MODELLING TIMES SERIES VOLATILITY: A CASE STUDY OF NIGERIA ECONOMIC VARIABLES

Abstract

The profound vulnerability of the Nigerian economy to global oil price shocks necessitates a robust analysis of its macroeconomic volatility transmission mechanisms. This study employed the Dynamic Conditional Correlation Multivariate GARCH (DCC-MGARCH) model to investigate the interconnected volatility and time-varying spillovers among crude oil prices, exchange rates, inflation, and stock market performance using monthly data from January 2003 to December 2023. The results reveal significant volatility persistence and strong interdependencies among these variables, identifying oil price shocks and exchange rate fluctuations as primary drivers of volatility in inflation and stock returns. The analysis further uncovers distinct volatility regimes, including near-integrated GARCH processes for inflation and stock returns, and non-normal, fat-tailed distributions across all return series. While the model confirms high correlation persistence, it also exposes specification challenges, indicating that standard DCC frameworks may be inadequate for fully capturing the complex volatility dynamics inherent in Nigeria's oil-dependent economy. The study concludes that Nigeria remains acutely susceptible to oil-driven macroeconomic instability, underscoring the critical need for diversification policies and enhanced risk management strategies. These findings provide valuable insights for policymakers and investors while contributing to existing empirical literatures for analyzing multivariate financial-market interdependencies in resource-dependent emerging economies.