過去應用GARCH模型於期貨避險方面的研究大多僅侷限於雙資產避險，雙資產以上避險的研究相對較少，其主要原因為維度上的詛咒(curse of dimension)，當維度太高即會造成參數過多難以收斂的問題。本文試圖採用結合獨立成分分析法（Independent Component Analysis，簡稱ICA）的ICA-GARCH模型來解決此問題，透過其維度降階與捕捉波動叢聚的特點，探討該模型於多資產下的外匯投資組合期貨避險績效。實證結果顯示，ICA-GARCH模型於雙資產外的績效不比傳統BEKK-GARCH模型差，而在雙資產以上的投資組合避險下，除了維度降階的優點更加明顯外，ICA-GARCH在樣本外的結果均有較佳的績效。

The main focus of previous research on dynamic futures hedging with GARCH models is restricted to bivariate. This is because the problem of curse of dimension inherent in the multivariate portfolio hedging that creates problems of over-parameterization and convergence. This article attempts to apply Independent Component Analysis GARCH (ICA-GARCH) for dynamic futures hedging for foreign exchange data. ICA-GARCH possesses properties of dimension reduction and volatility clustering observed frequently in the financial data and avoids the problems of curse of dimension. Empirical results show that in general ICA-GARCH is not inferior to BEKK-GARCH model out-of-sample for bivariate hedging and for the case of portfolio hedging with more than one currency, the benefits of dimension reduction is getting clearly and moreover, ICA-GARCH consistently outperforms BEKK-GARCH out-of sample.