本論文提出控制最差風險值與超越指數的雙目標投資組合最佳化模型，我們同時考慮兩種風險—指數追蹤的下方偏差(downside absolute deviation) 與最差風險值(worst-case value-at-risk, WCVaR)。並提出兩種不同模型，模型A針對兩者間的規避程度分別分配其權重，結合成單一目標函數的線性規劃模型。而模型B先計算出歷史資料中的WCVaR值，再以此風險值為限制式，使建立的投資組合與被追蹤指數報酬率的下方偏差降至最低的兩階段單目標線性規劃模型。我們使用台灣股票市場的資料進行實證，用以驗證兩模型之可行性與效能差異。實證結果顯示，不論是股市處於上漲、下跌或盤整階段，本模型所建立之投資組合的表現均能有效超越被追蹤指數。

指數型基金透過追蹤指數來提供投資人被動管理的投資策略，因而成為保守投資人的熱門投資工具。本論文的目的在於建立一個追蹤指數的同時也能有效控管損失的指數型基金。在此目標下，該基金面臨到的不單是追蹤指數的績效，還有降低資產配置風險的問題。有鑑於此，本論文融合兩種下方風險的概念：指數追蹤的下方偏差(downside absolute deviation)以及條件風險值(conditional value-at-risk, CVaR)。針對兩者間的規避程度分別分配其權重，並以該基金之平均報酬大於指數的平均報酬作為限制條件，經由改寫下方偏差與離散化CVaR後得到一個新的線性規劃模型。本論文以台灣50指數與恆生指數的歷史資料做為實證探討的對象，驗證使用本線性規劃模型所建立之指數型基金的效能。 / Index fund has become popular in these days among the conservative investors since it provides a passive investment strategy. The main purpose of this paper is to create an index fund which can replicate the performance of a broad-based index of stocks and has the ability to control the loss efficiently at the same time. For this purpose, the index fund we build confronts with not only the performance of index tracking, but also lowering the level of the risk of assets allocation. In order to accomplish the goal, we combine two concepts of downside risk: downside absolute deviation and conditional value-at-risk (CVaR). Under the constraint of average portfolio return being greater than average index return, and assign weights according to the degree of evasion to each of the risks, a linear programming model is formulated by rewriting downside absolute deviation and discretizing CVaR. The results obtained from the computational experience on Taiwan 50 index and Hang Seng index are provided for testing the efficiency of this model.