衍生性商品蓬勃發展，金融市場上重大事件頻傳，使得企業更注重風險控管。風險值 (Value-at-Risk)模型由於其觀念簡單與計算容易的優點而被市場普遍接受，在不同的假設與估計方式下，衍生出了各式各樣的風險值模型，但風險值的估計除須具備準確性之外，還必須考慮到資金使用之效率。本研究除了採用常用的變異數-共變異數法以及在文獻中實證績效不錯的歷史模擬法，配合Efron(1993)之拔靴複製法來建製樣本資料，另外考慮由於衍生性金融商品高槓桿的特性，容易造成損失擴大，因此採用近來新興的極值理論 (EVT)等三種風險預測模型來估計風險值，其中運用Danielssion & de Vries(1997)之門檻值選取方式來選取門檻值，並用一般化柏瑞圖分配來描述超過門檻值之尾端樣本分配。實證結果顯示，變異數-共變異數法的估計準確度有待加強，而歷史模擬法在總括來看，是所有模型中最準確的，但在價外買權情況下，其表現較其他模型稍遜。至於極值理論雖然表現較歷史模擬法稍遜，但其在極端事件發生之下，能夠確實估計。

Empirical evidence rejects the assumption that the distribution is normal and suggests that the distribution of financial asset returns be heavy-tailed. We compare the performance of extreme value theory in VaR calculations with that of other well-known modeling techniques, such as variance-covariance method and historical simulation. Using GARCH(1,1), EWMA and historical volatility to estimate the volatility. In extreme value method, we choose the peaks over thresholds to derive a natural model for the point process of large losses exceeding a high threshold. Moreover, we use the generalized Pareto distribution (GPD) to describe the samples which exceeding threshold. We provide the historical simulation is the most useful VaR forecast model. But in the extreme things, EVT is better than historical simulation.

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