Market risk, caused by price changes in financial markets, is a significant financial risk. Value at Risk (VaR) measures the maximum potential loss over a specific time period. Using conditional volatility models improves VaR estimates accuracy compared to traditional methods. These models adapt better to market changes, especially during high volatility periods. The choice of distribution for standardized residue series impacts the differences between models.