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。

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.

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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