This　paper　considers　the　problem　of　variance　estimation　for　sparse　ultra-high　dimensional　varying　coefficient　models.　We　first　use　B-spline　to　approximate　the　coefficient　functions,　and　discuss　the　asymptotic　behavior　of　a　naive　two-stage　estimator　of　error　variance.　We　also　reveal　that　this　naive　estimator　may　significantly　underestimate　the　error　variance　due　to　the　spurious　correlations,　which　are　even　higher　for　nonparametric　models　than　linear　models.　This　prompts　us　to　propose　an　accurate　estimator　of　the　error　variance　by　effectively　integrating　the　sure　independence　screening　and　the　refitted　cross-validation　techniques.　The　consistency　and　the　asymptotic　normality　of　the　resulting　estimator　are　established　under　some　regularity　conditions.　The　simulation　studies　are　carried　out　to　assess　the　finite　sample　performance　of　the　proposed　methods.