African Journal of
Mathematics and Computer Science Research

  • Abbreviation: Afr. J. Math. Comput. Sci. Res.
  • Language: English
  • ISSN: 2006-9731
  • DOI: 10.5897/AJMCSR
  • Start Year: 2008
  • Published Articles: 243

Full Length Research Paper

Volatility measure of Nigeria crude oil production as a tool to investigate production variability

Usoro A. E.
  • Usoro A. E.
  • Department of Mathematics and Statistics, Akwa Ibom State University, Mkpat Enin, Akwa Ibom State, Nigeria.
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Ikpang I. N.
  • Ikpang I. N.
  • Department of Mathematics and Statistics, Akwa Ibom State University, Mkpat Enin, Akwa Ibom State, Nigeria.
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George E. U.
  • George E. U.
  • Department of Mathematics and Statistics, Akwa Ibom State University, Mkpat Enin, Akwa Ibom State, Nigeria.
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  •  Received: 19 April 2019
  •  Accepted: 30 October 2019
  •  Published: 31 January 2020

Abstract

The interest to carry out volatility analysis of crude oil production in Nigeria in this paper is motivated by the shortfalls in quantities of crude oil produced in recent past, given the country’s high dependence on oil and its contribution to the nation’s economic development. In 2016 precisely, the country experienced drastic instability in prices of crude oil at international markets and dwindling production quantities due to vandalism on oil facilities and other corrupt practices in the sector. This paper aims at using volatility measures to investigate variability of crude oil production as an assumed contributor to the economic downturn observed in the recent past in Nigeria. The data used are crude oil production data in millions of barrels collected from NNPC Statistical Bulletin. Variance of the crude oil series has been fitted with ARCH (2) model. ARCH (3) and GARCH (3,3) models are also fitted to the variance of the error obtained from ARIMA(0,1,1). ARCH and GARCH models have shown evidence of volatility in the series. The parameter estimate of the non-linear component of the bilinear model fitted to the crude oil data could not capture volatility clustering. This explains the superiority of ARCH and GARCH model over bilinear model when fitting volatile series. Synonymous with oil price volatility, evidence has it that crude oil production data are volatile. Although, Nigerian government’s intervention and negotiations with Niger Delta Militants to end operational attacks on oil facilities yielded positive results, fact has been established that the two economic variables (production and price) are volatile, as contributors to the recent past economic recession in Nigeria. It is important for the stakeholders in the sector and Nigerian government to always exhibit proactive measures against illicit activities that negatively affect oil production at all times and ensure maximum control of crude oil production process and militating factors during price shocks to avoid uncontrollable economic instability.

Key words: Crude oil, volatility, ARIMA Model, ARCH, GARCH, BARIMA Model.