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系統識別號 U0002-1607201218325100
中文論文名稱 動態價格跳躍與最小變異數避險組合的避險效益-以布蘭特原油與期貨價格為例
英文論文名稱 Dynamic Price Jump and Hedging Effectiveness for the Minimum Variance Hedging Portfolio:The Case of Brent Crude Oil and Futures Price
校院名稱 淡江大學
系所名稱(中) 管理科學學系碩士班
系所名稱(英) Master’s Program, Department of Management Sciences
學年度 100
學期 2
出版年 101
研究生中文姓名 陶怡珍
研究生英文姓名 Yi-Chen Tao
學號 699620935
學位類別 碩士
語文別 中文
口試日期 2012-06-22
論文頁數 30頁
口試委員 指導教授-莊忠柱
共同指導教授-王譯賢
委員-林忠機
委員-婁國仁
委員-江慧貞
中文關鍵字 CBP-GARCH模型  相關跳躍強度  移動視窗  最小變異數避險組合  期貨  避險績效 
英文關鍵字 CBP-GARCH Model  Correlated Jump Intensity  Futures  Hedging Effectiveness  Rolling Window 
學科別分類
中文摘要 原油價格波動受國際政經影響甚劇,針對原油價格波動進行避險已成為投資人的主要課題之一。由於原油價格與期貨價格可能皆會因稀少事件的發生而存著價格不連續現象。本研究先利用Chan(2003)提出的雙變量CBP-GARCH模型捕捉價格不連續的變動及現貨報酬與期貨報酬的共變異數關係。本研究以2010年至2011年英國布蘭特原油價格為主要研究對象,利用移動視窗(rolling window)法探討樣本外(out of sample)條件最小變異數避險組合之避險效益,比較未避險模型、雙變量GARCH(1,1)模型與雙變量CBP-GARCH(1,1)模型的條件最小變異數避險組合之避險效益。研究發現雙變量GARCH(1,1)模型與雙變量CBP-GARCH(1,1)模型存在著條件最小變異數避險組合之避險效益,且雙變量CBP-GARCH(1,1)模型較雙變量GARCH(1,1)模型的避險效益更好,因雙變量CBP-GARCH(1,1)模型能捕捉資產價格間動態跳躍與動態波動性,因而利用其條件最小變異數避險組合可得到較佳的避險效益,此結果可為投資人避險之參考。
英文摘要 The international political and economic effect the crude oil price volatility dramatically. One of the main topics is hedging for the crude oil price volatility of the investors. Crude oil spot and futures prices exist to discontinuously depend on rare events occurred. In order to capture the dynamic price jump and covariance between spot and futures returns, we use Chan(2003) to address bivariate the CBP-GARCH model. The discussions on this paper are using rolling window to investigate the out-of-sample hedging effectiveness for the minimum variance hedging portfolio.
The data period probes Brent oil spot and futures price using daily data for the time span 2010 to 2011. The empirical results show that the bivariate GARCH (1,1) model and the bivariate CBP-GARCH (1,1) model have hedging effectiveness for minimum variance hedging portfolio. Moreover, hedging effectiveness of the bivariate CBP-GARCH (1,1) model better than the bivariate GARCH (1,1) model. The bivariate CBP-GARCH (1,1) model is able to capture the dynamic jump between the asset price volatility and dynamic correlation, thus the bivariate CBP-GARCH (1,1) model obtain is the better hedging effectiveness for minimum variance hedging portfolio. The results can be reference for investors.
論文目次 目錄

目錄Ⅰ
表目錄 Ⅲ
圖目錄 Ⅳ
1. 緒論1
1.1 研究背景與動機 1
1.2 研究目的 4
1.3 研究貢獻 4
1.4 研究流程 5
2. 樣本與方法 7
2.1 研究樣本與資料來源 7
2.2 實證模型 8
2.3 最小變異數避險組合的避險效益衡量 11
3. 實證結果分析 14
3-1 基本敘述統計量分析 14
3.2 最小變異數避險組合的避險效益實證分析 16
4. 結論與建議 23
4.1 結論 23
4.2 建議 24
參考文獻 26
中文部分 26
英文部分 28




表目錄
表3-1 基本敘述統計量分析 15
表3-2 雙變量GARCH(1,1)模型與雙變量CBP-GARCH(1,1)模型的參數估計值 18
表3-3 在不同模型下的平均最小變異數避險比率 19
表3-4 不同模型避險效益的比較 20




圖目錄
圖1-1 研究流程 6
圖3-1 布蘭特原油現貨與期貨日價格時間走勢圖 14
圖3-2 布蘭特原油現貨與期貨日報酬時間走勢圖 14
圖3-3 移動視窗法架構 16
圖3-4 雙變量GARCH(1,1)模型的動態避險比率之時間走勢圖 17
圖3-5 雙變量CBP-GARCH(1,1)模型的動態避險比率之時間走勢圖 17
圖3-6 雙變量CBP-GARCH(1,1)模型最小變異數避險組合的波動性 21

參考文獻 參考文獻

一、中文部分
1.巫春洲、劉炳麟和楊奕農(2009),農產品期貨動態避險策略的評價,農業與經濟,第四十二期,頁39-62。

2.李命志(2006),雙變量跳躍模型之應用-以輕原油與熱燃油為例,行政院國家科學委員會專題研究計畫成果報告。

3.林師模、謝文耀和林晉勗(2009),原油進口之動態避險策略分析,農業與資源經濟,第六卷第二期,頁1-27。

4.邱哲修、洪瑞成、林卓民和徐明傑(2005),價格不連續下的最適避險策略-ARJI模型之應用,計量管理期刊,第一卷第一期,頁1-31。

5.胡緒寧、洪瑞成和李命志(2006),原油期貨的跳躍行為與跳躍相關性-CBP-GARCH模型之應用,東海管理評論,第八卷第一期,頁53-74。

6.徐清俊和張加民(2003),台灣股價指數期貨最適避險比率探討,遠東學報,第二十卷第三期,頁531-542。

7.高峰、洪瑞成、姜世杰和李命志(2005),價格跳躍下的最適避險策略-日經225指數現貨與期貨,華岡經濟論叢,第四卷第二期,頁65-90。

8.劉洪鈞、黃聖志和王怡文(2008),西德州與布蘭特原油避險策略,真理財經學報,第十八期,頁71-98。




二、英文部分
1.Bailey, W. (1989), “The Market for Japanese Stock Index Futures: Some Preliminary Evidence,” The Journal of Futures Markets, Vol. 9, No. 4, pp. 283-295.

2.Bollerslev, T. (1986), “Generalized Autoregressive Conditional Heteroskedasiticity,” Journal of Econometrics, Vol. 31, No. 3, pp. 307–327.

3.Chan, W. H. and J. M. Maheu (2002), “Conditional Jump Dynamics in Stock Market Returns,” Journal of Business and Economic Statistics, Vol. 20, No. 3, pp. 377-389.

4.Chan, W. H. (2003), “A Correlated Bivariate Poisson Jump Model for Foreign Exchange,” Empirical Economics, Vol. 28, No. 4, pp. 669-689.

5.Chan, W. H. (2004), “Conditional Correlatied Jump Dynamics in Foreign Exchange,” Economics Letters, Vol. 83, No. 1, pp. 23-28.

6.Chang, C. L., M. McAleer and R. Tansuchat (2011), “Crude Oil Hedging Strategies Using Dynamic Multivariate GARCH,” Energy Economics, Vol. 33, No. 5, pp. 912-923.

7.Ederington, L. H. (1979), “The Hedging Performance of the New Future Markets,” The Journal of Finance, Vol. 34, No. 1, pp. 157-170.

8.Engle, R. F. (1982), “Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflation,” Econometrica, Vol. 50, No. 4, pp. 987-1007.

9.Figlewski, S. (1984), “Hedging Performance and Basic in Index Futures,” The Journal of Finance, Vol. 39, No. 3, pp. 657-669.

10.Huang, B. Y., J. S. Chiou and P. S. Wu (2007), “Abnormal Profitability and Foreign Investment Based on the Investigation of Covered Interest Parity,” Physica A: Statistical Mechanics and its Applications, Vol. 384, No. 2, pp. 475-484.

11.Hung, J. C., Y. H. Wang, M. C. Chang, K. H. Shih and H. H. Kao (2011), “Minimum Variance Hedging with Bivariate Regime-Switching Model for WTI Crude Oil,” Energy, Vol. 36, No. 5, pp. 3050-3057.

12.Johnson, L. L. (1960), “The Theory of Hedging and Speculation in Commodity Futures,” Review of Economics Studies, Vol. 27, No. 3, pp. 139-151.

13.Junkus, J. and C. F. Lee (1985), “Use of Three Stock Index Futures in Hedging Decisions,” The Journal of Futures Markets, Vol. 5, No. 2, pp. 201-222.

14.Lien, D. (2005), “A Note on the Superiority of the OLS Hedge Ratio,” Journal of Futures Markets, Vol. 25, No. 11, pp. 1121-1126.

15.Park, T. H. and L. N. Switzer (1995), “Bivariate GARCH Estimation of the Optimal Hedge Ratios for Stock Index Futures: A Note,”Journal of Futures Markets, Vol. 15, No. 1, pp. 61-67.

16.Yun, W. C. and H. J. Kim (2010), “Hedging Strategy for Crude Oil Trading and the Factors Influencing Hedging Effectiveness,” Energy Policy, Vol. 38, No. 5, pp. 2404-2408.

17.Zanotti, G., G. Gabbi and M. Geranio (2010), “Hedging with Futures: Efficacy of GARCH Correlation Model to European Electricity Markets,” Journal of International Financial Markets, Institutions and Money, Vol. 20, No. 2, pp. 135-148.
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