Symmetric Volatility Forecast Models for Crude Oil Price in Nigeria

Symmetric Volatility Forecast Models for Crude Oil Price in Nigeria

Onyeka-Ubaka J.N.Agwuegbo S.O.N. Abass O. Imam R.O. 

Department of Mathematics, Faculty of Science, University of Lagos, Nigeria

Department of Statistics, School of Science, Federal University of Agriculture, Abeokuta, Nigeria

Department of Computer Sciences, Bells University, Ota, Nigeria

Corresponding Author Email: 
jonyeka-ubaka@unilag.edu.ng
Page: 
8-14
|
DOI: 
https://doi.org/10.18280/mmc_d.390102
Received: 
21 January 2018
|
Accepted: 
15 April 2018
|
Published: 
31 December 2018
| Citation

OPEN ACCESS

Abstract: 

Oil price data are available at a high frequency and therefore, there is increasing evidence of the presence of statistically significant correlations between observations that are large apart and possibility of conditional heteroskedasticity. This paper empirically analyzes the crude oil price return volatility patterns using autoregressive integrated moving average (ARIMA) and generalized autoregressive conditional heteroskedasticity (GARCH) family models. The results reveal that GARCH (1, 1) and ARIMA (1, 1, 0) models perform well in capturing the stylistic features present in high frequency crude oil prices in Nigeria within the sampled period. The Holt-Winters forecast made for twenty six (26) months using ARIMA (1, 1, 0) was approximately close to the real price of crude oil per barrel as evident from the 95% confidence interval estimates. The paper practically disseminates independent and impartial crude oil price information to promote sound policymaking, efficient markets and understanding of crude oil price and its interaction with the economy and the environment.

Keywords: 

symmetric, forecast, ARIMA models, volatility clustering

1. Introduction
2. Literature Review
3. Methodology
4. Results and Discussion
5. Conclusion
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