title: Day-ahead crude oil price forecasting using a novel morphological component analysis based model.
abstract: As a typical nonlinear and dynamic system, the crude oil price movement is difficult to predict and its accurate forecasting remains the subject of intense research activity. Recent empirical evidence suggests that the multiscale data characteristics in the price movement are another important stylized fact. The incorporation of mixture of data characteristics in the time scale domain during the modelling process can lead to significant performance improvement. This paper proposes a novel morphological component analysis based hybrid methodology for modeling the multiscale heterogeneous characteristics of the price movement in the crude oil markets. Empirical studies in two representative benchmark crude oil markets reveal the existence of multiscale heterogeneous microdata structure. The significant performance improvement of the proposed algorithm incorporating the heterogeneous data characteristics, against benchmark random walk, ARMA, and SVR models, is also attributed to the innovative methodology proposed to incorporate this important stylized fact during the modelling process. Meanwhile, work in this paper offers additional insights into the heterogeneous market microstructure with economic viable interpretations.
authors: School of Economics and Finance, Xi'an Jiaotong University, Xi'an 710049, China ; International Business School, Shaanxi Normal University, Xi'an 710062, China.
authors: Qing
authors: Q
authors: Zhu
authors: School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China.
authors: Kaijian
authors: 0000-0003-0604-0583
authors: K
authors: He
authors: School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China ; College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China.
authors: Yingchao
authors: Y
authors: Zou
authors: International Business School, Shaanxi Normal University, Xi'an 710062, China ; Department of Management Sciences, City University of Hong Kong, Tat Chee Avenue, Kowloon Tong, Kowloon, Hong Kong.
authors: Kin Keung
authors: KK
authors: Lai
mesh_terms: D003132:Commerce / Q000191:economics / Q000639:trends*; D005544:Forecasting; D008962:Models, Theoretical; D010578:Petroleum / Q000191:economics*
chemical_list: D010578:Petroleum