Applications of Neural Networks in Modeling and Forecasting Volatility of Crude Oil Markets: Evidences from US and China

Abstract:

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Previous researches on oil price volatility have been done with parametric models of GARCH types. In this work, we model volatility of crude oil price based on GARCH(p,q) by using Neural Network which is one of powerful classes of nonparametric models. The empirical analysis based on crude oil prices in US and China show that the proposed models significantly generate improved forecasting accuracy than the parametric model of normal GARCH(p,q). Among nine different combinations of hybrid models (for p = 1,2,3 and q = 1,2,3), it is found that NN-GARCH(1,1) and NN-GARCH(2,2) perform better than the others in US market whereas, NN-GARCH(1,1) and NN-GARCH(3,1) outperform in Chinese case.

Info:

Periodical:

Advanced Materials Research (Volumes 230-232)

Edited by:

Ran Chen and Wenli Yao

Pages:

953-957

DOI:

10.4028/www.scientific.net/AMR.230-232.953

Citation:

P. H. Ou and H. S. Wang, "Applications of Neural Networks in Modeling and Forecasting Volatility of Crude Oil Markets: Evidences from US and China", Advanced Materials Research, Vols. 230-232, pp. 953-957, 2011

Online since:

May 2011

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$35.00

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