近幾年來，在金融界一個重要的議題是風險管理，其中風險值(VaR) 是用以度量且管理市場風險的參考。本文目的在於計算投資組合之風險值，以及風險值之尾端估計機率，再與原始設定之尾端機率作比較。而蒙地卡羅分析到目前為此是最有用的方法之一，其最大的缺點是計算時間較長。在本文中，我們假設風險因子為一個多維常態分配，利用重點抽樣方法，增加尾端機率樣本被抽樣的機率，配合拔靴法，即重點抽樣下的拔靴法估計投資組合之風險值，以及估計風險值之尾端機率。最後使用本文所建議的方法，對台灣兩個股票之投資組合，台泥與華碩，做一個實證的分析。 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.
