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題名 亞洲各國匯率與黃金期貨價格之頻域因果關係 作者 周清宥
Chou, Qing-You貢獻者 徐士勛
Hsu, Shih-Hsun
周清宥
Chou, Qing-You關鍵詞 匯率
黃金期貨價格
頻域因果關係檢定
Granger因果關係檢定日期 2020 上傳時間 3-Aug-2020 18:12:21 (UTC+8) 摘要 本文藉由時域架構下的Granger 因果關係檢定及Breitung and Candelon(2006) 提出之頻域架構下的因果關係檢定，探討亞洲各國匯率與黃金期貨價格之頻域因果關係，且在不同時間下的因果關係是否會有所不同。在時域的架構下探討亞洲匯率變數與黃金期貨價格之間的因果關係，可以發現在未加控制變數及加入控制變數後無條件及有條件模型下，得出兩變數之間僅存在單向的因果關係。然而本文在頻域的架構下探討亞洲匯率變數與黃金期貨價格之因果關係。在無條件模型中，欲將時間劃分成全期及三個子樣本下，得出在全期下，變數之間存在雙向因果關係，而在三個子樣本中，僅存在單向的因果關係；在有條件模型中，得出在全期及2008 年至2012 年這組子樣本下存在雙向的因果關係，另外兩個子樣本僅存在單向的因果關係。 參考文獻 [1] Breitung, J., and B. Candelon(2006).“Testing for Short and Long Run Causality:A FrequencyDomain Approach. Journal of Econometrics, 132(2),363-378.[2] Bhunia, A., and Pakira, S., (2014).“Investing the impact of gold price and exchange rates on sensex:An evidence of India.”European Journal of Accounting,Finance of Business, 2(1), 1-11.[3] Dickey, D.A. and W.A. Fuller (1981). “Likelihood ratio statistics for autoregressive time series with a unit root.”Econometrica, 49,1057–1072.[4] Geweke, J., (1982). “Measurement of linear dependence and feedback between multiple time series.” Journal of the American Statistical Association, 77, 304–313.[5] Granger,C.W.J (1969).“Investigating casual relations by econometric models and cross spectral methods. Econometrica, 37,424–438.[6] Hosoya, Y., (1991).“The decomposition and measurement of the interdependency between secondorderstationary processes.”Probability Theory Relat Fields, 88, pp. 429-444.[7] Hosoya, Y., (2001).“Elimination of third‐series effect and defining partial measures of causality.”Journal of time series analysis, 22(5),537-554.[8] Le, T.H., and Chang, Y.,(2013).“Dynamic Relationships between the Price of Oil, Gold and Financial Variables in Japan: A Bounds TestingApproach.”Online at http://mpra.ub.unimuenchen.de/33030/ MPRA , Paper No. 33030.[9] Nelson, C. R., and Plosser, C. R.,(1982). “Trends and random walks in macroeconmic time series: some evidence and implications.”Journal of monetary economics, 10(2), 139-162.[10] Schaling,E., Ndlovu,X., and Alagidede,P.,(2014).“Modelling the rand and commodity prices: A Ganger causality and cointegration analysis.” Sajems Ns ,17,No 5:673–690.[11] Yao, F., and Hosoya, Y., (2000). “Inference on one way effect and evidence in japanese macroeconomic data.”Journal of Econometrics, 98, 225–255. 描述 碩士
國立政治大學
經濟學系
107258028資料來源 http://thesis.lib.nccu.edu.tw/record/#G0107258028 資料類型 thesis dc.contributor.advisor 徐士勛 zh_TW dc.contributor.advisor Hsu, Shih-Hsun en_US dc.contributor.author (Authors) 周清宥 zh_TW dc.contributor.author (Authors) Chou, Qing-You en_US dc.creator (作者) 周清宥 zh_TW dc.creator (作者) Chou, Qing-You en_US dc.date (日期) 2020 en_US dc.date.accessioned 3-Aug-2020 18:12:21 (UTC+8) - dc.date.available 3-Aug-2020 18:12:21 (UTC+8) - dc.date.issued (上傳時間) 3-Aug-2020 18:12:21 (UTC+8) - dc.identifier (Other Identifiers) G0107258028 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/131183 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 經濟學系 zh_TW dc.description (描述) 107258028 zh_TW dc.description.abstract (摘要) 本文藉由時域架構下的Granger 因果關係檢定及Breitung and Candelon(2006) 提出之頻域架構下的因果關係檢定，探討亞洲各國匯率與黃金期貨價格之頻域因果關係，且在不同時間下的因果關係是否會有所不同。在時域的架構下探討亞洲匯率變數與黃金期貨價格之間的因果關係，可以發現在未加控制變數及加入控制變數後無條件及有條件模型下，得出兩變數之間僅存在單向的因果關係。然而本文在頻域的架構下探討亞洲匯率變數與黃金期貨價格之因果關係。在無條件模型中，欲將時間劃分成全期及三個子樣本下，得出在全期下，變數之間存在雙向因果關係，而在三個子樣本中，僅存在單向的因果關係；在有條件模型中，得出在全期及2008 年至2012 年這組子樣本下存在雙向的因果關係，另外兩個子樣本僅存在單向的因果關係。 zh_TW dc.description.tableofcontents 1 前言 12 文獻回顧 23 模型與方法 53.1 單根檢定. . . . . . . . .. . . . . . . . . . . . . . 53.2 Granger Causality . . . . . . . . . . . . . . . . . 83.3 頻域因果檢定. . . . . . . . . . . . . . . . . . . . . 94 資料與基本統計性質 144.1 資料來源. . . . . . . . . . . . . . . . . . . . . . 144.2 資料說明. . . . . . . . .. . . . . . . . . . . . . . 155 實證結果與討論 245.1 時域下因果關係檢定. . . . . . . . . . . . . . . . . 255.1.1 雙變量無條件模型. . . . . . . . . . . . . . . . . 255.1.2 雙變量有條件模型. . . . . . . . . . . . . . . . . 305.2 頻域下因果關係檢定. . . . . . . . . . . . . . . . . 345.2.1 雙變量無條件模型. . . . . . . . . . . . . . . . . 345.2.2 雙變量有條件模型. . . . . . . . . . . . . . . . . 435.3 美元指數與黃金期貨價格之頻域關係. . . . . . . . . . . 486 結論與建議 50參考文獻 52 zh_TW dc.format.extent 1467378 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0107258028 en_US dc.subject (關鍵詞) 匯率 zh_TW dc.subject (關鍵詞) 黃金期貨價格 zh_TW dc.subject (關鍵詞) 頻域因果關係檢定 zh_TW dc.subject (關鍵詞) Granger因果關係檢定 zh_TW dc.title (題名) 亞洲各國匯率與黃金期貨價格之頻域因果關係 zh_TW dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) [1] Breitung, J., and B. Candelon(2006).“Testing for Short and Long Run Causality:A FrequencyDomain Approach. Journal of Econometrics, 132(2),363-378.[2] Bhunia, A., and Pakira, S., (2014).“Investing the impact of gold price and exchange rates on sensex:An evidence of India.”European Journal of Accounting,Finance of Business, 2(1), 1-11.[3] Dickey, D.A. and W.A. Fuller (1981). “Likelihood ratio statistics for autoregressive time series with a unit root.”Econometrica, 49,1057–1072.[4] Geweke, J., (1982). “Measurement of linear dependence and feedback between multiple time series.” Journal of the American Statistical Association, 77, 304–313.[5] Granger,C.W.J (1969).“Investigating casual relations by econometric models and cross spectral methods. Econometrica, 37,424–438.[6] Hosoya, Y., (1991).“The decomposition and measurement of the interdependency between secondorderstationary processes.”Probability Theory Relat Fields, 88, pp. 429-444.[7] Hosoya, Y., (2001).“Elimination of third‐series effect and defining partial measures of causality.”Journal of time series analysis, 22(5),537-554.[8] Le, T.H., and Chang, Y.,(2013).“Dynamic Relationships between the Price of Oil, Gold and Financial Variables in Japan: A Bounds TestingApproach.”Online at http://mpra.ub.unimuenchen.de/33030/ MPRA , Paper No. 33030.[9] Nelson, C. R., and Plosser, C. R.,(1982). “Trends and random walks in macroeconmic time series: some evidence and implications.”Journal of monetary economics, 10(2), 139-162.[10] Schaling,E., Ndlovu,X., and Alagidede,P.,(2014).“Modelling the rand and commodity prices: A Ganger causality and cointegration analysis.” Sajems Ns ,17,No 5:673–690.[11] Yao, F., and Hosoya, Y., (2000). “Inference on one way effect and evidence in japanese macroeconomic data.”Journal of Econometrics, 98, 225–255. zh_TW dc.identifier.doi (DOI) 10.6814/NCCU202000699 en_US
