropy criteria to determine the model specification. The forecasting accuracy improves at the statistically significant level.