臺灣博碩士論文加值系統

有鑑於指數股票型基金的興起，使用股票指數期貨規避指數股票型基金投資風險之績效成為現代投資實證研究上備受關注的議題。前人著作多採用波動不動稱族群的GARCH模型用以評估兩個市場的投資風險，用以計算避險比率，據以擬訂投資策略。惟，前述兩個市場報酬波動之間往往存在著相關性(Dependence)，怠於考量此一現象，可能產生偏誤的結果。有別於前人著作，本研究導入Copulas函數捕捉兩市場間報酬波動的相關結構現象，檢視Copulas-GJR-GARCH替代模型(Alternative model)是否能更為精確估算避險比率，產生較佳的避險績效。研究結果顯示Gumbel-Copula函數較能刻畫變數間的相關結構，而達到較佳的避險效果。但就整體避險績效而言靜態的OLS表現優於大多數的Copula避險模型以及DCC、GJR-DCC模型。證明OLS的表現也有凌駕動態避險策略的趨勢，但透過適當的Copula函數選擇，兩標的資產相關結構能否確實的被描述會導致避險績效差異。

Due to the rise of Exchange Traded Fund, using Stock Index Futures to avoid the risk of investing Exchange Traded Fund has become a more and more popular issue on study of contemporary investments. Former papers on this subject introduced the GARCH model of asymmetric volatility to estimate the risk of investing in two different markets, and to calculate hedge ratio in order to make an investing strategy. However, in former descriptions, there would be certain dependence between the two markets, there could be a biased result when this fact of the existence of the dependence is ignored. Aside from former studies, this study introduces Copulas to capture the structure of returns volatility between the two markets so that we can check if this Copula-GJR-GARCH alternative model can provide a more precise hedge ratio and generate more effective hedge performance. According to this study, adopting Gumbel-Copula is proved to more effectively show the structures between variables, as a result, it is proved to be a better method for risk hedge. However, the OLS method performs more effective than most Copula hedge models, including DCC and GJR-DCC models. Thus, it is proved that OLS tends to overtop time-vary hedge model; however, via proper Copula function choosing, whether two targeted assets' structures can be precisely described or not could differentiate their hedge performances.

目錄
頁次
目錄 I
圖目錄 III
表目錄 IV
第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究問題與目的 2
第三節 論文架構 4
第二章 文獻探討 5
第一節 指數股票型基金 5
第二節 波動與相關模型 8
第三節 關聯結構理論 10
第三章 研究方法 13
第一節 檢定分析 13
第二節 非對稱一般化自我迴歸條件異質變異模型 15
第三節 DCC-GARCH模型 16
第四節 關聯結構理論 18
第五節 避險績效之衡量 21
第四章 實證分析 24
第一節 資料來源與期間 24
第二節 樣本特徵統計分析 25
第五章 結論 33
第一節 結論 33
第二節 未來研究方向 34
參考文獻 35
圖目錄
圖1.1 全球ETF資產規模 1
圖1.2 研究架構 4
圖2.1 ETF交易過程 7
圖4.1 ETF與期貨價格及報酬走勢圖。 24
表目錄
表2.1 ETF與共同基金、指數期貨、指數選擇權之比較 8
表4.1 ETF與期貨報酬率之基本統計量 25
表4.2 雙變量 GARCH (1,1)-DCC 模型 26
表4.3 雙變量 GJR-GARCH (1,1)-DCC模型 26
表4.4 邊際函數模型(GARCH)參數估計結果 27
表4.5 邊際函數模型(GJR-GARCH)參數估計結果 28
表4.6 Copula函數估計(殘差項符合常態分配)參數估計結果 29
表4.7 Copula函數估計(殘差項符合T分配)參數估計結果 30
表4.8 Copula函數估計(殘差項符合GED分配) 參數估計結果 31
表4.9 投資組合避險效果（投資組合變異數） 32

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