Due to its dynamic nature, the capital market faces more uncertainty compared to other financial markets such as bank deposits and corporate bonds; therefore, it is expected to have higher returns. This feature has led to the prediction of financial market volatility and stock prices of companies listed on the stock exchange always receiving attention from researchers and analysts. In this article, by examining the common characteristics of financial time series, the volatility of daily stock returns in the Iranian capital market is modeled. Then, the general trend of fitting the mean and volatility models of financial time series is explained. Given that variance heterogeneity is observed in many financial time series, symmetric and asymmetric conditional heteroscedasticity models will be introduced and examined. To evaluate the introduced models, Mellat Bank stock data is used and the trend of fitting different models is presented, along with sensitivity analysis to different distributions of innovation factors. Finally, after evaluating the goodness of fit of the proposed model, an in-sample forecast of Mellat Bank stock returns and volatility will be made and compared with the actual values ​​of the series returns.