The study looked at how to better predict stock market volatility in the Gulf Cooperation Council (GCC) using long-memory GARCH-class models. They found that models with long memory and asymmetry are more accurate in capturing volatility. The FIAPARCH model with skewed Student distribution was the best for estimating value at risk and expected shortfall. This model outperformed other models in predicting market risk. Overall, long-memory, asymmetry, persistence, and fat tails are important factors to consider when predicting volatility and assessing total risk in the GCC markets.