市場效率性長久以來一直是全球財務學者所感興趣的議題，也是各國政府極力想達成的目標。本研究藉由美國國家不動產投資信託協會所編製之美國不動投資信託（Real Estate Investment Trusts）指數，探討2001年01月03日至2008年10月31日間，其四種REITs指數在傳統OLS模型、GARCH模型與GARCH-Level模型下，市場報酬與波動性之星期效應（Day-of-the-week Effect）變化為何？是否存在所謂的週一效應（Monday Effect）或稱週末效應（Weekend Effect）。此外本篇研究加入次級房貸風暴之虛擬變數以探討次級房貸風暴對於REITs的報酬與波動性是否存在影響性。本篇研究也特別考慮利用VaR模型進行風險值的預測，以比較最適模型在應用上是否有其一致性。
	實證結果顯示，使用GARCH-Level模型對於市場報酬與市場波動度的解釋能力優於傳統OLS模型與GARCH模型，解釋能力相較於其他模型有明顯的提升。在星期效應的實證上發現與故去文獻研究有一致性。在波動度星期效應的研究上發現，All REITs指數與Equity REITs指數在週二、週三與週四的波動度有顯著的異常現象。Hybrid REITs指數在週一與週五的波動度有異常性。Mortgage REITs指數則是五天波動度皆有異常性。形成此一現象的因素可能來自受到次級房貸風暴的影響而有此一結果。此外次級房貸風暴對於REITs的報酬並無顯著的影響，但對於波動度卻有明顯的影響性。另外在VaR的檢驗上，GARCH-Level模型在考慮波動度後，其風險預測能力相較於傳統GARCH模型來的準確，顯示GARCH-Level模型在實務的應用上依然為最適模型。

Market efficiency has been an interesting financial issue. We examine the day-of-the-week effect of return and volatility for American REITs (Real Estate Investment Trusts) index data from January 3, 2001 to October 31, 2008 by traditional OLS model, GARCH model, GARCH-Level model and VaR model. And then we also examine that the Subprime Mortgage event affects the return and volatility of REITs.
Our results show that using GARCH-Level model has better explanation of market return and market volatility than OLS model and GARCH model, that is, all REITs index, equity REITs index and hybrid REITs index have abnormal return on Monday, Wednesday and Friday, but mortgage REITs index have on Monday and Tuesday, and this finding is not only consistent with other studies but also the explanation is improved than other models. 
The results also indicate that Subprime Mortgage Crisis has significant effect to volatility of REITs. Besides, the examination of VaR model shows that the prediction ability of risk of GARCH-Level model is more accurate than other models, which means GARCH-Level model still is the optimal model for practical application.

目錄
中文摘要	I
英文摘要	II
目錄	III
表目錄	IV
圖目錄	V
第ㄧ章 緒論	1
第ㄧ節 研究背景與動機	1
第二節 研究目的	3
第三節 研究流程	4
第二章 文獻探討	5
第一節 星期效應之理論假說	5
第二節 不動產投資信託之星期效應相關實證文獻	13
第三節 其它金融商品之星期效應相關實證文獻	15
第三章 研究方法	21
第ㄧ節 樣本資料與資料處理	21
第二節 單根檢定	23
第三節 一般迴歸分析	28
第四節 GARCH 模型	32
第五節 GARCH-Level Effec模型	41
第六節 VaR（Value-at-Risk）模型	51
第四章 實證結果分析	56
第ㄧ節 基本統計量分析	56
第二節 單根檢定	59
第三節 OLS模型、GARCH模型與GARCH-Level模型比較分析	61
第五節 VaR模型分析	87
第五章 結論與建議	95
附錄	97
第ㄧ節 不動產投資信託之介紹	97
第二節 美國不動產投資信託發展現況	101
第三節 次級房貸風暴簡述	104
參考文獻	107
表目錄
表 4-1-1  基本統計量分析	57
表 4-2-1  四種REITs指數原始水準單根檢定	60
表 4-2-2  四種REITs指數一階差分水準單根檢定	60
表 4-3-1  All REITs星期效應之五種模型比較	64
表 4-3-2  Equity REITs星期效應之五種模型比較	67
表 4-3-3  Mortgage REITs星期效應之五種模型比較	71
表 4-3-4  Hybrid REITs星期效應之五種模型比較	74
表 4-3-5  All REITs週一與其他天差異之比較	77
表 4-3-6  Equity REITs週一與其他天差異之比較	80
表 4-3-7  Mortgage REITs週一與其他天差異之比較	83
表 4-3-8  Hybrid REITs週一與其他天差異之比較	86
表 4-4-1  不同模型之多部位（Long Positions）預測績效比較	93
表 4-4-2  不同模型之空部位（Short Positions）預測績效比較	94
表 附錄   REITs 類型特性比較分析表	100
圖目錄
圖 4-1-1  All REITs 指數價格與報酬率走勢圖	58
圖 4-1-2  Equity REITs 指數價格與報酬率走勢圖	58
圖 4-1-3  Mortgage REITs 指數價格與報酬率走勢圖	58
圖 4-1-4  Hybrid REITs 指數價格與報酬率走勢圖	58
圖 4-4-1  樣本外一天風險值預測之移動視窗法	87

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