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題 名 | 緩長記憶波動模型之風險值計算--以臺灣加權股價指數為例=Value at Risk of Long Memory Volatility Model--Empirical Study of Taiwan Weighted Stock Index |
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作 者 | 張揖平; 洪明欽; 林彥豪; | 書刊名 | 東吳經濟商學學報 |
卷 期 | 43 2003.12[民92.12] |
頁 次 | 頁79-103 |
分類號 | 563.53 |
關鍵詞 | 緩長記憶; FIGARCH模型; 風險值; Value at risk; Long memory; FIGARCH model; |
語 文 | 中文(Chinese) |
中文摘要 | 風險值 (Value-at-Risk) 是目前廣為國際金融機構接受的一個風險管理機制,因此,風險值的精準估算已經成為金融機構免除危機的重要關鍵。在計算風險值時,若可以精準估計金融資產的波動度,應較能精確估算風險值,並有效控制金融市場的風險。由於許多實證研究發現金融資產報酬率之波動常具有緩長記憶 (long memory) 性,因此本研究將在資產報酬率之波動具有緩長記憶的假設下,探討Baillie、Bollerslev及Mikkelsen (1996) 提出的部分整合自迴歸條件異質變異數 (fractionally integrated generalized autoregressive conditional heteroskedasticity;簡稱FIGARCH) 模型之風險值計算法。最後,以台灣加權股價指數為標的資產,發現報酬率之波動度具有緩長記憶現象,且FIGARCH模型之風險值計算法的表現亦不錯。 |
英文摘要 | The volatility of financial time series plays an important role in many applications, especially in the field of risk management. More recently, many studies suggest that the long memory phenomenon do exist in the conditional volatility of financial data. The new class of fractionally integrated generalized autoregressive conditional heteroskedasticity (FIGARCH) model proposed by Baillie, Bollerslev, and Mikkeisen (1996) can allow for this long memory property in the conditional variance. In this paper, we use the FIGARCH model to compute Value at Risk (VaR) measure for daily stock returns. The empirical examples with stock returns show that FIGARCH model provides a good representation in VaR framework. |
本系統中英文摘要資訊取自各篇刊載內容。