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Journal of Applied Mathematics
Volume 2012, Article ID 646475, 15 pages
http://dx.doi.org/10.1155/2012/646475
Research Article

Forecasting Crude Oil Price and Stock Price by Jump Stochastic Time Effective Neural Network Model

Department of Mathematics, Key Laboratory of Communication and Information System, Beijing Jiaotong University, Beijing 100044, China

Received 2 July 2011; Revised 19 September 2011; Accepted 10 October 2011

Academic Editor: Wolfgang Schmidt

Copyright © 2012 Jun Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The interacting impact between the crude oil prices and the stock market indices in China is investigated in the present paper, and the corresponding statistical behaviors are also analyzed. The database is based on the crude oil prices of Daqing and Shengli in the 7-year period from January 2003 to December 2009 and also on the indices of SHCI, SZCI, SZPI, and SINOPEC with the same time period. A jump stochastic time effective neural network model is introduced and applied to forecast the fluctuations of the time series for the crude oil prices and the stock indices, and we study the corresponding statistical properties by comparison. The experiment analysis shows that when the price fluctuation is small, the predictive values are close to the actual values, and when the price fluctuation is large, the predictive values deviate from the actual values to some degree. Moreover, the correlation properties are studied by the detrended fluctuation analysis, and the results illustrate that there are positive correlations both in the absolute returns of actual data and predictive data.