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題名:以分量迴歸估計下方風險從事投資組合之研究
作者:吳千慧
作者(外文):Chien-Hui Wu
校院名稱:國立高雄第一科技大學
系所名稱:管理研究所
指導教授:周賢榮
菅瑞昌
學位類別:博士
出版日期:2011
主題關鍵詞:波動預測風險值期望短缺分量迴歸投資組合Forecasting VolatilityValue at RiskExpected ShortfallPortfolioQuantile Regression
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有句諺語說:不要將所有雞蛋放在同一個籃子裡。此意謂著風險分散的概念,一般而言,當投資愈多角化,則風險愈低,因此投資者可以藉由建構投資組合以降低風險。而本研究即以此觀念,首先以九個國家之12檔股價指數進行波動估計及下方風險之衡量, 進一步建構四組國際投資組合,衡量其相關係數與下方風險。
本研究利用等量加權移動平均法(equally weighted moving average,SMA) 、指數加權移動平均法(exponential weighted moving average,EWMA)、Harris and Shen(2004)提出的Bias-Corrected EWMA法及本研究首次使用之分量迴歸修正Bias-Corrected EWMA法進行波動估計、風險值及期望短缺之衡量。實證結果顯示,無論是12檔股價指數或是四組國際投資組合,分量迴歸修正Bias-Corrected EWMA法在波動預測上之誤差相較其他三個方法最小外,在風險值與期望短缺的部分,其通過檢定比率亦最高。由
此說明分量迴歸修正Bias-Corrected EWMA法相較其他三種方法而言,為一個最適之模型,且在風險預測能力上為最佳。
There is a saying, “Don’t put all your eggs in one basket.” This is an important thing to build a diversified portfolio with lower risk. This study applies equally weighted moving average (SMA), exponential weighted moving average (EWMA), Bias-Corrected EWMA and quantile regression approach to improve Bias-Corrected
EWMA model to estimate portfolio downside risk.
Using historical daily return data of twelve stock prices and four international portfolios, we test the performance of this modified approach to see if it can improve the
precise forecasting capability of downside risk. The empirical results, derived from the Kupiec (1995) tests, show that the proposed method indeed offers substantial
improvements on capturing dynamic return distributions. Moreover, the proposed quantile regression approach proves superior to ES prediction models relative to the other methods.
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