This paper seeks to model daily, weekly and monthly stock indices returns using GARCH (1,1) model which is expected to reproduce most of the stylized facts of financial time series data which, in most cases, are found in different types of market. In addition, the distributional behavior of returns as the data changes from daily through to monthly returns is investigated by performing the JB and K-S tests. The results indicate evidence of volatility clustering, leverage effects, Gaussianity and leptokurtic distribution in the stock returns. A key observation is that the monthly returns of the three indices follow a Gaussian distribution (i.e. as the data changes from daily through to monthly returns it follows a normal distribution).