Nowadays, risk management is an important issue. A standard benchmark used to measure and to manage market risks is the Value-at-Risk (VaR).To evaluate a portfolio value-at-risk (VaR), Monte Carlo analysis is by far the most powerful method. However, the biggest drawback of this method is its computational time. In this paper, we model the return of risk factors with a multivariate normal and provide an efficient method, a bootstrap algorithm with importance resampling, to estimate portfolio loss probability and portfolio value-at-risk. As an illustration of our proposed methods, we report an empirical study based on two stock index returns in Taiwan, the Taiwan cement corporation and the ASUS.