With global competition of the financial sector and financial deregulation，commercial banks are facing increasing operational risk which has become the focus of attention． Therefore，reliable operational risk measurement is becoming increasingly important for commercial banks and other financial institutions． In this paper，nonparametric methods based on heavy-tailed distributions are applied to operational risk measurement．The main advantage of these nonparametric methods is that there are no assumptions made about the shape of loss distributions． It avoids estimate deviation caused by unwittingly mis-specified models． Meanwhile，according to the characteristics of heavy-tailed distributions，a new method to estimate the mean of loss distributions is put forward，and the adjusted mean focuses more on the tail part of loss distributions． The empirical results demonstrate that the adjusted mean exceeds the sample mean，which is in more conformity with the right heavy-tailed distributions’characteristics． This paper employs non-parametric approaches and constructs a consistent and unbiased point and interval estimates for VaＲ． It has overcome the weakness of underestimating of traditional VaR． We discuss three methods of estimating confidence intervals to improve the accuracy of risk measurement． As a consequence，DT ( Data Tilting) interval estimates turned out to be the best．