The article explores different models to predict how volatile the Zimbabwean Stock Exchange will be. They used two models, GARCH (1,1) and exponential GARCH (1,1), to analyze stock market data from 2009 to 2014. The results showed that the asymmetric EGarch (1;1) model was better at forecasting future volatility compared to the symmetric Garch (1;1) model. The data also indicated that the stock market did not follow a normal pattern and had varying levels of volatility over time. The researchers suggest using more complex Garch models for better predictions when analyzing long periods of data.