Modelling Volatility of Stock Returns: Is GARCH(1,1) enough?
Keywords:
Volatility, Modelling, stock returns, GARCH(1, 1).Abstract
In this paper, we apply the Generalized autoregressive conditional Heteroscedasticity (GARCH) model of different lag order to model volatility of stock returns on Uganda Securities Exchange (USE). We use the Quasi Maximum Likelihood Estimation (QMLE) method to estimate the models. Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC) are used to select the best GARCH(p,q) model. From the empirical results, it has been found that USE returns are non-normal, positively skewed and stationary. Overall, GARCH(1,1) outperformed the other GARCH(p,q) models in modeling volatility of USE returns.
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