本文應用Hansen et al. [1]所提出的MCS(model confidence set)，替七國匯率(英鎊、日幣、港幣、新台幣、印尼盾、泰國銖)找出能夠估計出樣本外200天VaR （涉險值）最佳GARCH模型集。本文實證發現:針對不同的實證資料，會有不同的最佳MCS組合，同時不同的MCS，其組合內的各模型績效也各不相同，有的匯率以簡單的模型表現就很好，甚至優於複雜模型（有考慮偏態及槓桿效應），相反的，有的貨幣需要複雜模型，甚至包含波動率長短期效應才能預估準確，因此不同的模型適合不同的功能與不同的實證標的，所以模型信心集有其重要性，因其能結合預測能力(equal predictive ability)相當的模型，如此結合模型當優於單一模型的預估能力。

This paper compares the Value-at-Risk (VaR) forecasts delivered by 30 GARCH-family model specifications using the Model Confidence Set (MCS) procedure recently developed by Hansen et al. (2011). The MCS method is analogous to confidence interval of a parameter estimation in the sense that the MCS contains a set of statistical predictive accuracy (Equal Predictive Ability) models. Our empirical study suggests that some exchange rate's MCS contain more complicate models, such as model with skewness and leverage effect, however, other countries exchange rates can be predicted well using simplest GARCH model. It signals the importance of MCS method which permits to combine a set of superior models, and such superior set can improve the efficiency of predict accuracy than a single best model.