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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 洪茂蔚(Mao-Wei Hung) | |
dc.contributor.author | Shun-Pi Yang | en |
dc.contributor.author | 楊舜弼 | zh_TW |
dc.date.accessioned | 2021-06-15T13:27:13Z | - |
dc.date.available | 2026-01-01 | |
dc.date.copyright | 2016-03-08 | |
dc.date.issued | 2016 | |
dc.date.submitted | 2016-02-17 | |
dc.identifier.citation | 1. Akaike H., 1974. “A New Look at the Statistical Model Identification,” IEEE Transactions on Automatic Control.19: 716-723.
2. Andersen, T. and T. Bollerslev., 1997. “Heterogeneous Information Arrivals and Return Volatility Dynamics:Uncovering the Long Run in High Frequency Returns,” Journal of Finance. 52: 975-1005. 3. Bollerslev, T., 1986. “Generalized Autoregressive Conditional Heteroskedasticity,” Journal of Econometrics.31: 34-105. 4. Bollerslev, T., 1987. “A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return,” The Review of Economics and Stastics,69(3),542-547. 5. Chien-fu Jeff Lin., . “Modeling Volatility in Financial Data:ARCH related Model.”. 6. Clifford M.Hurvich and Chih-Ling Tsai., 1989.“Regression and Time Series Model Selection in Small Samples.”Biometrika,76(2),297-307. 7. Daniel, B.Nelson.,1991. “Conditional Heteroskedasticity in Asset Returns:A New Approach.” Econometrica. 59: 347-370. 8. Engle, R. F.,1982. “Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of UK Inflation,” Econometrica. 50: 987-1008. 9. Fama, E. F., 1965.,”The Behavior of Stock-Market Prices,” Journal of Business. 38: 34-105. 10. Felmingham,B. S. & P.Mansfield(1997). “Rationality and the Risk premium on the Australian Dollar,” International Economic Journal, 11(3),47-59. 11. Ghulam Ali.,2013. “EGARCH, GJR-GARCH, TGARCH, AVGARCH, NGARCH, IGARCH and APARCH Models for Pathogens at Marine Recreational Sites,” Journal of Statistical and Econometric Methods, vol. 2, no.3, 2013, 57-73. 12. Kenneth, R.French and G.William Schwert and Robert,F.Stambaugh ., 1987. “Expected Returns and Volatility.” Journal of Finance Economics. 19: 3-29. 13. Markowitz,H. M.,1952.”Portfolio Selection” The Journal of Finance7:71-91. 14. Ngama, Y. L. (1994). “Testing for the Presence of Time-Varying Risk Premium Using a Mean-Conditional-Variance Optimization Model” Oxford Bulletin of Economics & Statistics,56(2),189-208. 15. Park, J. (2001), “Information Flows between Non-deliverable Forward(NDF) and Spot Markets: Evidence from Korean Currency,” Pacific-Basin Finance Journal,9(4),363-77. 16. Rob Reider., 2009. “Volatility Forecasting I: GARCH Models.”. 17. Schwartz, G. E., 1978. “Estimating the Dimension of a Model.” Annals of Statistics. 6(2): 461-464. 18. 潘浙楠、席嘉澤、陳曉倩,2009,「自我相關殘差管制圖模型選取之研究」,品質學報。16卷第4期,273-282 19. 廖偉真、雷立芬,2010,「不同樣本頻率之股市波動性估計-GARCH、TGARCH與EGARCH之比較」,台灣銀行季刊。61卷第4期,294-307 20. 蔡明翰,2009,「應用ARIMA與GARCH模式於台灣運輸產業股價之預測」,國立交通大學運輸與物流管理學系碩士論文 21. 陳盈之,2002,「市場訊息變動對外匯波動之不對稱影響與其反轉特性:選擇權市場的證據」,國立政治大學財務管理學系碩士論文 22. 邱建良、吳佩珊、邱哲修, 2004。「亞洲外匯市場行為之探討-不對稱門檻GARCH模型之應用」,台灣管理學刊。4卷第2期,187-202 23. 林芸生,2010,「從假設檢定的觀點探討ARMA模型的參數配適」,國立政治大學統計學系碩士論文 24. 林育秀,2008,「新台幣對人民幣與美元的匯率波動對台灣出口的影響」,朝陽科技大學財務金融學系碩士論文 | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/51196 | - |
dc.description.abstract | 本研究之主要目的係探討在不同波動模型下,樣本頻率對於外匯波動性估計的影響。同時採用了GARCH、EGARCH以及TGARCH模型進行比較,除了檢驗樣本配適度檢定以外,並會進行樣本外預測績效。希冀透過多元的波動模型檢驗,找出適合捕捉外匯波動性的樣本頻率特性。
實證結果顯示,觀察頻率越高越能捕捉外匯的波動性。且不同模型的比較,可以發現EGARCH以及TGARCH的模型配適度普遍比GARCH佳。 | zh_TW |
dc.description.abstract | The main purpose of the thesis is comparing the results of volatility estimation under different models and observation frequencies. These models include GARCH, EGARCH and TGARCH models. Both testing for the goodness-of-fit and sample forecasting will be conducting in the thesis.
Empirical results show that higher observation frequency stands for higher goodness-of-fit when estimating in foreign exchange volatility. Another implication from the research is that both EGARCH and TGARCH model generally position higher goodness-of-fit than GARCH model. | en |
dc.description.provenance | Made available in DSpace on 2021-06-15T13:27:13Z (GMT). No. of bitstreams: 1 ntu-105-R02724075-1.pdf: 813847 bytes, checksum: 3a3f58b5b021646eabd01dc4de2532b1 (MD5) Previous issue date: 2016 | en |
dc.description.tableofcontents | 誌謝…………………………………………………………………. i
中文摘要………………………………………………………… ii Abstract………………………………………………………... iii 目錄……………………………………………………………….. iv 圖目錄……………………………………………………..…... vi 表目錄……………………………………………………... vii 第一章 緒論………………………………………………………. 1 1.1 研究背景與動機………………………………………….. 1 1.2 研究目的……………………………………...…….. 2 1.3 研究流程……………………………………...…….. 4 第二章 文獻探討……………………………………………….... 5 2.1 GARCH模型相關文獻………………........... 5 2.2 配適度檢定與樣本外預測相關文獻………………. 8 第三章 研究方法………………………………………... 10 3.1 模型定義…………………………….… 10 3.2 樣本內配適度檢定與樣本外預測績效………. 10 第四章 實證結果分析………………………………. 12 4.1 GARCH模型下實證研究………………..…...… 12 4.2 EGARCH模型下實證研究…………...….… 14 4.3 TGARCH模型下實證研究…………….…... 15 4.4 不同模型下實證研究……………………………….….. 17 第五章 結論………………………………………........ 20 參考文獻…………………………………………………………..… 21 | |
dc.language.iso | zh-TW | |
dc.title | 不同觀察頻率下之外匯波動性估計 | zh_TW |
dc.title | Observation Frequency of Volatility Estimation in Foreign Exchange | en |
dc.type | Thesis | |
dc.date.schoolyear | 104-1 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 馮詩蘋(Shih-Ping Feng),蔡佳芬(Chia-Fen Tsai),邱琦倫(Chi-Lun Chou) | |
dc.subject.keyword | 外匯,波動, | zh_TW |
dc.subject.keyword | Foreign,exchange,volatility, | en |
dc.relation.page | 22 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2016-02-17 | |
dc.contributor.author-college | 管理學院 | zh_TW |
dc.contributor.author-dept | 國際企業學研究所 | zh_TW |
顯示於系所單位: | 國際企業學系 |
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