臺灣博碩士論文加值系統

過去研究在估計最小變異最適避險比率(minimum variance optimal hedge ratios)時，大多採用最小平方法(Ederington, 1979；Figlewski, 1984)、共整合法(Ghosh, 1993；Lien and Luo, 1993)或允許變異數隨時間變動的雙變量GARCH 系列模型(Baillie and Myers, 1991；Kroner and Sultan, 1993；Park and Switzer, 1995；Gagnon and Lypny, 1995； Kavussanos and Nomikos, 2000；Bystrom, 2003)。惟前述模型基本上皆利用期、現貨間的「線性」相關(linear correlation)來計算最適避險比率，一旦期、現貨間呈現非橢圓聯合分配時，抑或存在非線性(nonlinear)相關結構，其相關性的估計有可能出現偏誤。
具有不對稱及極值特性的copula函數不僅能打破以往的假設限制，更能呈現期、現貨間大幅共漲、共跌的特性。本研究整合copula函數與Hamilton (1989, 1994)馬可夫轉換(Markov-switching)模型，建立允許期、現貨間相關性可在兩不同相關結構間不斷轉換的dependence-switching (DS)模型。應用此模型估算不同狀態下的相關性與避險比率，據以建構股票指數期、現貨動態避險策略。本研究實證結果發現，由DS模型所建構之避險投資組合避險績效優於傳統方法(OLS、ECM 及DCC-GARCH)。

The ordinary least squares (OLS) technique (Ederington, 1979; Figlewski, 1984), the co-integration method (Ghosh, 1993; Lien and Luo, 1993), and the bivariate GARCH-type models allowing time-varying nature in asset returns (Baillie and Myers, 1991; Kroner and Sultan, 1993; Park and Switzer, 1995; Gagnon and Lypny, 1995; Kavussanos and Nomikos, 2000; Bystrom, 2003) are the most common approaches to estimate minimum-variance hedge ratios. However, those conventional approaches have been used to calculate the optimal hedge ratios in a sense of linear correlation which could result in bias estimates if the joint distribution of spot and futures is not elliptical and/or is non-linear.
Since copula functions of asymmetric dependence structures and extreme values can capture the extreme co-movements of spot and futures, this study builds a dependence-switching model (DS model), which is integrated by copula functions and Markov-switching model by Hamilton (1989, 1994) and is allowed that the dependence of spot and futures can switch between two different structures. We construct a hedging portfolio via the DS model and evaluate the dynamic hedging performance. The results show that the DS model outperforms the conventional approaches such as OLS, ECM, and DCC-GARCH.

謝 辭 i
摘 要 ii
Abstract iii
目錄 iv
表次 vi
圖次 vii
第壹章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的 5
第三節 論文架構 6
第四節 研究流程 7
第貳章 文獻回顧與探討 8
第一節 避險理論回顧 8
第二節 Copula 模型相關文獻回顧 12
第參章 研究方法與實證模型建構 14
第一節 Copula相關模型與估計 14
第二節 Dependence-switching模型建立與估計 23
第三節 傳統避險模型 26
第肆章 資料特徵與實證結果分析 28
第一節 資料特徵 28
第二節 實證結果分析 35
第伍章 結論與建議 38
第一節 結論 38
第二節 建議 39
第三節 研究限制與未來研究方向 40
參考文獻 41

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