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The risk of the financial market is the focus of global financing institution and supervisory authorities. Correspondingly, the accurate measure of volatility is central to the measure of the Value-at-Risk. VaR is a popular method to computer the finance risk at present, and the key of calculating VaR is predicting volatility exactly. About the measurements of volatility, the first method is the initial classic model that the volatility is estimated from the financial analysis model (such as Black-Scholes). The second method is the ARCH model and the SV model. The third method is realized volatility which is based upon high frequency data. From these, we can see the measure of volatility is developed quickly. In this paper, we compare the performance of the value-at-risk based on realized volatility which is based upon high frequency data and GARCH(1,1) model under different distributions, and the results show the performance of value-at-risk using realized volatility is more effective.