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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/31760

    Title: 避險基金指數之風險值探討
    Other Titles: The value at risk analysis of hedge fund index
    Authors: 杜國賓;Tu, Kuo-pin
    Contributors: 淡江大學財務金融學系碩士在職專班
    邱建良;Chiu, Chien-liang
    Keywords: 風險值;風險矩陣;GARCH;馬可夫轉換模型;VAR;RiskMetrics;GARCH;Markov switching model
    Date: 2008
    Issue Date: 2010-01-11 01:13:22 (UTC+8)
    Abstract: 本文採用RiskMetrics模型與GARCH模型及馬可夫轉換模型估算避險基金指數之風險值,並進一步以RiskMetrics模型與GARCH模型及馬可夫轉換模型所估出之風險值進行比較,用以探討何種模型有較佳的預測能力及績效,使投資大眾於面臨風險時,能正確的評估與控管,以避免承擔超過預期的損失,實證結果如下:
    This paper investigates the Valu-at-Risk(VaR) of returns on hedge fund indexes using the RiskMetrics , the GARCH and the Markov Switching Models. Furthermore, we compared the Valu-at-Risk(VaR) between the RiskMetrics , the GARCH and the Markov Switching Models. The purpose is to find out which of three models has better prediction and performance for investors to evaluate and to take control in order to avoid unexpected lost while minimizing damage. The result of this study shows the following:
    1.The back-test shows that the RiskMetrics model, the GARCH model and the Markov Switching Model can estimate Valu-at-Risk(VaR) effectively which proves that the ability to control risk is at a good standard. Besides, the empirical results show Markov Switching Model can capture the distribution better than the others with a 99% confidence level under the back-test.
    2.According to the RMSE, the Markov Switching Model erforms better than either the GARCH model or the RiskMetrics model. We infer that the Markov Switching Model can well capture the distribution resulting from the adoption of the transformation mechanism of Markov chain. The Markov chain contains more relative information of time serial data than other models do.
    Appears in Collections:[財務金融學系暨研究所] 學位論文

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