Day-ahead crude oil price forecasting using a novel morphological component analysis based model.

Qing Zhu,Yingchao Zou,Kin Keung Lai,Kaijian He

doi:10.1155/2014/341734

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.

Highlights

With the technological advancement and development of global economic integration, both the demand and the supply of crude oil are influenced by increasingly complex and diverse market participants around the world
The lag order for autoregressive moving average model (ARMA)(r,m) in the forecasting process is determined based on the Akaike information criteria (AIC) and Bayesian information criteria (BIC) minimization principle
Based on the heterogeneous market hypothesis (HMH), this paper proposes a hybrid modeling methodology to incorporate multiscale market structure information into the modeling process, which provides a view of the microstructure of the underlying DGP, besides finer modeling accuracy

Summary

Introduction

With the technological advancement and development of global economic integration, both the demand and the supply of crude oil are influenced by increasingly complex and diverse market participants around the world. Some preliminary findings using the wavelet analysis to analyze the multiscale structure of DGP have led to some positive performance improvement in areas such as crude oil, electricity, equities, and exchange rate markets. These empirical studies have used one single family of wavelets to extract data features of interest. The introduction of MCA based approach incorporated the stylized fact that there are redundant forms of representations on the underlying data generating process, which need to be optimized, and contributes to the understanding and forecasting of the evolutions in the market microstructure.

Relevant Theories

Empirical Studies

Conclusions