CONDITIONAL VOLATILITY OF CRUDE OIL PRICES IN NIGERIA USING GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSKEDASTICITY MODELS

This study investigates the conditional volatility of crude oil prices in Nigeria using Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models and their variants. Given the significant economic dependence of Nigeria on crude oil revenues, understanding the dynamics of fluctuations in oil prices is critical. This study analyzes monthly and daily crude oil price data from 2006 to 2022, employing ARIMA-GARCH, TARCH, and other heteroskedasticity models under different error distributions. Statistical tests, including stationarity checks, normality measures, and heteroskedasticity diagnostics, were conducted to ensure model adequacy. Results reveal strong evidence of volatility clustering and long memory effects in the return series, with asymmetric models like TARCH outperforming symmetric GARCH in capturing leverage effects. Furthermore, volatility mean reversion and half-life analysis indicate that shocks in oil price returns persist over time but gradually revert to mean values. These findings underscore the necessity of accurate volatility modeling to enhance forecasting, risk management, and policy formulation in oil-dependent economies like Nigeria