Wavelet Entropy Based Analysis and Forecasting of Crude Oil Price Dynamics

Yingchao Zou,Lean Yu,Kaijian He

doi:10.3390/e17107167

Abstract

For the modeling of complex and nonlinear crude oil price dynamics and movement, wavelet analysis can decompose the time series and produce multiple economically meaningful decomposition structures based on different assumptions of wavelet families and decomposition scale. However, the determination of the optimal model specification will critically affect the forecasting accuracy. In this paper, we propose a new wavelet entropy based approach to identify the optimal model specification and construct the effective wavelet entropy based forecasting models. The wavelet entropy algorithm is introduced to determine the optimal wavelet families and decomposition scale, that will produce the improved forecasting performance. Empirical studies conducted in the crude oil markets show that the proposed algorithm outperforms the benchmark model, in terms of conventional performance evaluation criteria for the model forecasting accuracy.

Highlights

In recent years, rapid development of electronic technology and the increasing level of global economic integration have fundamentally changed the crude oil markets, both in terms of market structure and market risk exposure
Bekiros et al [1] used the time-varying Vector Autoregressive (VAR) model to model the impact of the economic policy uncertainty on the oil price movement and found the improved forecasting performance compared to other more standard univariate models [1]
We have found some positive results in wavelet based forecasting exercises for crude oil price movement

Summary

Introduction

Rapid development of electronic technology and the increasing level of global economic integration have fundamentally changed the crude oil markets, both in terms of market structure and market risk exposure. Numerous approaches have been developed to incorporate nonlinearity, auto correlation and heteroscedasticity data features into the modeling process, aiming at improving the forecasting accuracy further. These models include structural and econometric models, artificial intelligence models, and ensemble models. Deng and Sakurai [2] used the multiple kernel learning regression method to forecast the crude oil spot price They found that information from different time frame is useful in improving the forecasting accuracy of the model [2]. Cuaresma et al [4]

Methods

Results

Conclusion