Modeling And Measuring Oil Price Volatility In Nigeria: The Garch Technique
Ifeanyi Chukwuma Nwankwo
Department of Applied Mathematics/Statistics, Rivers State University, Port Harcourt, Nigeria
Samuel Ebiowei Otobo
Department of Applied Mathematics/Statistics, Rivers State University, Port Harcourt, Nigeria
Abstract
Crude oil price volatility and its associated risk-return trade-off are critical factors influencing national investment strategies, economic decision-making, and the financial resilience of oil-dependent nations like Nigeria. This study aims to model the price volatility and risk-return profile of the Nigerian crude oil export market using first-order symmetric and asymmetric Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models under three distributional assumptions: Normal, Student’s t, and Generalized Error Distribution (GED). The analysis is based on monthly crude oil price data obtained from the Central Bank of Nigeria’s online statistical database, covering the period from January 1987 to June 2017.
The empirical findings indicate high volatility in the crude oil market, with significant evidence of a positive risk premium—suggesting that investors are compensated for assuming additional risk. While both symmetric (GARCH(1,1)) and asymmetric (EGARCH(1,1)) models were tested, the GARCH(1,1) model under the Student’s t-distribution provided the best fit. Diagnostic tests, including the ARCH effect test, serial correlation test, and Q-Q plot analysis, confirmed the robustness and appropriateness of the selected models.
These results imply that while the Nigerian crude oil market presents opportunities for gain, it also exhibits periods of extreme volatility that necessitate cautious trading strategies, especially during high-risk periods as indicated by standard deviation metrics. The study recommends that the Nigerian government should diversify its economic base to reduce overreliance on oil revenues by investing in agriculture, manufacturing, and mining sectors. Furthermore, investors are encouraged to consider leverage effects in risk assessment models by applying asymmetric GARCH models when evaluating the volatility of macroeconomic variables