金融資產報酬通常為厚尾且非常態為主，而過去多數文獻的模型以常態分配為假設，而Copula函數能夠依據個別資料之間的關聯性找出最適之分配，使得模型的運用上更加有彈性。
本文主要分別利用傳統避險模型、固定條件相關(CCC-GJR-GARCH)模型、動態條件相關(DCC-GJR-GARCH)模型以及以Copula-based GJR-GARCH模型，利用最小變異避險理論為避險績效衡量標準，依據樣本內及樣本外進行避險比率及避險績效的實證，找出最佳的模型，提供最適的避險比率之衡量與績效評估之比較。實證結果發現以Copula 為基礎的GJR-GARCH模型的避險績效較傳統OLS模型佳。

Financial asset returns are usually fat-tailed and non-Gaussian. In the past, most
of the literatures have the normal distribution assumption. The Copula functions that
based on the relationship between the individual assets have more flexible than the
other models to find the optimal allocation.
In this paper, we use traditional hedging model, fixed conditional correlation
(CCC-GJR-GARCH) model, dynamic conditional correlation (DCC-GJR-GARCH)
model and the Copula-based GJR-GARCH model for the estimation of the optimal
hedge ratio and the hedging performance measure by the theory of minimum variance.
Empirical results show that the Copula-based GJR-GARCH models perform more
effectively in the in-sample test. In addition, all of the dynamic hedging models
perform more effectively than OLS model in the out-sample test.

目錄
表目錄	V
圖目錄	VI
第一章	緒論	1
第一節	研究動機	1
第二節	研究目的	3
第三節	研究架構與流程	4
第二章	理論基礎與文獻回顧	6
第一節	商品期貨契約介紹	6
第二節	避險理論回顧	10
第三節	避險模型文獻回顧	12
第四節	copula模型文獻回顧	14
第五節	黃金與白銀相關文獻回顧	17
第六節	小結	18
第三章	研究方法	19
第一節	Copula介紹	19
第二節	避險模型	25
第三節	避險績效指標	31
第四章	實證結果與分析	33
第一節	資料來源與型態	33
第二節	實證模型與過程	33
第三節	實證結果分析	35
第四節	績效評估	48
第五章	結論與建議	52
第一節	結論	52
第二節	建議	54
參考文獻	55

表目錄
表1、COMEX及TOCOM黃金期貨契約規格	9
表2、COMEX及TOCOM白銀期貨契約規格	10
表3、Copula函數和相對應之相關係數Kendall’s τ	24
表4、敘述統計量	36
表5、單根檢定結果	39
表6、COMEX市場之CCC-GJR-GARCH模型參數	40
表7、TOCOM市場之CCC-GJR-GARCH模型參數	42
表8、COMEX市場之DCC-GJR-GARCH模型動態相關係數參數	43
表9、TOCOM市場之DCC-GJR-GARCH模型動態相關係數參數	43
表10、COMEX 市場之Copula-GJR-GARCH之所有估計參數	44
表11、TOCOM 市場之Copula-GJR-GARCH之所有估計參數	46
表12、不同避險模型之樣本內績效評估	49
表13、不同避險模型之樣本外績效評估(預測向前5天)	50



圖目錄
圖1、研究流程圖	5
圖2、Gaussian Copula函數之機率密度函數	23
圖3、T Copula函數之機率密度函數	23
圖4、Clayton Copula函數之機率密度函數	23
圖5、避險策略之移動視窗說明	34
圖6、個別市場的黃金報酬	37
圖7、個別市場的白銀報酬	38
圖8、COMEX市場之商品價格走勢圖	39
圖9、TOCOM市場之商品價格走勢圖	41

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