This　paper　proposes　five　new　simple　moment　estimators　of　the　effective　spread　based　on　the　covariance　estimator　of　Roll　(1984)　and　the　High-Low　estimator　recently　developed　by　Corwin　and　Schultz　(2012).　And　then　the　authors　theoretically　investigate　the　statistical　properties　of　six　simple　High-Low　spread　estimators　including　Corwin　and　Schultz＇s　estimator.　The　biases　and　mean　squared　errors　(MSE)　of　these　six　estimators　have　been　derived　and　compared　with　each　other　asymptotically,　which,　together　with　the　subsequent　simulation　study,　reveal　explicitly　the　superior　performance　of　newly　developed　High-Low　estimators　over　Corwin　and　Schultz＇s　estimator　in　both　ideal　and　non-ideal　conditions.　Moreover,　this　paper　also　develops　GMM　estimators　constructed　by　three　or　more　moment　conditions　and　compares　with　the　six　simple　High-Low　estimators.　Finally,　several　example　applications　on　the　U.S.　and　Chinese　financial　markets　are　conducted　to　demonstrate　the　superior　performance　of　the　new　High-Low　estimators.　The　results　provide　alternative　choices　for　identifying　the　liquidity　proxies　that　well　capture　different　structure　of　markets