Modeling Nigeria Crude Oil Price with Selected Error Distribution

Abstract

The crude oil sector plays a vital role in Nigeria's economy, accounting for the majority of government revenue and foreign exchange earnings. However, the inherent volatility in crude oil prices, driven by global factors and market shocks, poses significant challenges to economic stability and fiscal planning. This study aims to model Nigeria's crude oil price dynamics by incorporating selected error innovations to enhance the accuracy of volatility modeling and forecasting.
Using a dataset spanning 1960 to 2024, econometric models such as GARCH, EGARCH, and TGARCH were employed to analyze the impact of error innovations on oil price volatility. The research evaluates the performance of these models in capturing stylized facts, such as volatility clustering and asymmetries, while considering different error distributions. Findings highlight the critical role of error innovations in explaining price fluctuations and demonstrate the superior forecasting accuracy of models that incorporate these shocks.
The study provides both theoretical and practical contributions, advancing econometric methodologies for volatility modeling and offering insights for policymakers, investors, and industry stakeholders. Accurate forecasts can aid in mitigating economic risks, improving fiscal policy formulation, and guiding investment decisions in Nigeria's oil sector. Despite limitations related to model assumptions, data quality, and structural changes in the oil market, the research underscores the importance of error innovations in understanding and managing crude oil price volatility.