This article investigated the relevance of the skewed Student-t distribution innovation in analyzing volatility stylized facts, namely, volatility clustering, volatility asymmetry, and volatility persistence, in three individual Korean shares. For this purpose, we assessed the performance of RiskMetrics and two long memory Value-at-Risk (VaR) models (FIGARCH and FIAPARCH) with the normal, Student-t, and skewed Student-t distribution innovations. From the results of the empirical VaR analysis, the skewed Student-t distribution innovation provided more accurate VaR calculations, in capturing stylized facts in the volatility of three sample returns. Thus, the correct assumption of return distribution might improve the estimated performance of VaR models in the Korean stock market.