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請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/71327
完整後設資料紀錄
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dc.contributor.advisor李顯峰(Hsien-Feng Lee)
dc.contributor.authorAi- Ling Leeen
dc.contributor.author李愛玲zh_TW
dc.date.accessioned2021-06-17T05:58:56Z-
dc.date.available2021-02-26
dc.date.copyright2021-02-26
dc.date.issued2021
dc.date.submitted2021-02-19
dc.identifier.citation英文部分:
1. Antonakakis, N. and J. Darby (2012), “Forecasting Volatility in Developing Countries' Nominal Exchange Returns”, MPRA Working Paper No.40875.
2. Black, F. (1976), “Studies of Stock Price Volatility Changes”, Proceedings of the 1976 Meeting of the Business and Economics Statistics Section, American Statistical Association, Washington DC, 177-181.
3. Bloom, D. E. and A. S. Mahal (1997), “Does the AIDS Epidemic Threaten Economic Growth? ”, Journal of Econometrics, 77, 105-124.
4. Bollerslev, T. (1986), “Generalized Autoregressive Conditional Heteroskedasticity”, Journal of Econometrics, 31(3), 307-327.
5. Brodeur, Abel and David M. Gray, Anik, Islam and Bhuiyan, Suraiya (2020), “A Literature Review of the Economics of Covid-19” , IZA Discussion Paper No. 13411, Available at SSRN: https://ssrn.com/abstract=3636640
6. Chen, M. H., S. C. Jang and W. G. Kim (2007), “The Impact of the SARS Outbreak on Taiwanese Hotel Stock Performance: An Event-Study Approach”, Hospitality Management, 26, 200–212.
7. Dickey, DA. and W. A. Fuller (1979), “Distributions of the Estimators for Autoregressive Time Series with a Unit Root”, Journal of American Statistical Association, 74(366), 427-431.
8. Dickey, DA. and W. A. Fuller (1981), “Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root”, Econometrica, 49(4), 1057-1072.
9. Dornbusch, R. (1976), “Expectations and Exchange Rate Dynamics”, Journal of
Political Economy, 84, 1161-1176.
10. Engle, R. F. (1982), “Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of U.K. Inflation”, Econometrica, 50(4), 987-1008.
11. Epaphra, M. (2017), “Modeling Exchange Rate Volatility: Application of the GARCH and EGARCH Models”, Journal of Mathematical Finance, 7(1), 121-143.
12. Fama, E. F. (1965), “The Behavior of Stock Market Prices”, Journal of Business, 38(1), 34-105.
13. Franses, P. H. and D. Van Dijk (1996), “Forecasting Stock Market Volatility
Using Non-linear GARCH Models”, Journal of Forecasting, 15, 229-235.
14. Friedman, D. and S. Vandersteel (1982), “Short-Run Fluctuations in Foreign Exchange Rates: Evidence from the Data 1973-1979”, Journal of International Economics, 13(1-2), 171-186.
15. Fernandes, N. (2020), “Economic Effects Of Coronavirus Outbreak (COVID-19) On The World Economy, 13 March”. [ https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3557504 accessed on June 29 2020].
16. Glosten, L. R., R. Jagannathan and D. E. Runkle (1993), “On the Relation
between the Expected Value and the Volatility of the Nominal Excess Return on
Stocks”, Journal of Finance, 48, 1779-1801.
17. Hofmann, B., I. Shim and H. S. Shin (2020), “Emerging Market Economy Exchange Rates And Local Currency Bond Markets Amid The Covid-19 Pandemic”, BIS Bulletin, No. 5, 7 April.
18. Jarque, C. and A. Bera (1980), “Efficient Tests for Normality, Homoscedasticity and Serial Independence of Regression Residuals”, Economics Letters, 6(3), 255-259.
19. Kauffman, K. and A. Weerapana (2006), “The Impact Of AIDS-Related News On Exchange Rates In South Africa”, Economic Development And Cultural Change, 54(2), 349-368.
20. Keogh-Brown, M. R. and R. D. Smith ( 2008), “The Economic Impact Of SARS: How Does The Reality Match The Predictions? ” , Health Policy, 88(1), 110-120.
21. Kouam, H. (2020), “COVID-19: Crisis-Averse versus Growth-Centric Monetary Policy (July 18, 2020) ”. Available at SSRN: https://ssrn.com/abstract=3655315 or http://dx.doi.org/10.2139/ssrn.3655315
22. Lastrapes, W. D. (1989), “Exchange Rate Volatility and U.S. Monetary Policy: An ARCH Application”, Journal of Money, Credit Banking, 21(1): 66-77.
23. Ljung G. M. and G. E. P. Box (1978), “On a Measure of Lack of Fit in Time Series Models”, Biometrika, 65(2), 297-303.
24. MacKinnon J. G. (1996), “Numerical Distribution Functions for Unit Root and Cointegration Tests”, Journal of Applied Econometrics, 11(6), 601-618.
25. Mandelbrot, B. (1963), “The Variation of Certain Speculative Prices”, Journal of Business, 36(4), 394-414.
26. Nelson, D. B. (1991), “Conditional Heteroscedasticity in Asset Returns: A New Approach”, Econometrica, 59(2), 347-370.
27. Newey, W. K. and K. D. West (1994), “Automatic Lag Selection in Covariance Matrix Estimation”, Review of Economic Studies, 61(4), 631-654.
28. Odhiambo, J., P. Weke and P. Ngare (2020), “Modeling Kenyan Economic Impact of Corona Virus in Kenya Using Discrete-Time Markov Chains”, Journal of Finance and Economics, 8(2), 80-85.
29. Pagan, A. R. and G. W. Schwert (1990), “Alternative Models for Conditional
Stock Volatilities”, Journal of Econometrics, 45, 267-290.
30. Phillips, P. C. B. and P. Perron (1998), “Testing for a Unit Root in Time Series Regression”, Biometrika, 75(2), 335-346.
31. Roley, V. V. and G. H, Jr. Sellon (1998), “ Monetary Policy Actions, Intervention, and Exchange Rates: A Reexamination of the Empirical Relationships Using Federal Funds Rate Target Data”, Journal of Business, 71(2): 147-77
32. Tsay, R. S. (1989), “Testing and Modeling Threshold Autoregressive Processes”, Journal of the American Statistical Assorciation, (84), 231-240.
中文部分:
1. 李榮謙(2012),「貨幣銀行學」,十版增訂,臺北市,智勝文化有限公司。
2. 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」,《臺灣管理學刊》,4(2),187 - 201。
3. 陳旭昇(2013),「時間序列分析總體經濟與財務金融之應用」,二版,臺北市,東華書局。
4. 彭德明(2013),「東亞主要新興經濟體匯率變動率波動與共變程度之實證分析」,《中央銀行季刊》,35(3),3-36。
5. 蕭欽篤(2006),「國際金融」,五版,臺北市,智勝文化有限公司。
dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/71327-
dc.description.abstract本研究探討新冠疫情(COVID-19)對匯率波動率的影響,樣本期間為2010年1月至2020年6月之月資料,計有125個觀察值。採用GARCH(1,1)、TGARCH(1,1)、EGARCH(1,1)實證模型的波動性,分析COVID-19疫情對各國月平均外匯匯率波動的影響。採取ARCH效果檢定是以殘差平方的相關圖(Q)檢驗法,實證結果顯示COVID-19疫情在2020年1月至2020年6月的時間序列分析,在澳幣、加拿大幣、人民幣、歐元、英鎊、港幣、日圓、韓元、新台幣、新加坡幣兌美元的月平均匯率波動率其波動程度較大,各國月平均匯率波動率波動皆顯示於外匯市場存在著不對稱效果。
依據實證結果顯示,在GARCH建立波動性估計模型中,尤以GARCH(1,1)模型僅人民幣不符合共變異數條件,而在TGARCH(1,1)模型有微弱的非對稱性,但是在EGARCH(1,1)模型,人民幣在月平均匯率波動性極具非對稱性及存在槓桿效果。主要原因在於中國人行擴大貨幣政策力度及運用多種貨幣政策工具措施,穩定金融市場與減少疫情對企業的衝擊,以應對新冠疫情之影響。故針對各國樣本資料進行匯率波動性的模型評估中,以EGARCH 模型效果最好。
zh_TW
dc.description.abstractThis study explores the impact of the COVID-19 pandemic on exchange rate volatility. The sample period is from January 2010 to June 2020, with 125 observations. Using the volatility of the empirical models of GARCH(1,1), TGARCH(1,1), and EGARCH(1,1), we analyze the impact of the COVID-19 epidemic on the fluctuations of the monthly average foreign exchange rates of various currencies. The ARCH effect test is based on the correlation diagram (Q) test method of residual squares. The empirical results show that the time series analysis of the COVID-19 epidemic from January 2020 to June 2020 is in Australian dollars, Canadian dollars, Renminbi (RMB), Euro, The monthly average exchange rate volatility of the British pound, Hong Kong dollar, Japanese yen, South Korean won, New Taiwan dollar, and Singapore dollar against the U.S. dollar fluctuates greatly. The fluctuations in the monthly average exchange rate volatility of various countries all show that there is an asymmetric effect in the foreign exchange market.
According to the empirical results, in the volatility estimation model established by GARCH, especially the GARCH(1,1) model only the RMB does not meet the covariance condition, while the TGARCH(1,1) model has weak asymmetry, but in the EGARCH(1,1) model, the monthly average exchange rate volatility of the RMB is extremely asymmetric and has a leverage effect. The main reason is that the People's Bank of China has expanded its monetary policy and used a variety of monetary policy tools and measures to stabilize the financial market and reduce the impact of the epidemic on enterprises by the COVID-19. Therefore, in the model evaluation of exchange rate volatility based on sample data of various countries, the EGARCH model has the best effect.
en
dc.description.provenanceMade available in DSpace on 2021-06-17T05:58:56Z (GMT). No. of bitstreams: 1
U0001-1902202109192700.pdf: 8686688 bytes, checksum: 48e2d80d6d86e9cefa92f51aba2df234 (MD5)
Previous issue date: 2021
en
dc.description.tableofcontents誌謝 i
摘要 ii
Abstract iii
目 錄 iv
圖目錄 v
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 5
1.3研究架構 6
第二章 文獻回顧 8
2.1文獻探討 8
2.2 外匯匯率波動之影響因素 10
第三章 研究方法 13
3.1 GARCH模型、TGARCH模型、EGARCH模型. 13
3.2單根檢定(Unit root test) 16
第四章 實證結果 19
4.1 ARCH效果檢定 24
4.2敘述統計及ADF與PP平穩性測試 30
4.3實證分析 32
第五章 結論與建議 37
5.1結論 37
5.2建議 38
參考文獻 39
附錄 42
dc.language.isozh-TW
dc.subject新冠疫情(COVID-19)zh_TW
dc.subject匯率波動zh_TW
dc.subject時間序列分析zh_TW
dc.subjectARCH效果檢定zh_TW
dc.subjectGARCH(1zh_TW
dc.subject1)模型zh_TW
dc.subjectTGARCH(1zh_TW
dc.subject1)模型zh_TW
dc.subjectEGARCH(1zh_TW
dc.subject1)模型zh_TW
dc.subjectTGARCH (1en
dc.subjectCOVID-19en
dc.subjectExchange rate volatilityen
dc.subjectTime series analysisen
dc.subject1) modelen
dc.subjectARCH effect testen
dc.subjectGARCH (1en
dc.subjectEGARCH (1en
dc.subject1) modelen
dc.subject1) modelen
dc.title新冠疫情對匯率波動之實證研究zh_TW
dc.titleAn Empirical Study of the COVID-19 on Exchange Rates Volatilityen
dc.typeThesis
dc.date.schoolyear109-1
dc.description.degree碩士
dc.contributor.oralexamcommittee謝德宗(Der-tzon Hsieh),林惠玲(Hui-Lin Lin)
dc.subject.keyword新冠疫情(COVID-19),匯率波動,時間序列分析,ARCH效果檢定,GARCH(1,1)模型,TGARCH(1,1)模型,EGARCH(1,1)模型,zh_TW
dc.subject.keywordCOVID-19,Exchange rate volatility,Time series analysis,ARCH effect test,GARCH (1,1) model,TGARCH (1,1) model,EGARCH (1,1) model,en
dc.relation.page62
dc.identifier.doi10.6342/NTU202100748
dc.rights.note有償授權
dc.date.accepted2021-02-19
dc.contributor.author-college社會科學院zh_TW
dc.contributor.author-dept經濟學研究所zh_TW
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