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The estimation of loss distribution is
always a big issue for insurance companies. Several parametric or
nonparametric methods are introduced to fit loss distributions. In
this paper, we propose a method by combining both parametric and
nonparametric methods to solve this problem. We first determine the
threshold between large and small losses by observing the graph of
mean excess function, then use the generalized Pareto distribution,
the parametric method, to fit excess data, and use kernel density
estimation, the nonparametric method, to fit the distribution below
threshold. Finally, we use a data set about Chinese annual
earthquake loss to compare this method with other existing methods.