In this paper, we explore the long-range memory on energy markets volatility and value-at-risk (VaR). The main question is: can we estimate better the VaR if long memory exists? To investigate this question several GARCHtype processes, including the FIGARCH process, have been implemented to some main energy products’daily prices (January 1986 to July 2007). Value at risk was estimated for both the short and the long trading positions and at various confidence levels. We suggest using normal, Student and skewed Student distributions for divers GARCH-type processes. The GARCH-type VaR performance is assessed by estimating the failure rate of the Kupiec test statistic. Consistent with previous studies, our results show that energy price volatility exhibits a long-range memory. The VaR computed through a skewed Student-t FIGARCH process provides the best performance for both the short and the long trading positions.