本論文將融合日收益及實現波動率測度 (realized volatility measure) 的 Real-
ized GARCH 模型應用於風險值及預期短缺的估計。 除了直接使用一般模型的常態
假設去計算這些風險測度, 本文也採用 McNeil and Frey(2000) 的概念將極值理論
(extreme value theory) 與之融合, 獲得厚尾分配下的風險測度估計, 並比較不同分
配假設下之結果。 實證上, 以標準普爾100(S&P 100) 指數的高頻資料作為研究對象,
並加入一般基於日收益的 GARCH 模型以及單純使用實現波動率測度的 MEM 模型
作風險值預測能力之比較。 實證結果顯示, Realized GARCH 模型的風險值預測表現
優於其他的波動率模型。

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