Application of Garch Models to Estimate and Predict Financial Volatility of Daily Stock Returns in Nigeria

This paper estimates the optimal forecasting model of stock returns and the nature of stock returns volatility in Nigeria using daily All-Share stock data. The study unlike previous ones estimates six sets of symmetric and asymmetric GARCH-family models of stock returns volatility (three of which are augmented with trading volume) in three different set of error distributions: normal, student’s t and generalized error distribution (GED) with a view to selecting the model with best predictive power. Relying on root mean square error (RMSE) and Thiel’s Inequality Coefficient, GARCH (1,1) and augmented EGARCH(1,1) in GED proved to possess the best forecasting capability as adjudged by the last 30 days out-of-sample forecast. Our finding also suggests the presence of leverage effect and decline in persistence parameter after incorporating trading volume. Overall, the result provides evidence of high probability of making negative return from investment in the Nigerian stock market over the sample period. The empirical merit of the model is, thus, its potential for applications in analysis of value at risk (VAR) of quoted stocks and, therefore, evaluation of risk premia that guide investors’ choice of stock portfolio.