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系統識別號 U0002-0506200717333400
中文論文名稱 不動產投資信託指數之風險值探討
英文論文名稱 The Value at Risk Analysis of the Real Estate Investment Trust Index
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 95
學期 2
出版年 96
研究生中文姓名 李世昌
研究生英文姓名 Shih-Chang Li
學號 694490078
學位類別 碩士
語文別 中文
口試日期 2007-05-20
論文頁數 75頁
口試委員 指導教授-邱建良
共同指導教授-陳玉瓏
委員-王凱立
委員-李命志
委員-邱建良
委員-鄭婉秀
中文關鍵字 風險值  風險矩陣  跳躍  厚尾 
英文關鍵字 VaR  RiskMetrics  Jump  Heavy Tail 
學科別分類 學科別社會科學商學
中文摘要 在資本市場中,除了傳統的股票、匯率、債券等資產外,近年來,不動產投資信託掀起了一股投資熱潮,已成為市場參與者關注的投資標的。然而,當市場參與者將資金投入REITs商品時,除了關注其所預期之報酬以外,應當對其持有部位所面臨的風險加以控管。
本文利用風險值的概念,探討當市場呈現空頭狀態時,市場參與者對於不動產投資信託指數商品所應承受的最大損失報酬率。在模型上使用J.P. Morgan(1996)所提出的RiskMetrics模型及Chan and Maheu(2002)所提出的ARJI模型估算風險值。此外,為了解決傳統模型常態分配假設所無法捕捉到的厚尾現象,亦可修正一般文獻多使用t分配來解釋厚尾問題所產生的低峰態缺點,本文假設誤差項服從Politis(2004)所提出的厚尾分配,將此分配導入RiskMetrics模型及ARJI模型做修正。實證結果顯示,當模型導入厚尾分配的假設,確實能有效改善風險值模型預測能力,而以資金使用效率角度來說明,則以ARJI-HT模型優於其他模型;此外,不論模型是否導入厚尾分配假設,由於ARJI模型可捕捉到波動群聚的效果外,還加上了跳動的變異,因此在模型預測能力以及資金使用效率方面優於J.P. Morgan所提出的RiskMetrics模型。
英文摘要 Beside the traditional assets, like stocks, exchange rate, and bonds in the capital market, there is the Real Estate Investment Trusts, becomes the most popular investment underlying. However, when the market investors put their capital into Real Estate Investment Trusts, they have to manage the risk they meet, beside they concern about the expected return.
This paper adopts the conception of the Value at Risk to investigate the extreme loss the investors sustain when they put their capital into the Real Estate Investment Trusts in the bear market. It takes the RiskMetrics model proposed by J.P. Morgan(1996)and the ARJI model proposed by Chan and Maheu(2002)in this paper. In order to solve some problem that the traditional model with normal distribution assumption could not capture the heavy tail phenomenon, and modify the shortcoming of general reference with t distribution assumption, it use the heavy tail distribution assumption proposed by Politis(2004), and apply the heavy tail distribution to RiskMetrics model and ARJI model. The result shows that it could improve the ability to predict the Value at Risk, when it apply the heavy tail distribution assumption to the model. From the efficiency of capital usage point, the ARJI-HT model is better than the others. Furthermore, no matter the model with the heavy tail distribution assumption, the ARJI model is better than RiskMetrics model, because it could capture the volatility clustering and jump factor.
論文目次 目錄
第一章  緒論
第一節   研究動機 .....................................1
第二節 研究目的 .....................................4
第三節 研究架構 .....................................5
第四節 研究流程 .....................................6
第二章 理論基礎與文獻回顧
第一節 不動產投資信託 ...............................7
第二節 風險值的意義及概念 ..........................10
第三節 常見之風險值模型及其估算方法 ................13
第四節 探討風險值估計方法及特性之相關文獻 ..........20
第三章 研究方法
第一節 研究程序 ....................................32
第二節 資料檢驗 ....................................33
第三節 風險值的估算及使用模型 ......................38
第四節 風險值預測模型 ..............................40
第五節 風險值的評價方式與預測績效 ..................46
第四章 實證結果分析
第一節 資料來源與處理 ..............................48
第二節 基本統計量特性分析 ..........................49
第三節 單根檢定與ARCH效果檢定 ......................51
第四節 RiskMetrics模型與ARJI模型估計 ...............55
第五節 風險值估算與預測績效 ........................61
第五章 結論 ..........................................71
參考文獻 ...............................................73
表目錄
【表2.1.1】不動產投資信託投資內涵比較表 .................8
【表2.1.2】不動產投資信託組織方式比較表 .................9
【表2.3.1】四種估算風險值方法特性 ......................19
【表3.2.1】國外相關文獻整理 ............................23
【表3.2.2】國內相關文獻整理 ............................30
【表3.5.1】Kupiec(1995)檢定法之臨界值 ................46
【表4.2.1】REITs指數報酬率基本統計特性 .................50
【表4.3.1】ADF及PP單根檢定(水準項)....................53
【表4.3.2】ADF及PP單根檢定(差分項)....................53
【表4.3.3】LM檢定與Q檢定 ...............................55
【表4.4.1】RiskMetrics模型參數估計結果 .................57
【表4.4.2】RiskMetrics-HT模型參數估計結果 ..............57
【表4.4.3】ARJI模型參數估計結果 ........................58
【表4.4.4】厚尾ARJI模型參數估計結果 ....................59
【表4.5.1】99%信賴水準下各模型回溯測試之失誤率檢定與均方誤差 .......62
【表4.5.2】95%信賴水準下各模型回溯測試之失誤率檢定與均方誤差 .......63
【表4.5.3】90%信賴水準下各模型回溯測試之失誤率檢定與均方誤差 .......64
圖目錄
【圖1.3.1】研究流程圖 ...................................6
【圖2.1.1】不動產投資信託分類架構圖 .....................8
【圖2.2.1】風險值示意圖 ................................12
【圖4.2.1】各國REITs指數原始時間序列圖 .................50
【圖4.2.2】各國REITs指數報酬率序列圖 ...................51
【圖4.4.1】估計期間(1000天)與預測風險值(1天)之移動視窗方法 .........60
【圖4.5.1】US-REITs指數估計風險值與實際報酬的比較 ......65
【圖4.5.2】NL-REITs指數估計風險值與實際報酬的比較 ......66
【圖4.5.3】AU-REITs指數估計風險值與實際報酬的比較 ......67
【圖4.5.4】WD-REITs指數估計風險值與實際報酬的比較 ......68
【圖4.5.5】標準常態分配與 機率密度函數分配圖比較 .......70
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