Modelling and Forecasting Stock Returns Volatility on Uganda Securities Exchange Using Univariate Garch Models

Abstract

Stock returns volatility of daily closing prices of the Uganda Securities Exchange (USE) all share index over a period of 04/01/2005 to 18/12/2013 is Modelled. We employ di erent univariate Generalised Autoregressive Conditional Heteroscedastic( GARCH) models; both symmetric and asymmetric. The models include; GARCH(1,1), GARCH-M, EGARCH(1,1) and TGARCH(1,1). Quasi Maximum Likelihood(QML) method was used to estimate the models and then the best performing model obtained using two model selection criteria; Akaike Information criterion(AIC) and Bayesian Information criterion(BIC). Their forecasting abilities was determined using two loss functions; Mean square error (MSE) and Mean absolute error (MAE). The GARCH(1; 1) model outperformed the other competing models in modelling volatility while EGARCH(1; 1 )performed best in forecasting volatility of USE returns.