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系統識別號 U0002-2806201302303500
中文論文名稱 金磚五國之期貨避險績效─應用Copula-based GJR-GARCH模型
英文論文名稱 The Hedging Performance for BRICS Futures─A Copula-based GJR-GARCH Model
校院名稱 淡江大學
系所名稱(中) 財務金融學系碩士班
系所名稱(英) Department of Banking and Finance
學年度 101
學期 2
出版年 102
研究生中文姓名 柯星妤
研究生英文姓名 Hsing-Yu Ke
學號 600530165
學位類別 碩士
語文別 中文
口試日期 2013-06-23
論文頁數 65頁
口試委員 指導教授-李沃牆
委員-張揖平
委員-陳達新
委員-李沃牆
中文關鍵字 Copula  避險績效  金磚五國  GJR-GARCH模型 
英文關鍵字 Copula  Hedging Performance  BRICS  GJR-GARCH Model 
學科別分類
中文摘要 金磚五國(BRICS)在近幾年憑藉天然資源、勞動人力等優勢,已成全球經濟增長的重要源頭。與五國市場相關的衍生性商品受到投資人的青睞,其中,金磚五國的交易所推出的股價指數期貨,提供了股票持有人一個良好的避險工具,如何進行有效的避險,為本文研究探討的重點。
本文現貨與期貨的研究資料使用巴西IBOVESPA指數、俄羅斯RTS指數、印度S&P CNX NIFTY指數、中國CSI300指數以及南非FTSE/JSE Shareholder Weighted Top40 指數。本文主要採用Copula-based GJR-GARCH 模型估計現貨與期貨報酬的最小變異避險比率,並和傳統避險模型、固定條件相關係數之CCC-GJR-GARCH模型以及動態條件相關係數之DCC-GJR-GARCH模型進行各個模型的避險績效之比較,找出最適的避險比率和最佳的模型。實證結果發現,除了中國CSI300指數外,其它四國在樣本內及樣本外的績效評估檢定下,均以Copula為基礎的GJR-GARCH模型的避險績效較傳統OLS模型佳。
英文摘要 In recent years, BRICS have become vital sources of growth in the global economy by the advantage of the natural resources and labor force. Derivatives products of BRICS market are favored by investors, including stock index futures are listed on the BRICS Exchanges, provide a good hedging tool for stock holders. The focus of this paper is how to conduct an effective hedging.
The main data for empirical study consists of the IBOVESPA index, the RTS index, the S & P CNX NIFTY index, China Securities Index 300 (CSI 300) index and FTSE/JSE Shareholder Weighted Top40 index spots and futures. This paper uses copula-based GJR-GARCH models for the estimation of the optimal hedge ratio and compares their effectiveness with that of other hedging models, including the conventional static, the constant conditional correlation (CCC) GJR-GARCH, and the dynamic conditional correlation (DCC) GJR-GARCH models. The empirical results show that in both the in-sample and out-of-sample tests, the copula-based GJR-GARCH models perform more effectively than OLS model, except for CSI 300 index.
論文目次 目 次
謝辭I
表次V
圖次VI
第一章 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 研究架構與流程6
第二章 理論與文獻回顧8
第一節 股價指數期貨契約規格介紹8
第二節 避險理論10
第三節 避險模型相關文獻13
第三章 研究方法20
第一節 Copula介紹20
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32
第一節 資料來源與型態32
第二節 實證方法與過程33
第三節 實證結果分析36
第四節 績效評估 51
第五章 結論與建議55
第一節 結論55
第二節 建議57
參考文獻58
一、中文文獻58
二、英文文獻59
附錄63

表 次
表1 BRICS的股價指數期貨契約規格9
表2 避險理論比較12
表3 避險模型近年相關文獻統整19
表4 資料統整33
表5 敘述統計量37
表6 單根檢定和結構轉變檢定39
表7 CCC-GJR-GARCH模型之參數估計42
表8 DCC-GJR-GARCH模型之動態相關係數參數估計43
表9 Copula-based GJR-GARCH模型之參數估計45
表10 不同避險模型之樣本內績效評估52
表11 不同避險模型之樣本外績效評估(避險期間為10天)53
表12 不同避險模型之樣本外績效評估(避險期間為20天)54

圖 次
圖1 研究架構圖7
圖2 移動視窗方法34
圖3 實證方法流程35
圖4 巴西現貨期貨價格和報酬率序列40
圖5 巴西現貨(左)和期貨(右)報酬率之常態分配檢定40
附圖1 俄羅斯現貨期貨的價格和報酬率序列63
附圖2 印度現貨期貨的價格和報酬率序列63
附圖3 中國現貨期貨的價格和報酬率序列64
附圖4 南非現貨期貨的價格和報酬率序列64
附圖5 俄羅斯現貨(左)和期貨(右)常態分配檢定65
附圖6 印度現貨(左)和期貨(右)常態分配檢定65
附圖7 中國現貨(左)和期貨(右)常態分配檢定65
附圖8 南非現貨(左)和期貨(右)常態分配檢定65

參考文獻 參考文獻
一、中文文獻
1.巫春洲、劉炳麟、楊奕農 (2009),「農產品期貨動態避險策略的評價」,農業與經濟,第42卷,頁 39-62。
2.李沃牆、李莠苓(2011),應用Copula-GJR-GARCH模型於黃金與白銀期貨之避險,台灣期貨與衍生性商品學刊,第12期,頁28-65。
3.李亦屏(2004),黃金期貨之避險分析,中原大學企業管理學系碩士論文。
4.何其祥、張晗、鄭明(2009),「包含股指期貨的投資組合之風險研究-copula方法在金融風險管理中的應用」,數理統計與管理(中國),第28卷,第 1期,頁159-166。
5.林俊良、劉子康(2009),「自由度具解析解之動態t-copula在商品期貨風險管理的應用」,台灣期貨與衍生性商品學刊,第9期,頁1-30。
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8.徐偉書 (2008),動態避險下基差與負面衝擊的不對稱效果,淡江大學財務金融學系碩士論文。
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二、英文文獻
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20.Patton, A. J., (2006), “Modelling Asymmetric Exchange Rate Dependence,” International Economic Review, Vol. 47, pp. 527-556.
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22.Rockinger, M. and E. Jondeau, (2001). “The Copula-GARCH Model of Conditional Dependencies: An International Stock Market Application,” Journal of International Money and Finance, Vol. 25, No. 3, pp. 827-853.
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25.Weia, Y., Y. Wang, and D. Huang, (2011), “A Copula-Multifractal Volatility Hedging Model for CSI 300 Index Futures,” Physica A: Statistical Mechanics and its Applications, Vol. 390, pp. 4260-4272.
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