Financial Econometrics studies have demonstrated that forecasting Value At Risk under standard normal distribution conditions could lead to underestimation of potential losses, hence of fat tail volatility risk events, other methods to improve Value At Risk forecasting have been studied and implemented such as VaRx, Conditional VaR and also utilizing student t-Distribution with variable degrees of freedom, to better estimate potential ES, Expected Shortfall, and VaR fat tail risks.