We　investigate　point　estimation　and　confidence　interval　estimation　for　the　heavy-tailed　generalized　Pareto　distribution　(GPD)　based　on　the　upper　record　values.　When　the　shape　parameter　is　known,　the　bias-corrected　moments　estimators　and　maximum　likelihood　estimators　(MLE)　for　the　location　and　scale　parameters　are　derived.　However,　in　practice,　the　shape　parameter　is　typically　unknown.　We　propose　the　MLE　by　a　new　methodological　approach　for　all　three　parameters　of　the　heavy-tailed　GPD　when　the　shape　parameter　is　unknown.　Confidence　intervals　for　the　location　and　scale　parameters　are　constructed　by　the　equal　probability　density　principle.　If　the　shape　parameter　is　known,　we　can　find　known　distributions　of　the　pivots　of　the　location　and　scale　parameters,　but　not　approximate.　While　if　the　shape　parameter　is　unknown,　the　distributions　of　the　pivots　are　closed　linked　to　an　estimation　of　the　shape　parameter.　The　advantage　of　our　method　is　that　the　proposed　interval　estimation　provides　the　smallest　confidence　interval,　regardless　of　whether　the　distribution　of　the　pivot　is　symmetric.　Extensive　simulations　are　used　to　demonstrate　the　performance　of　the　point　estimation　and　confidence　intervals　estimation　and　show　that　our　method　outperforms　the　traditional　technique　in　most　cases.