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題名 日內報酬率波動性之微波分析
其他題名 Wavelet Analysis of Intraday Return Volatility
作者 林信助
關鍵詞 日內波動性;微波分析
Intraday Periodicity;Wavelet Analysis
日期 2002
上傳時間 18-Apr-2007 16:36:10 (UTC+8)
Publisher 臺北市:國立政治大學國際貿易學系
摘要 在本研究計畫中,我們研究『台灣股票發行量加權指數期貨』日內報酬率波動性的行為。我們發現,該日內報酬率波動性有類似於文獻中所提到的呈現明顯 U-型變動的現象。我們採用 Andersen and Bollerslev (1997) 同時考慮『日內循環變動因子』及『日間條件變異因子』的理論架構,來探討報酬率日內波動性如何受到日內循環變動因子的影響。所不同的是,我們採用微波分析 ( Wavelet Analysis)來取代 Fourier Flexible Form (FFF),以過濾日內循環變動因子。這麼做的好處是微波分析的結果與時間上的頻率相互對應,具有明顯的經濟涵意。這是其他多項式配適 (polynomial fitting),包括 FFF,所不具備的性質。我們發現,微波分析的確可以有效地過濾掉大部分的日內循環變動因子。本研究有助於日內報酬率條件變異數模型參數的穩健與正確的估計,以及幫助我們對高頻率日內報酬率波動性的性質有更進一步的瞭解。
In this paper, we propose to examine the intraday volatility behavior of the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) futures returns. Observing that the TAIEX futures return volatility exhibits the same U-shape patters as those reported in the literature, we adopt Andersen and Bollerslev (1997) stylized model, which allows interaction between the intraday periodicity and the interday conditional heteroscedasticity. The main thrust of this project is to replace Andersen and Bollerslev`s Fourier Flexible Form (FFF) filtration of the intraday periodicity with a wavelet filtration. Since all wavelet analysis correspond to frequencies in calendar time, the proposed wavelet filtration allows us to filter out intraday periodicity more appropriately, and hence contributes to our better understanding of the intraday volatility behavior.
描述 核定金額:433200元
資料類型 report
dc.coverage.temporal 計畫年度:91 起迄日期:20020801~20030731en_US
dc.creator (作者) 林信助zh_TW
dc.date (日期) 2002en_US
dc.date.accessioned 18-Apr-2007 16:36:10 (UTC+8)en_US
dc.date.accessioned 8-Sep-2008 15:51:16 (UTC+8)-
dc.date.available 18-Apr-2007 16:36:10 (UTC+8)en_US
dc.date.available 8-Sep-2008 15:51:16 (UTC+8)-
dc.date.issued (上傳時間) 18-Apr-2007 16:36:10 (UTC+8)en_US
dc.identifier (Other Identifiers) 912415H004023.pdfen_US
dc.identifier.uri (URI) http://tair.lib.ntu.edu.tw:8000/123456789/3791en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/3791-
dc.description (描述) 核定金額:433200元en_US
dc.description.abstract (摘要) 在本研究計畫中,我們研究『台灣股票發行量加權指數期貨』日內報酬率波動性的行為。我們發現,該日內報酬率波動性有類似於文獻中所提到的呈現明顯 U-型變動的現象。我們採用 Andersen and Bollerslev (1997) 同時考慮『日內循環變動因子』及『日間條件變異因子』的理論架構,來探討報酬率日內波動性如何受到日內循環變動因子的影響。所不同的是,我們採用微波分析 ( Wavelet Analysis)來取代 Fourier Flexible Form (FFF),以過濾日內循環變動因子。這麼做的好處是微波分析的結果與時間上的頻率相互對應,具有明顯的經濟涵意。這是其他多項式配適 (polynomial fitting),包括 FFF,所不具備的性質。我們發現,微波分析的確可以有效地過濾掉大部分的日內循環變動因子。本研究有助於日內報酬率條件變異數模型參數的穩健與正確的估計,以及幫助我們對高頻率日內報酬率波動性的性質有更進一步的瞭解。-
dc.description.abstract (摘要) In this paper, we propose to examine the intraday volatility behavior of the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) futures returns. Observing that the TAIEX futures return volatility exhibits the same U-shape patters as those reported in the literature, we adopt Andersen and Bollerslev (1997) stylized model, which allows interaction between the intraday periodicity and the interday conditional heteroscedasticity. The main thrust of this project is to replace Andersen and Bollerslev`s Fourier Flexible Form (FFF) filtration of the intraday periodicity with a wavelet filtration. Since all wavelet analysis correspond to frequencies in calendar time, the proposed wavelet filtration allows us to filter out intraday periodicity more appropriately, and hence contributes to our better understanding of the intraday volatility behavior.-
dc.format applicaiton/pdfen_US
dc.format.extent bytesen_US
dc.format.extent 279583 bytesen_US
dc.format.extent 279583 bytes-
dc.format.extent 31723 bytes-
dc.format.mimetype application/pdfen_US
dc.format.mimetype application/pdfen_US
dc.format.mimetype application/pdf-
dc.format.mimetype text/plain-
dc.language zh-TWen_US
dc.language.iso zh-TWen_US
dc.publisher (Publisher) 臺北市:國立政治大學國際貿易學系en_US
dc.rights (Rights) 行政院國家科學委員會en_US
dc.subject (關鍵詞) 日內波動性;微波分析-
dc.subject (關鍵詞) Intraday Periodicity;Wavelet Analysis-
dc.title (題名) 日內報酬率波動性之微波分析zh_TW
dc.title.alternative (其他題名) Wavelet Analysis of Intraday Return Volatility-
dc.type (資料類型) reporten