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題名 台灣股市與國際股市之時變領先落後連動性研究 - 遞迴演進窗口法之應用
Time-varying Lead-lag Movement between Taiwan and International Stock Markets: An Application of Recursive Evolving Window Approach作者 張竣凱
Chang, Chun-Kai貢獻者 徐士勛
Hsu, Shih-Hsun
張竣凱
Chang, Chun-Kai關鍵詞 遞迴演進窗口法
時變因果關係
股市領先落後關係
Recursive evolving window approach
Time-varying causality
Stock market lead-lag relationship日期 2022 上傳時間 1-Jul-2022 16:26:14 (UTC+8) 摘要 本研究使用 Shi, Phillips and Hurn (2018) 提出的遞迴演進窗口法，探討台灣股市與國際股市之間隨時間改變的領先落後關係。我們選定1997年1月至2021年12月的台灣加權股價指數以及8個已開發國家股票市場大盤指數作為分析資料，並建置未控制美國股市影響下之雙變量模型以及控制美國股市影響之三變量模型進行實證分析。研究結果顯示，在未控制美國股市影響下，台灣股市曾單向領先日本股市，韓國股市也曾單向領先台灣股市，但中國股市與台灣股市於任何時點皆不存在領先落後關係，而香港股市與台灣股市曾存在雙向領先落後關係；整體而言，亞洲各國股市與台灣股市的領先落後關係時期存在較大差異。相較之下，英國、德國、法國股市與台灣股市都曾存在雙向領先落後關係，且歐洲各國股市與台灣股市的領先落後關係時期較為一致。當控制美國股市的影響後，亞洲各國股市與台灣股市的領先落後關係時點依然迥異，且歐洲各國股市與台灣股市的領先落後關係時期也產生顯著變化，驗證了美國股市確實對各國股市具有相當影響力。
This study uses the recursive evolving window method proposed by Shi, Phillips and Hurn (2018) to explore the time-varying lead-lag relationship between Taiwan and international stock markets. We select Taiwan`s weighted stock price index from January 1997 to December 2021 and the stock market index of 8 developed countries as the analysis data, and build the bivariate model and the trivariate model which controls for the influence of US stock market in particular for the empirical analysis. The results of bivariate models show that, Taiwan stock market once led Japanese stock market, and South Korean stock market once led Taiwan stock market, but Chinese and Taiwan stock market did not have a lead-lag relationship at any time. Besides, there was a bidirectional lead-lag relationship between Hong Kong and Taiwan stock market; on the whole, there is a big difference in the period of the lead-lag relationship between Asian and Taiwan stock market. In contrast, UK, German and French stock markets and Taiwan stock markets all had a bidirectional lead-lag relationship once, and the period of the lead-lag relationship between European and Taiwan stock market are relatively consistent. After controlling for the influence of U.S. stock market, the period of the lead-lag relationship between Asian and Taiwan stock market is still very different, and the period of the lead-lag relationship between European and Taiwan stock market also changed significantly, which proves that U.S. stock market does have a considerable impact on the stock markets of various countries.參考文獻 Cagli, E. C. (2019). The Causal Relationship between Returns and Trading Volume in Cryptocurrency Markets: Recursive Evolving Approach. Blockchain Economics and Financial Market Innovation, 167–190.Cagli, E. C. (2019). The Causality between Consumer Confidence Index and Stock Returns: Evidence from Recursive Evolving Granger Causality Test. Journal of Yasar University, 14, 164–172.De Gooijer, J. G. and Sivarajasingham, S. (2008). Parametric and Nonparametric Granger Causality Testing: Linkages between International Stock Markets. Physica A, 387, 2547–2560.Emirmahmutoglu, F., Denaux Z., and Topcu, M. (2021). Time-varying Causality between Renewable and Non-renewable Energy Consumption and Real Output: Sectoral Evidence from the United States. Renewable and Sustainable Energy Reviews, 149, 111326.Gharib, C., Mefteh-Wali, S., and Jabeur, S. B. (2021). The Bubble Contagion Effect of COVID-19 Outbreak: Evidence from Crude Oil and Gold Markets. Finance Research Letters, 38, 101703.Gkillas, K., Tsagkanosa, A., and Vortelinos, D. I. (2019). Integration and Risk Contagion in Financial Crises: Evidence from International Stock Markets. Journal of Business Research, 104, 350–365.Hammoudeh, S., Ajmi, A. N., and Mokni, K. (2020). Relationship between Green Bonds and Financial and Environmental Variables: A Novel Time-varying Causality. Energy Economics, 92, 104941.Jiang, Y., Yu, M., and Hashmi, S. M. (2017). The Financial Crisis and Comovement of Global Stock Markets - A Case of Six Major Economies. Sustainability, 9(2), 260.Merica, I., Kim J. H., Gongc L., and Meric G. (2012). Co-movements of and Linkages between Asian Stock Markets. Business and Economics Research Journal, 3(1), 1–15.Papana, A., Kyrtsou, C., Kugiumtzis, D., and Diks, C. (2017). Financial Networks Based on Granger Causality: A Case Study. Physica A, 2017, 65–73.Shi, S., Phillips, P. C. B., and Hurn, S. (2018). Change Detection and the Causal Impact of the Yield Curve. Journal of Time Series Analysis, 39, 966–987.Shi, S., Hurn, S., and Phillips, P. C. B. (2019). Causal Change Detection in Possibly Integrated Systems: Revisiting the Money-income Relationship. Journal of Financial Econometrics, 18(1), 158–180.Swanson N. R. (1998). Money and Output Viewed through a Rolling Window. Journal of Monetary Economics, 41(3), 455 474.Tang, Y., Xiong, J. J., Luo, Y., and Zhang, Y. C. (2019). How Do the Global Stock Markets Influence One Another? Evidence from Finance Big Data and Granger Causality Directed Network. International Journalof Electronic Commerce, 23(1), 85–109.Thoma M. A. (1994). Subsample Instability and Asymmetries in Moneyincome Causality. Journal of Econometrics, 64(1-2), 279–306.Zheng, Q. and Song, L. (2018). Dynamic Contagion of Systemic Risks on Global Main Equity Markets Based on Granger Causality Networks. Discrete Dynamics in Nature and Society, 2018, 9461870. 描述 碩士
國立政治大學
經濟學系
109258003資料來源 http://thesis.lib.nccu.edu.tw/record/#G0109258003 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) 張竣凱 zh_TW dc.contributor.author (Authors) Chang, Chun-Kai en_US dc.creator (作者) 張竣凱 zh_TW dc.creator (作者) Chang, Chun-Kai en_US dc.date (日期) 2022 en_US dc.date.accessioned 1-Jul-2022 16:26:14 (UTC+8) - dc.date.available 1-Jul-2022 16:26:14 (UTC+8) - dc.date.issued (上傳時間) 1-Jul-2022 16:26:14 (UTC+8) - dc.identifier (Other Identifiers) G0109258003 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/140685 - 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) 109258003 zh_TW dc.description.abstract (摘要) 本研究使用 Shi, Phillips and Hurn (2018) 提出的遞迴演進窗口法，探討台灣股市與國際股市之間隨時間改變的領先落後關係。我們選定1997年1月至2021年12月的台灣加權股價指數以及8個已開發國家股票市場大盤指數作為分析資料，並建置未控制美國股市影響下之雙變量模型以及控制美國股市影響之三變量模型進行實證分析。研究結果顯示，在未控制美國股市影響下，台灣股市曾單向領先日本股市，韓國股市也曾單向領先台灣股市，但中國股市與台灣股市於任何時點皆不存在領先落後關係，而香港股市與台灣股市曾存在雙向領先落後關係；整體而言，亞洲各國股市與台灣股市的領先落後關係時期存在較大差異。相較之下，英國、德國、法國股市與台灣股市都曾存在雙向領先落後關係，且歐洲各國股市與台灣股市的領先落後關係時期較為一致。當控制美國股市的影響後，亞洲各國股市與台灣股市的領先落後關係時點依然迥異，且歐洲各國股市與台灣股市的領先落後關係時期也產生顯著變化，驗證了美國股市確實對各國股市具有相當影響力。 zh_TW dc.description.abstract (摘要) This study uses the recursive evolving window method proposed by Shi, Phillips and Hurn (2018) to explore the time-varying lead-lag relationship between Taiwan and international stock markets. We select Taiwan`s weighted stock price index from January 1997 to December 2021 and the stock market index of 8 developed countries as the analysis data, and build the bivariate model and the trivariate model which controls for the influence of US stock market in particular for the empirical analysis. The results of bivariate models show that, Taiwan stock market once led Japanese stock market, and South Korean stock market once led Taiwan stock market, but Chinese and Taiwan stock market did not have a lead-lag relationship at any time. Besides, there was a bidirectional lead-lag relationship between Hong Kong and Taiwan stock market; on the whole, there is a big difference in the period of the lead-lag relationship between Asian and Taiwan stock market. In contrast, UK, German and French stock markets and Taiwan stock markets all had a bidirectional lead-lag relationship once, and the period of the lead-lag relationship between European and Taiwan stock market are relatively consistent. After controlling for the influence of U.S. stock market, the period of the lead-lag relationship between Asian and Taiwan stock market is still very different, and the period of the lead-lag relationship between European and Taiwan stock market also changed significantly, which proves that U.S. stock market does have a considerable impact on the stock markets of various countries. en_US dc.description.tableofcontents 1 緒論 12 文獻回顧 23 研究方法 53.1 單根檢定 63.2 格蘭傑因果關係 83.3 時變因果關係模型 93.3.1 向前擴張窗口法 113.3.2 滾動窗口法 123.3.3 遞迴演進窗口法 133.4 拔靴法估計模型臨界值 144 資料說明與實證模型 174.1 資料說明與敘述統計 174.2 實證模型 265 實證結果 285.1 遞迴演進窗口法雙變量模型 285.2 遞迴演進窗口法三變量模型 436 結論 557 參考文獻 57圖目錄1 向前擴張窗口法過程示意圖 122 滾動窗口法過程示意圖 133 遞迴演進窗口法過程示意圖 144 台灣加權股價指數價格序列 235 台灣加權股價指數報酬率序列 236 美國道瓊工業指數價格序列 237 美國道瓊工業指數報酬率序列 238 日本日經225指數價格序列 239 日本日經225指數報酬率序列 2310 韓國綜合股價指數價格序列 2411 韓國綜合股價指數報酬率序列 2412 中國深圳綜合指數價格序列 2413 中國深圳綜合指數報酬率序列 2414 香港恆生指數價格序列 2415 香港恆生指數報酬率序列 2416 英國FTSE 100指數價格序列 2517 英國FTSE 100指數報酬率序列 2518 德國DAX 30指數價格序列 2519 德國 DAX 30 指數報酬率序列 2520 法國 CAC 40 指數價格序列 2521 法國 CAC 40 指數報酬率序列 2522 美國領先台灣之雙變量模型檢定結果 2923 台灣領先美國之雙變量模型檢定結果 2924 日本領先台灣之雙變量模型檢定結果 3125 台灣領先日本之雙變量模型檢定結果 3126 韓國領先台灣之雙變量模型檢定結果 3327 台灣領先韓國之雙變量模型檢定結果 3328 中國領先台灣之雙變量模型檢定結果 3529 台灣領先中國之雙變量模型檢定結果 3530 香港領先台灣之雙變量模型檢定結果 3631 台灣領先香港之雙變量模型檢定結果 3632 英國領先台灣之雙變量模型檢定結果 3833 台灣領先英國之雙變量模型檢定結果 3834 德國領先台灣之雙變量模型檢定結果 4035 台灣領先德國之雙變量模型檢定結果 4036 法國領先台灣之雙變量模型檢定結果 4137 台灣領先法國之雙變量模型檢定結果 4138 雙變量模型檢定結果統整 4239 日本領先台灣之三變量模型檢定結果 4440 台灣領先日本之三變量模型檢定結果 4441 韓國領先台灣之三變量模型檢定結果 4642 台灣領先韓國之三變量模型檢定結果 4643 中國領先台灣之三變量模型檢定結果 4744 台灣領先中國之三變量模型檢定結果 4745 香港領先台灣之三變量模型檢定結果 4946 台灣領先香港之三變量模型檢定結果 4947 英國領先台灣之三變量模型檢定結果 5048 台灣領先英國之三變量模型檢定結果 5049 德國領先台灣之三變量模型檢定結果 5250 台灣領先德國之三變量模型檢定結果 5251 法國領先台灣之三變量模型檢定結果 5352 台灣領先法國之三變量模型檢定結果 5353 三變量模型檢定結果統整 54表目錄1 各國大盤股價指數 182 各國股市大盤報酬率序列敘述統計量 203 各國股市大盤報酬率序列單根檢定 26 zh_TW dc.format.extent 1488045 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0109258003 en_US dc.subject (關鍵詞) 遞迴演進窗口法 zh_TW dc.subject (關鍵詞) 時變因果關係 zh_TW dc.subject (關鍵詞) 股市領先落後關係 zh_TW dc.subject (關鍵詞) Recursive evolving window approach en_US dc.subject (關鍵詞) Time-varying causality en_US dc.subject (關鍵詞) Stock market lead-lag relationship en_US dc.title (題名) 台灣股市與國際股市之時變領先落後連動性研究 - 遞迴演進窗口法之應用 zh_TW dc.title (題名) Time-varying Lead-lag Movement between Taiwan and International Stock Markets: An Application of Recursive Evolving Window Approach en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) Cagli, E. C. (2019). The Causal Relationship between Returns and Trading Volume in Cryptocurrency Markets: Recursive Evolving Approach. Blockchain Economics and Financial Market Innovation, 167–190.Cagli, E. C. (2019). The Causality between Consumer Confidence Index and Stock Returns: Evidence from Recursive Evolving Granger Causality Test. Journal of Yasar University, 14, 164–172.De Gooijer, J. G. and Sivarajasingham, S. (2008). Parametric and Nonparametric Granger Causality Testing: Linkages between International Stock Markets. Physica A, 387, 2547–2560.Emirmahmutoglu, F., Denaux Z., and Topcu, M. (2021). Time-varying Causality between Renewable and Non-renewable Energy Consumption and Real Output: Sectoral Evidence from the United States. Renewable and Sustainable Energy Reviews, 149, 111326.Gharib, C., Mefteh-Wali, S., and Jabeur, S. B. (2021). The Bubble Contagion Effect of COVID-19 Outbreak: Evidence from Crude Oil and Gold Markets. Finance Research Letters, 38, 101703.Gkillas, K., Tsagkanosa, A., and Vortelinos, D. I. (2019). Integration and Risk Contagion in Financial Crises: Evidence from International Stock Markets. Journal of Business Research, 104, 350–365.Hammoudeh, S., Ajmi, A. N., and Mokni, K. (2020). Relationship between Green Bonds and Financial and Environmental Variables: A Novel Time-varying Causality. Energy Economics, 92, 104941.Jiang, Y., Yu, M., and Hashmi, S. M. (2017). The Financial Crisis and Comovement of Global Stock Markets - A Case of Six Major Economies. Sustainability, 9(2), 260.Merica, I., Kim J. H., Gongc L., and Meric G. (2012). Co-movements of and Linkages between Asian Stock Markets. Business and Economics Research Journal, 3(1), 1–15.Papana, A., Kyrtsou, C., Kugiumtzis, D., and Diks, C. (2017). Financial Networks Based on Granger Causality: A Case Study. Physica A, 2017, 65–73.Shi, S., Phillips, P. C. B., and Hurn, S. (2018). Change Detection and the Causal Impact of the Yield Curve. Journal of Time Series Analysis, 39, 966–987.Shi, S., Hurn, S., and Phillips, P. C. B. (2019). Causal Change Detection in Possibly Integrated Systems: Revisiting the Money-income Relationship. Journal of Financial Econometrics, 18(1), 158–180.Swanson N. R. (1998). Money and Output Viewed through a Rolling Window. Journal of Monetary Economics, 41(3), 455 474.Tang, Y., Xiong, J. J., Luo, Y., and Zhang, Y. C. (2019). How Do the Global Stock Markets Influence One Another? Evidence from Finance Big Data and Granger Causality Directed Network. International Journalof Electronic Commerce, 23(1), 85–109.Thoma M. A. (1994). Subsample Instability and Asymmetries in Moneyincome Causality. Journal of Econometrics, 64(1-2), 279–306.Zheng, Q. and Song, L. (2018). Dynamic Contagion of Systemic Risks on Global Main Equity Markets Based on Granger Causality Networks. Discrete Dynamics in Nature and Society, 2018, 9461870. zh_TW dc.identifier.doi (DOI) 10.6814/NCCU202200553 en_US
