  We develop Edgeworth expansion theory for spot volatility estimator under
general assumptions on the log-price process that allow for drift and leverage
effect. The result is based on further estimation of skewness and kurtosis,
when compared with existing second order asymptotic normality result. Thus our
theory can provide with a refinement result for the finite sample distribution
of spot volatility. We also construct feasible confidence intervals (one-sided
and two-sided) for spot volatility by using Edgeworth expansion. The Monte
Carlo simulation study we conduct shows that the intervals based on Edgeworth
expansion perform better than the conventional intervals based on normal
approximation, which justifies the correctness of our theoretical conclusion.
