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研究生:曾至苹
研究生(外文):Chih-Ping Tseng
論文名稱:Copula-basedGARCH模型於期貨避險之應用
論文名稱(外文):Futures Hedging with a Copula-based Multivariate GARCH Model
指導教授:王耀輝王耀輝引用關係徐之強徐之強引用關係
指導教授(外文):Chih-Chiang HsuChih-Chiang Hsu
學位類別:碩士
校院名稱:國立中央大學
系所名稱:財務金融研究所
學門:商業及管理學門
學類:財務金融學類
論文種類:學術論文
論文出版年:2006
畢業學年度:94
語文別:英文
論文頁數:37
相關次數:
  • 被引用被引用:2
  • 點閱點閱:228
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:2
過去的研究指出,固定的避險比率並不能達到最佳的避險效果,而建議最適避險比率應隨時間變動調整。本篇論文提出以Copula-based GARCH模型估計避險比率,並將其避險績效與傳統避險模型 (OLS),CCC GARCH模型,以及DCC GARCH模型相比較。Copula-based GARCH模型不受限於常態分配的假設,使資產邊際分配的選擇更有彈性,更能貼近其真實的分配。實證結果指出,不論是在樣本內或樣本外,以Copula-based GARCH模型估計避險比率所形成的投資組合變異數皆為最小,有最好的避險績效。
Many recent studies have demonstrated that using the constant hedge ratio obtained by the ordinary least squares method is inappropriate and hence different dynamic hedging strategies are suggested. In this paper we propose a new copula-based GARCH model to estimate the optimal hedge ratio, and compare its hedging effectiveness with different hedge models, including the constant conditional correlation GARCH model and the dynamic conditional correlation GARCH model. The advantage of the proposed model is that it allows for a more flexible distribution specification; Namely, marginal distributions or the dependence structure can be considered separately and simultaneously without the multivariate normality assumption. Hedging performance, in terms of variance reduction of portfolio returns, is evaluated for alternative models. Based on in-sample and out-of-sample comparisons, we find that the proposed model provides best hedging effectiveness.
Contents
1. Introduction 1

2. Methodology 6

2.1 Hedging Theories 6

2.2 Bivariate Constant Correlation GARCH 9

2.3 Dynamic Conditional Correlation Multivariate GARCH 10

3. Copula-based Multivariate GARCH 12

3.1 Copula 12

3.2 Estimation of Parameters 14

3.3 Empirical Methodology 15

4. Data 19

5. Empirical Analysis 20

5.1 Parameters Estimation 20

5.2 In-sample Comparison of Hedging Performance 21

5.3 Out-of-sample Comparison of Hedging Performance 23

6. Conclusion 25

Reference 26
Reference

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