Modeling　excesses　over　a　high　threshold　and　estimating　extreme　tail　risk　are　two　utmost　studies　in　the　extreme　value　literature.　Traditional　techniques　are　limited　on　handling　these　two　challenges.　To　better　analyze　this　type　of　data,　we　propose　a　novel　approach　which　utilizes　the　generalized　Pareto　distribution　(GPD)　in　the　peaks-over-threshold　(POT)　framework.　Under　the　proposed　approach,　by　using　partial　L-moments　(PL-moments),　computational　efficient　estimators　are　derived　for　the　parameters　in　the　GPD.　Additionally,　we　propose　method　to　estimate　the　tail　expectiles　and　apply　a　recently　developed　stopping　rule　to　find　the　optimal　threshold.　Various　simulation　researches　show　that　the　proposed　approach　outperforms　the　traditional　techniques　in　some　aspects.　Last,　we　apply　the　proposed　method　to　the　Shanghai　Stock　Exchange　data　for　comprehensively　illustrating　the　details　and　providing　guidance　for　future　applications.