「高報酬、高風險」是永恆不變的投資定律，相較於銀行的無風險利率，其他理財工具雖具備較高的預期報酬，卻也潛伏著較大的投資風險。過去常以「敏感度」、「波動性」與「機率」來衡量風險概念，但這些指標卻無法有效滿足投資人僅關注損失風險的事實。風險值（VaR, Value-at-Risk）擁有動態管理風險與量化風險的優點，並具有僅描述投資部位下方風險的特性，正可修正傳統風險指標之不足。

　　近年來，共同基金因為具有「專業投資」與「藉由投資組合分散投資風險」的特性而成為熱門的理財工具。但統計資料卻顯示半數以上基金之年化報酬率劣於定存利率，所以，能否選取較有潛力的投資標的乃成為獲利與否的關鍵因素。為了建立有效的投資績效指標，本研究嘗試以風險值改善傳統指標建構於常態假設的偏誤，並以市場實際數據驗證其有效性。

　　本研究根據樣本之常態檢定結果選定蒙地卡羅模擬法與歷史模擬法為風險值模型，經由實證結果發現，蒙地卡羅模擬法可藉由微調係數使風險值估計量更貼近實際風險，在應用上也比歷史模擬法更有彈性，但數倍於其他風險值模型的人力、物力與時間成本則是其最大缺點。而在投資績效指標之應用部份，雖然在一般情況下，風險值並無法有效改善傳統投資績效指標建構於常態假設下的偏誤，但本研究卻發現VaRRAF觀念可補足風險值在投資損失超乎預期時所無法提供的風險資訊。另外，本研究亦發現集中市場加權指數平均報酬率屬於動態指標，比無風險利率更適合引用為投資標竿；而當投資市場處於長期多頭時，風險收益（VaB）之參考價值也勝於風險值。整體來說，若能將風險值的模型誤差控制在可接受的範圍之內，風險值的確是一種能夠明確衡量損失幅度的風險指標。

　“High return accompanies higher risk” is the constant law of investment. Compared to risk-free interest rate, other tools of personal finance are more risky in despite of higher expected return on investment. Sensitivity, volatility and probability are often used to interpret the risk of investment in the past. These indicators, however, cannot effectively meet the needs of investors who only care the risk of loss. Value-at-Risk（VaR）has the advantages of both dynamic management of risk and risk quantification. It also gives a good description of downside risk of the investment, which traditional indicators cannot achieve.

　In recent years, securities investment trust funds（or mutual funds）become one of the most popular tools for personal finance due to the characteristics of professional investment and dispersing the investment risk while statistical data shows that more than half yearly return rate of funds is lower than the risk-free rate of interest. As a result, the key to make more profit is to pick out investments with great potential profit. In order to develop an effective index of investment performance, a VaR method is developed to improve traditional indicators under the assumption of normal-distribution bias. Its effectiveness is verified with the real market data.

　After sample test of normal-distribution, Monte Carlo Simulation Approach and Historical Simulation Approach are chosen as the VaR models in this research. According to empirical tests, Monte Carlo Simulation can make VaR more close to the real risk of investment by fine-tuning the estimate of VaR, and it is more flexible than Historical Simulation Approach in application. However, the most unfavorable is that its cost of time, manpower and material resources is times to those of the other VaR models. With regard to the application of performance indicators, the concept of VaRRAF can provide information about risk as investment loss exceeds the expectation. In addition, this research finds that the average return rate of weighted TSE index is a dynamic indicator and hence more suitable to be used as an investment benchmark than risk-free interest rate. When the investment market is in bull in the long run, VaB is superior to VaR for reference. Generally speaking, VaR is certainly an excellent risk indicator to measure the amount of loss if its model bias is controlled in a tolerant range.

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