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.

Forecasting Energy Value at Risk Using Multiscale Dependence Based Methodology

“…He et al [26] proposed a wavelet decomposed ensemble model for the crude oil market and found the improved performance of the proposed algorithm against the benchmark models [26]. Zhu et al [27] proposed the morphological component analysis-based forecasting model and found improved forecasting accuracy in the crude oil markets [27]. Li et al [28] proposed a wavelet denoising ARMA model ensembled by least square support vector regression.…”