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(Journal Article) |
Journal of Applied Mathematics 2012 (2012)
Forecasting Crude Oil Price and Stock Price by Jump Stochastic Time Effective Neural Network Model
Jun Wang
,
Huopo Pan
,
Fajiang Liu
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.