Wavelet Entropy Based Analysis and Forecasting of Crude Oil Price Dynamics

Entropy

Yingchao Zou¹  Lean Yu¹  Kaijian He¹ 

[1] School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China; E-Mails:

关键词: wavelet entropy;    wavelet analysis;    crude oil forecasting;    Autoregressive Moving Average (ARMA) model;   

DOI  :  10.3390/e17107167

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.