To solve moment estimation problems in heavy-tailed distribution which do not possess set of finite central moments, we introduce the theory of L-moments which is developed in hydrology. Considering the factors of anticipation and volatility and fitting the tail with Generalized Pareto Distribution using high frequency excess return in the static and dynamic condition, we check the results by invoking Kupiec-LR and dynamic quantile test. The analysis of models and VaR shows that problems of moment estimation in heavy-tailed distribution can be　solved with L-moments. Generalized Pareto Distribution is better fitted with the feature of return series in extreme condition, which has the implication that our model can catch the dynamic character of return series in extreme conditions.