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中文論文名稱 基差與變幅波動之資訊內涵對於避險績效之影響
英文論文名稱 The Information Contents of Basis and Range Volatility on Hedging Performance
校院名稱 淡江大學
系所名稱(中) 財務金融學系碩士在職專班
系所名稱(英) Department of Banking and Finance
學年度 98
學期 1
出版年 99
研究生中文姓名 鄭佩芳
研究生英文姓名 Pei-Fang Cheng
學號 796530201
學位類別 碩士
語文別 中文
口試日期 2010-01-09
論文頁數 77頁
口試委員 指導教授-邱建良
指導教授-洪瑞成
委員-林卓民
委員-邱建良
委員-李命志
委員-洪瑞成
委員-涂登才
中文關鍵字 基差  變幅波動估計量  避險績效  SPA檢定  CCC-GARCH 
英文關鍵字 Basis  Hedging Performance  CCC-GARCH  Range-Based Estimator  SPA Test 
學科別分類
中文摘要 本文以美國、英國、台灣等國之股價指數與指數期貨為主要研究對象,研究期間取自2001年1月1日至2008年12月31日止,採用CCC- GARCH避險模型,探討加入基差與變幅波動對避險績效的影響,實證結果發現在基差變數的比較上,CCC- GARCH避險模型加入不對稱基差的避險績效為所有修正模型中最高,比加入對稱基差的避險模型與CCC-GARCH避險模型好;在變幅波動變數的比較上,CCC- GARCH避險模型加入以Parkinson (1980) 或 Garman and Klass (1980) 或Rogers and Satchell (1991) 所估算波動率的避險模型並無一致的結果;在基差和變幅波動變數的比較上,CCC- GARCH避險模型加入不對稱基差的避險績效最佳,而不考慮變數的CCC-GARCH模型避險績效最差。最後,以優勢預測能力檢定(Superior Predictive Ability Test;SPA)模型檢定,評估模型預測績效優劣,CCC- GARCH避險模型加入不對稱基差,可提供投資人決定最適避險比率及衡量避險績效之參考。
英文摘要 This thesis takes S&P 500, Dow Jones, FTSE 100 and Taiwan stock indexs as the research object. The sample period covers from 1/1/2001 to 31/12/2008.With the use of the constant conditional correlation GARCH framework, and incorporating the decomposed basis and range volatility into the model to estimate hedging performances. The empirical results indicate that asymmetry effect model provides better hedging performance than the symmetric effect model and CCC-GARCH model. The hedging performance of CCC-GARCH also improves significantly by the inclusion of extreme-value volatility. The volatility estimates, based on the Parkinson estimator, provide better forecasts than those based on the Garman and Klass or Rogers Satchell estimator. Furthermore, use the SPA Test to determine which model has better accuracy in predicting the hedging performance of the actual market. In conclusion, the result indicates that asymmetric basis effect model has the best hedging performances. Asymmetric basis effect model provides investors to decide the hedging ratio of futures and to measure hedging performance.
論文目次 目錄
摘要 I
目錄 III
表目錄 V
圖目錄 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 3
第三節 研究限制 4
第四節 研究架構 5
第五節 研究流程 6
第二章 文獻回顧 7
第一節 避險理論之探討 7
第二節 國外文獻回顧 12
第三節 國內文獻回顧 15
第三章 研究方法 21
第一節 資料檢驗 21
第二節 ARCH效果檢定 24
第三節 GARCH模型 27
第四節 避險績效之衡量 35
第五節 避險績效檢定 37
第四章 實證結果 39
第一節 資料來源與處理 39
第二節 基本統計量分析 41
第三節 單根檢定 45
第四節 ARCH 效果檢定 48
第五節 各模型之估計 49
第六節 樣本內避險實證結果 60
第七節 樣本外避險實證結果 65
第五章 結論 70
參考文獻 72
一、 國外文獻 72
二、 國內文獻 76


表目錄
【表4-2-1】各國股價指數現貨與期貨報酬率之基本統計量 42
【表4-3-1】股價指數現貨時間序列之單根檢定(水準項) 46
【表4-3-2】股價指數期貨時間序列之單根檢定(水準項) 46
【表4-3-3】股價指數現貨時間序列之單根檢定(差分項) 47
【表4-3-4】股價指數期貨時間序列之單根檢定(差分項) 47
【表4-4-1】各國指數現貨與期貨ARCH效果檢定 48
【表4-5-1】樣本內CCC-GARCH模型各項參數估計結果 50
【表4-5-2】樣本內CCC-GARCH_BA模型各項參數估計結果 53
【表4-5-3】樣本內CCC-GARCH_ABA模型各項參數估計結果 54
【表4-5-4】樣本內CCC-GARCH_PK模型各項參數估計結果 57
【表4-5-5】樣本內CCC-GARCH_GK模型各項參數估計結果 58
【表4-5-6】樣本內CCC-GARCH_RS模型各項參數估計結果 59
【表4-6-1】樣本內不同避險模型避險績效之比較 63
【表4-6-2】樣本內不同避險模型避險績效之比較(以CCC-GARCH為基準) 64
【表4-7-1】樣本外不同避險模型避險績效之比較 68
【表4-7-2】樣本外不同避險模型避險績效之比較(以CCC-GARCH為基準) 69




圖目錄
【圖4-2-1】S&P500股價指數現貨與期貨原始時間序列圖 43
【圖4-2-2】道瓊工業股價指數現貨與期貨原始時間序列圖 43
【圖4-2-3】倫敦金融時報100指數現貨與期貨原始時間序列圖 43
【圖4-2-4】台灣加權股價指數現貨與期貨原始時間序列圖 44
【圖4-7-1】移動視窗方法 65

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Bollerslev, T. (1990). Modeling the Coherence in Short-run Nominal Exchange Rates Generalized ARCH. Review of Economics and Statistics, 70, 498-505.
Benet, B. A. (1992). Hedge Period Length and Ex-ante Futures Hedging Effectiveness: The Case of Foreign-exchange Risk Cross Hedges. Journal of Futures Markets, 12, 163-175.
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Diebold, F. X. and Mariano, R. S. (1995). Comparing Predictive Accuracy. Journal Business Economic Statistic, 13, 253-263.
Edrington, L. H. (1979). The Hedging Performance of the New Futures Markets. Journal of Finance, 34,157-170.
Engle, R. F. (1982). Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica, 50(4), 987-1007.
Engle, R. F. and Yoo, B. S., (1987). Forecasting and Testing in Co-integrated Systems, Journal of Econometrics, 35, 143-328.
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Garman, M. B. and Klass, M. J. (1980). On the Estimation of Security Price Volatilities from Historical Data. Journal of Business, 53, 67-78.
Hsin, C. W., Kuo, J. and Lee, C. F. (1994). A New Measure to Compare the Hedging Effectiveness of Foreign Currency Futures Versus Options. Journal of Futures Markets, 14(6), 685-707.
Hansen, P. R. (2005). A Test for Superior Predictive Ability. Journal of Business & Economic Statistics, 23, 365-380.
Hansen, P. R. and Lunde, A. (2005). A Forecast Comparison of Volatility Models: Does Anything Beat a GARCH(1,1)?. Journal of Applied Econometrics, 20(7), 873-889.
Hsu Ku, Y., Chen, H. and Chen, K. (2007). On the Application of the Dynamic Conditional Correlation Model in Estimating Optimal Time-varying Hedge Ratios. Applied Economics Letters, 14,503-509.
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Parkinson, M. (1980). The Extreme Value Method for Estimating the Variance of the Rate of Return. Journal of Business, 53, 61-65.
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二、國內文獻
林東炘 (2007),「基差不對稱、交易行為對避險績效的影響」,中正大學財務金融所碩士論文。
凃惠娟 (2007),「以DCC模型探討股價指數期貨與現貨」,大葉大學管理研究所博士論文。
邱建良、魏志良、吳佩珊、邱哲修 (2004),「TAIFEX與MSCI臺股指數期貨與現貨直接避險策略之研究」,商管科技季刊,第5卷第2期,頁169-184。
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。
張瓊嬌、古永嘉 (2003),「臺灣股價指數期貨與現貨市場資訊傳遞及價格波動性之研究-雙元EGARCH-X模式與介入模式之應用」,管理評論,第23卷第2期,頁53-74。
劉炳麟 (2008),「多變量變幅波動模型的理論與應用」,交通大學財務金融研究所博士論文。
賴昌作 (2000),「股價指數期貨之避險比率與避險效益」,台灣科技大學資訊管理系碩士論文。
盧惠盈 (2002),「期貨避險比率及績效分析-以外匯期貨為例」,中正大學財務金融研究所碩士論文。
魏志良 (2002),「國際股價指數期貨與現貨直接避險策略之研究」,淡江大學財務金融學系碩士論文。
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