Financial asset returns are complex and not normally distributed. To accurately predict Value-at-Risk (VaR), different distribution assumptions must be considered. A study compared various models and found that accounting for heavy-tails and skewness in the distribution leads to more accurate VaR forecasts. The skew-Student distribution performed the best in all tests and confidence levels.