A Hybrid Approach for Forecasting of Oil Prices Volatility

Akbar Komijani, Esmaeil Naderi and Nadiya Gandali Alikhani ()

MPRA Paper from University Library of Munich, Germany

Abstract: This study aims to introduce an ideal model for forecasting crude oil price volatility. For this purpose, the ‘predictability’ hypothesis was tested using the variance ratio test, BDS test and the chaos analysis. Structural analyses were also carried out to identify possible nonlinear patterns in this series. On this basis, Lyapunov exponents confirmed that the return series of crude oil price is chaotic. Moreover, according to the findings, the rate of return series has the long memory property rejecting the efficient market hypothesis and affirming the fractal markets hypothesis. The results of GPH test verified that both the rate of return and volatility series of crude oil price have the long memory property. Besides, according to both MSE and RMSE criteria, wavelet-decomposed data improve the performance of the model significantly. Therefore, a hybrid model was introduced based on the long memory property which uses wavelet decomposed data as the most relevant model.

Keywords: Forecasting; Oil Price; Chaos; Wavelet Decomposition; Long Memory (search for similar items in EconPapers)
JEL-codes: C53 C58 G17 (search for similar items in EconPapers)
Date: 2013-01-04
New Economics Papers: this item is included in nep-ene and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Related works:
Journal Article: A hybrid approach for forecasting of oil prices volatility (2014)
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