Stochastic volatility models have become essential for financial modelling and forecasting.The present thesis works with a two-factor stochastic volatility model that is reduced to four parameters. We start by making the case for the model that best fits data, use that modelto produce said parameters and then analyse the time series of these parameters. Suitable ARIMA models were then used to forecast the parameters and in turn, the implied volatilities.It was established that fitting the model for different groups of maturities produced better results. Moreover, we managed to reduce the forecasting errors by forecasting according to the different maturity groups.