Factor models are commonly used in Finance to understand relationships between variables. This study combines a Gaussian factor model with a GQARCH model to improve forecasting accuracy. By considering variations in factor variances, the model's identification issues are resolved. The researchers used Kalman filter and maximum likelihood methods to estimate parameters. Comparing forecasts from the GQARCH-Factor Model with other methods, results show that this approach produces better predictions overall.