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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/31712

    Title: 不動產投資信託指數之風險值探討
    Other Titles: The value at risk analysis of the real estate investment trust index
    Authors: 李世昌;Li, Shih-chang
    Contributors: 淡江大學財務金融學系碩士班
    邱建良;Chiu, Chien-liang
    Keywords: 風險值;風險矩陣;跳躍;厚尾;VAR;RiskMetrics;Jump;Heavy Tail
    Date: 2007
    Issue Date: 2010-01-11 01:10:04 (UTC+8)
    Abstract: 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。
    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.
    Appears in Collections:[財務金融學系暨研究所] 學位論文

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