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

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.

Modeling the Nigerian Bonny Light Crude Oil Price: The Power of Fuzzy Time Series

“…This study indicated that despite the changes in the oil price, the energy consumption associated with oil is still the same or considerable due to low investment in other sources of energies. Usoro et al [20] fitted crude oil series variance with ARCH (2), ARCH (3) and GARCH (3,3) models and also fitted to the ARIMA (0, 1, 1) variance of the error. This study suggested that GARCH and ARCH captured the fluctuation in the series while bilinear non-linear component of the model parameter did not show evidence of volatility clustering.…”