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題名 金融事件衝擊時國際金融變數對航運基金SEA之影響--運用傳遞熵與GARCH-MIDAS模型
作者 林泳孜
Lin, Yung-Tzu
貢獻者 林靖<br>蕭明福
林泳孜
Lin, Yung-Tzu
關鍵詞 SEA ETF
外溢效果
傳遞熵
GARCH-MIDAS
貨運市場
SEA ETF
Volatility spillovers
Transfer Entropy
GARCH-MIDAS
Shipping freight markets
日期 2020
上傳時間 2-Sep-2020 12:47:14 (UTC+8)
摘要 本研究使用全球海運指數基金(Invesco Shipping ETF; SEA ETF)所追蹤的指數推進Lin et al.（2019）研究成果，強調貨運市場和匯率市場和股價市場間的動態相關性，探討SEA ETF與航運市場和其他金融市場間的傳導聯繫。透過使用傳遞熵（Transfer Entropy）與GARCH-MIDAS實證分析研究SEA ETF的信息流之方向及傳遞效果，以探討其外溢效果（spillover effect）與路徑，提供投資者對其市場預測與投資組合有更多的見解，近一步改善風險管理，特別是發生金融衝擊的動盪時期。最後實證分析結果顯示SEA ETF在大部分時期與國際金融變數呈現顯著性相關，透過傳遞熵實證結果判定領先與落後指標，預測國際金融變數之未來走勢。
This study uses Invesco Shipping ETF (SEA ETF) to advance the research results of Lin et al. (2019), emphasizes the dynamic associativity between the freight market, the exchange rate market and the stock price market, and analysis the relationship between the SEA ETF, shipping market and other financial markets. Through the adaptation of Transfer Entropy and GARCH-MIDAS, this study illustrates the direction and transmission effect of the SEA ETF information flow. It passed to explore its spillover effect and path; provide investors with more insights to the market forecasts and investment portfolios, and improve risk management, especially during the financial crisis. The final empirical analysis results reveal that the SEA ETF is significantly correlated with global economic variables in most of the period. The results of Transfer Entropy are used for determining the leading and lagging indicators and predicting the future trend of international financial variables.
參考文獻 Amir H. Alizadeh, Roar Os Ådland and Steen Koekebakker,(2007). Predictive power and unbiasedness of implied forward charter rates, Journal of Forecasting, 26, 385–403.
Amir H.Alizadeh and GulnurMuradoglu,(2014).Stock market efficiency and. international shipping-market information, Journal of International Financial Markets, Institutions and Money, 33, 445-461.
Arthur J.Lin, Hai YenChang and Jung LiehHsiao,(2019).Does the Baltic Dry Index. drive volatility spillovers in the commodities, currency, or stock markets?, Transportation Research Part E: Logistics and Transportation Review, 127, 265-283.
Aviral Kumar Tiwari,Claudiu Tiberiu Albulescu and Seong-Min Yoon,(2017). A. multifractal detrended fluctuation analysis of financial market efficiency: Comparison using Dow Jones sector ETF indices, Physica A: Statistical Mechanics and its Applications, 483, 182-192.
Basel Awartani and Aktham IssaMaghyere,(2013).Dynamic spillovers between oil. and stock markets in the Gulf Cooperation Council Countries, Energy Economics, 36, 28-42.
Ben R.Marshall,Nhut H,Nguyen and NuttawatVisaltanachotic,(2013). ETF arbitrage: Intraday evidence, Journal of Banking & Finance, 37, Issue 9, 3486-3498.
Brunnermeier, Markus K,(2005). Information Leakage and Market Efficiency, Review. of Financial Studies, 18, 417-457.
C. E. SHANNON,(1948).A Mathematical Theory of Communication, The Bell. System Technical Journal, 27, 379–423, 623–656.
Chia-Lin Chang, Chia-Ping Liu and Michael McAleer,(2019). Volatility spillovers for spot, futures, and ETF prices in agriculture and energy, Energy Economics, 81,779–792.
comparison between exchange-traded funds and closed-end country funds, Journal of International Financial Markets, Institutions and Money, 16, Issue 2, 104-122.
David R Gallagher and Reuben Segara,(2005). The performance and trading. characteristics of exchange-traded funds, Journal of Investment Strategies, 1.
Dimitris A. Tsouknidis,(2016).Dynamic volatility spillovers across shipping freight markets, Transportation Research Part E: Logistics and Transportation Review, 91, 90-111.
Edwin J. Elton, Martin J. Gruber, George Comer and Kai Li(2002). Spiders: Where. are the bugs?, The Journal of Business, 75, 453-472.
Eric Ghysels, Arthur Sinko and Rossen Valkanov,(2007). MIDAS Regressions: Further Results and New Directions, Econometric Reviews, 26, 1.
G. WILLIAM SCHWERT, (1989). Why Does Stock Market Volatility Change Over. Time?, The Journal of Finance, 44, 1115-1153.
Gerasimos Georgiou Rompotis,(2006).Evaluating the Performance and the Trading. Characteristics of Ishares, http： //ssrn.com/abstract=946732.
Han Liyan, WanLi and XuYang,(2020). Can the Baltic Dry Index predict foreign. exchange rates?, Finance Research Letters, 32, 101157.
James M. Poterba and John B. Shoven,(2002). Exchange-Traded Funds: A New Investment Option for Taxable Investors. American Economic Review,92, No. 2, 422-427.
Jiayi He and Pengjian Shang,(2017).Comparison of transfer entropy methods for. financial time series, Physica A: Statistical Mechanics and its Applications, 482, 772-785.
Joel T. Harper,Jeff Madura and Oliver Schnusenberg,(2006). Performance.
Joëlle Miffre,(2004). Country-specific ETFs: An efficient approach to global asset. allocation, Journal of Asset Management, 8(2), 112-122.
Kavussanos, M.G.,(2003).Time Varying Risks Among Segments of the Tanker Freight Markets, Maritime Economics & Logistics, 5, 227-250.
Kostas Andriosopoulos, Michael Doumpos, Nikos C. Papapostolou and Panos K. Pouliasis,(2013).Portfolio optimization and index tracking for the shipping stock and freight markets using evolutionary algorithms, Transportation Research Part E: Logistics and Transportation Review, 52, 16-34.
Manolis G Kavussanos and Ilias D Visvikis,(2004).Market interactions in returns and volatilities between spot and forward shipping freight markets, Journal of Banking & Finance, 28, 8, Pages 2015-2049.
Manolis G. Kavussanos, Ilias D. Visvikis and David Menachof,(2004).The Unbiasedness Hypothesis in the Freight Forward Market: Evidence from Cointegration Tests, Review of Derivatives Research,7,241–266.
Maria Elisabete Duarte Neves, Carla Manuela Fernandes and Pedro Coimbra Martins,(2019).Are ETFs good vehicles for diversification? New evidence for critical investment periods, Borsa Istanbul Review, 19,2, Pages 149-157.
Mary Schmid Daugherty and Thadavillil Jithendranathan,(2015). A study of linkages. between frontier markets and the U.S. equity markets using multivariate GARCH and transfer entropy, Journal of Multinational Financial Management, 32–33, 95-115.
Nikos C.Papapostolou, Panos K.Pouliasis, Nikos K.Nomikos and. IoannisKyriakou.(2016). Shipping investor sentiment and international stock return predictability, Transportation Research Part E: Logistics and Transportation Review, 96, 81-94.
PanLiu, Dmitry Vedenov and Gabriel J.Power,(2020).Commodity financialization and sector ETFs: Evidence from crude oil futures, Research in International Business and Finance ,51, 101109.
Roar Adland, Fred EspenBenth and SteenKoekebakker,(2018). Multivariate modeling and analysis of regional ocean freight rates,Transportation Research Part E: Logistics and Transportation Review, 113,194-221.
Robert F. Engle, Eric Ghysels, and Bumjean Sohn,(2013). Stock market volatility and.macroeconomic fundamentals, The Review of Economics and Statistics , 95(3), pages 776-797.
Sangheon Shin and GökçeSoydemir,(2010).Exchange-traded funds, persistence in tracking errors and information dissemination. Journal of Multinational Financial Management, 20, 214-234.
Shun Chen, Hilde Meersman and Eddy van de Voorde,(2010).Dynamic interrelationships in returns and volatilities between Capesize and Panamax markets, Maritime Economics & Logistics, 12(1), 65-90.
Thomas Schreiber,(2000). Measuring Information Transfer, Phys. Rev. Lett., 85, 461–464.
Yu Wei, Qianwen Yu, Jing Liu and YangCao,(2018). Hot money and China’s stock. market volatility: Further evidence using the GARCH–MIDAS model, Physica A: Statistical Mechanics and its Applications, 492,923-930.
Yu You and Xiaochun Liu,(2020).Forecasting short-run exchange rate volatility with. monetary fundamentals: A GARCH-MIDAS approach, Journal of Banking & Finance, 116, 105849.
YutingGong, Kevin X.Li, Shu-LingChen and WenmingShic,(2020). Contagion risk. between the shipping freight and stock markets: Evidence from the recent US-China trade war, Transportation Research Part E: Logistics and Transportation Review, 136, 101900.
Zhongbao Zhou, Zhangyan Fu, Yong Jiang, Ximei Zeng and Ling Linc,(2020).Can. economic policy uncertainty predict exchange rate volatility? New evidence from the GARCH-MIDAS model, Finance Research Letters,Available online 12.
描述 碩士
國立政治大學
經濟學系
107258040
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0107258040
資料類型 thesis
dc.contributor.advisor 林靖<br>蕭明福zh_TW
dc.contributor.author (Authors) 林泳孜zh_TW
dc.contributor.author (Authors) Lin, Yung-Tzuen_US
dc.creator (作者) 林泳孜zh_TW
dc.creator (作者) Lin, Yung-Tzuen_US
dc.date (日期) 2020en_US
dc.date.accessioned 2-Sep-2020 12:47:14 (UTC+8)-
dc.date.available 2-Sep-2020 12:47:14 (UTC+8)-
dc.date.issued (上傳時間) 2-Sep-2020 12:47:14 (UTC+8)-
dc.identifier (Other Identifiers) G0107258040en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/131791-
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) 107258040zh_TW
dc.description.abstract (摘要) 本研究使用全球海運指數基金(Invesco Shipping ETF; SEA ETF)所追蹤的指數推進Lin et al.（2019）研究成果，強調貨運市場和匯率市場和股價市場間的動態相關性，探討SEA ETF與航運市場和其他金融市場間的傳導聯繫。透過使用傳遞熵（Transfer Entropy）與GARCH-MIDAS實證分析研究SEA ETF的信息流之方向及傳遞效果，以探討其外溢效果（spillover effect）與路徑，提供投資者對其市場預測與投資組合有更多的見解，近一步改善風險管理，特別是發生金融衝擊的動盪時期。最後實證分析結果顯示SEA ETF在大部分時期與國際金融變數呈現顯著性相關，透過傳遞熵實證結果判定領先與落後指標，預測國際金融變數之未來走勢。zh_TW
dc.description.abstract (摘要) This study uses Invesco Shipping ETF (SEA ETF) to advance the research results of Lin et al. (2019), emphasizes the dynamic associativity between the freight market, the exchange rate market and the stock price market, and analysis the relationship between the SEA ETF, shipping market and other financial markets. Through the adaptation of Transfer Entropy and GARCH-MIDAS, this study illustrates the direction and transmission effect of the SEA ETF information flow. It passed to explore its spillover effect and path; provide investors with more insights to the market forecasts and investment portfolios, and improve risk management, especially during the financial crisis. The final empirical analysis results reveal that the SEA ETF is significantly correlated with global economic variables in most of the period. The results of Transfer Entropy are used for determining the leading and lagging indicators and predicting the future trend of international financial variables.en_US
dc.description.tableofcontents 第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的 3
第三節 研究結構 5
第二章 文獻回顧 7
第一節 ETF文獻回顧 7
第二節 航運金融相關文獻回顧 12
第三節 傳遞熵文獻回顧 16
第四節 GARCH-MIDAS文獻回顧 18
第三章 研究方法 21
第一節 資料說明 21
第二節 傳遞熵（Transfer Entropy）模型 30
第三節 GARCH-MIDAS MODEL 32
第四章 實證結果 34
第一節 數據之敘述性統計 34
第二節 單根檢驗 37
第三節 ARCH效果檢定 40
第四節 傳遞熵（Transfer Entropy）之實證分析 42
第五節 GARCH-MIDAS之實證分析 54
第五章 結論與建議 62
第六章 參考文獻 65		zh_TW
dc.format.extent 3733066 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0107258040en_US
dc.subject (關鍵詞) SEA ETFzh_TW
dc.subject (關鍵詞) 外溢效果zh_TW
dc.subject (關鍵詞) 傳遞熵zh_TW
dc.subject (關鍵詞) GARCH-MIDASzh_TW
dc.subject (關鍵詞) 貨運市場zh_TW
dc.subject (關鍵詞) SEA ETFen_US
dc.subject (關鍵詞) Volatility spilloversen_US
dc.subject (關鍵詞) Transfer Entropyen_US
dc.subject (關鍵詞) GARCH-MIDASen_US
dc.subject (關鍵詞) Shipping freight marketsen_US
dc.title (題名) 金融事件衝擊時國際金融變數對航運基金SEA之影響--運用傳遞熵與GARCH-MIDAS模型zh_TW
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) Amir H. Alizadeh, Roar Os Ådland and Steen Koekebakker,(2007). Predictive power and unbiasedness of implied forward charter rates, Journal of Forecasting, 26, 385–403.
Amir H.Alizadeh and GulnurMuradoglu,(2014).Stock market efficiency and. international shipping-market information, Journal of International Financial Markets, Institutions and Money, 33, 445-461.
Arthur J.Lin, Hai YenChang and Jung LiehHsiao,(2019).Does the Baltic Dry Index. drive volatility spillovers in the commodities, currency, or stock markets?, Transportation Research Part E: Logistics and Transportation Review, 127, 265-283.
Aviral Kumar Tiwari,Claudiu Tiberiu Albulescu and Seong-Min Yoon,(2017). A. multifractal detrended fluctuation analysis of financial market efficiency: Comparison using Dow Jones sector ETF indices, Physica A: Statistical Mechanics and its Applications, 483, 182-192.
Basel Awartani and Aktham IssaMaghyere,(2013).Dynamic spillovers between oil. and stock markets in the Gulf Cooperation Council Countries, Energy Economics, 36, 28-42.
Ben R.Marshall,Nhut H,Nguyen and NuttawatVisaltanachotic,(2013). ETF arbitrage: Intraday evidence, Journal of Banking & Finance, 37, Issue 9, 3486-3498.
Brunnermeier, Markus K,(2005). Information Leakage and Market Efficiency, Review. of Financial Studies, 18, 417-457.
C. E. SHANNON,(1948).A Mathematical Theory of Communication, The Bell. System Technical Journal, 27, 379–423, 623–656.
Chia-Lin Chang, Chia-Ping Liu and Michael McAleer,(2019). Volatility spillovers for spot, futures, and ETF prices in agriculture and energy, Energy Economics, 81,779–792.
comparison between exchange-traded funds and closed-end country funds, Journal of International Financial Markets, Institutions and Money, 16, Issue 2, 104-122.
David R Gallagher and Reuben Segara,(2005). The performance and trading. characteristics of exchange-traded funds, Journal of Investment Strategies, 1.
Dimitris A. Tsouknidis,(2016).Dynamic volatility spillovers across shipping freight markets, Transportation Research Part E: Logistics and Transportation Review, 91, 90-111.
Edwin J. Elton, Martin J. Gruber, George Comer and Kai Li(2002). Spiders: Where. are the bugs?, The Journal of Business, 75, 453-472.
Eric Ghysels, Arthur Sinko and Rossen Valkanov,(2007). MIDAS Regressions: Further Results and New Directions, Econometric Reviews, 26, 1.
G. WILLIAM SCHWERT, (1989). Why Does Stock Market Volatility Change Over. Time?, The Journal of Finance, 44, 1115-1153.
Gerasimos Georgiou Rompotis,(2006).Evaluating the Performance and the Trading. Characteristics of Ishares, http： //ssrn.com/abstract=946732.
Han Liyan, WanLi and XuYang,(2020). Can the Baltic Dry Index predict foreign. exchange rates?, Finance Research Letters, 32, 101157.
James M. Poterba and John B. Shoven,(2002). Exchange-Traded Funds: A New Investment Option for Taxable Investors. American Economic Review,92, No. 2, 422-427.
Jiayi He and Pengjian Shang,(2017).Comparison of transfer entropy methods for. financial time series, Physica A: Statistical Mechanics and its Applications, 482, 772-785.
Joel T. Harper,Jeff Madura and Oliver Schnusenberg,(2006). Performance.
Joëlle Miffre,(2004). Country-specific ETFs: An efficient approach to global asset. allocation, Journal of Asset Management, 8(2), 112-122.
Kavussanos, M.G.,(2003).Time Varying Risks Among Segments of the Tanker Freight Markets, Maritime Economics & Logistics, 5, 227-250.
Kostas Andriosopoulos, Michael Doumpos, Nikos C. Papapostolou and Panos K. Pouliasis,(2013).Portfolio optimization and index tracking for the shipping stock and freight markets using evolutionary algorithms, Transportation Research Part E: Logistics and Transportation Review, 52, 16-34.
Manolis G Kavussanos and Ilias D Visvikis,(2004).Market interactions in returns and volatilities between spot and forward shipping freight markets, Journal of Banking & Finance, 28, 8, Pages 2015-2049.
Manolis G. Kavussanos, Ilias D. Visvikis and David Menachof,(2004).The Unbiasedness Hypothesis in the Freight Forward Market: Evidence from Cointegration Tests, Review of Derivatives Research,7,241–266.
Maria Elisabete Duarte Neves, Carla Manuela Fernandes and Pedro Coimbra Martins,(2019).Are ETFs good vehicles for diversification? New evidence for critical investment periods, Borsa Istanbul Review, 19,2, Pages 149-157.
Mary Schmid Daugherty and Thadavillil Jithendranathan,(2015). A study of linkages. between frontier markets and the U.S. equity markets using multivariate GARCH and transfer entropy, Journal of Multinational Financial Management, 32–33, 95-115.
Nikos C.Papapostolou, Panos K.Pouliasis, Nikos K.Nomikos and. IoannisKyriakou.(2016). Shipping investor sentiment and international stock return predictability, Transportation Research Part E: Logistics and Transportation Review, 96, 81-94.
PanLiu, Dmitry Vedenov and Gabriel J.Power,(2020).Commodity financialization and sector ETFs: Evidence from crude oil futures, Research in International Business and Finance ,51, 101109.
Roar Adland, Fred EspenBenth and SteenKoekebakker,(2018). Multivariate modeling and analysis of regional ocean freight rates,Transportation Research Part E: Logistics and Transportation Review, 113,194-221.
Robert F. Engle, Eric Ghysels, and Bumjean Sohn,(2013). Stock market volatility and.macroeconomic fundamentals, The Review of Economics and Statistics , 95(3), pages 776-797.
Sangheon Shin and GökçeSoydemir,(2010).Exchange-traded funds, persistence in tracking errors and information dissemination. Journal of Multinational Financial Management, 20, 214-234.
Shun Chen, Hilde Meersman and Eddy van de Voorde,(2010).Dynamic interrelationships in returns and volatilities between Capesize and Panamax markets, Maritime Economics & Logistics, 12(1), 65-90.
Thomas Schreiber,(2000). Measuring Information Transfer, Phys. Rev. Lett., 85, 461–464.
Yu Wei, Qianwen Yu, Jing Liu and YangCao,(2018). Hot money and China’s stock. market volatility: Further evidence using the GARCH–MIDAS model, Physica A: Statistical Mechanics and its Applications, 492,923-930.
Yu You and Xiaochun Liu,(2020).Forecasting short-run exchange rate volatility with. monetary fundamentals: A GARCH-MIDAS approach, Journal of Banking & Finance, 116, 105849.
YutingGong, Kevin X.Li, Shu-LingChen and WenmingShic,(2020). Contagion risk. between the shipping freight and stock markets: Evidence from the recent US-China trade war, Transportation Research Part E: Logistics and Transportation Review, 136, 101900.
Zhongbao Zhou, Zhangyan Fu, Yong Jiang, Ximei Zeng and Ling Linc,(2020).Can. economic policy uncertainty predict exchange rate volatility? New evidence from the GARCH-MIDAS model, Finance Research Letters,Available online 12.		zh_TW
dc.identifier.doi (DOI) 10.6814/NCCU202001713en_US