1. NCCU Academic Hub
  2. Publications
  3. College of Social Science
  4. Theses
  5. Item

Publications-Theses

Article View/Open

Publication Export

Google ScholarTM

NCCU Library

Citation Infomation

Related Publications in TAIR

題名 金融危機下散裝海運產業波動傳導對航運類股之影響
The Impact of Dry Bulk Shipping Industry Volatility Diffusion on Shipping Stock Index in Financial Crisis
作者 王守杰
Wang, Shou Jie
貢獻者 林靖
Lin, Jing
王守杰
Wang, Shou Jie
關鍵詞 BEKK-GARCH模型
傳遞熵
波羅的海乾散貨運價指數
金融傳導
金融海嘯
日期 2016
上傳時間 1-Jul-2016 15:23:27 (UTC+8)
摘要 本研究用金融傳導的角度，從散裝海運產業切入，利用標普高盛商品指數、加權遠期運費協議指數、波羅的海運費指數、道瓊全球航運指數以及美元指數，以傳遞熵與BEKK-GARCH模型，探討2008年3月至2016年3月之散裝海運產業金融傳導因子，在多次金融危機中，散裝海運產業金融傳導因子的領先落後關係、短期報酬外溢效果與長期波動傳遞效果，以及對航運類股之影響。
本研究成果可從投資策略與經濟意涵兩方面呈現，在投資策略上，根據實證結果，在金融危機期間，資訊從道瓊全球航運指數流向波羅的海乾散貨運價指數，再流向加權遠期運費協議指數，代表股票市場領先運費市場，而運費市場又領先遠期運費協議市場，而每個期間的加權遠期運費協議指數對波羅的海乾散貨運價指數皆為正向顯著關係，波羅的海乾散貨運價指數與道瓊全球航運指數間皆為雙邊正向顯著關係，本研究建議預測波羅的海乾散貨運價指數的散裝海運產業業者與投資人，可以道瓊全球航運指數與加權遠期運費協議指數作為先行指標。
在經濟意涵方面，根據實證結果，金融危機期間，金融市場動盪程度提高，連帶影響散裝海運運價價格波動劇烈，使得散裝海運產業業者與投資人的避險需求提升，由於波羅的海乾散貨運價指數為散裝海運產業業者的每日報價，並非金融市場交易之結果，故散裝海運產業業者與投資人可以參考商品市場、股票市場、外匯市場及運費市場的資訊進行避險操作。
參考文獻 一、中文部分
1. 林宏銘 (2010)，「美元、股票市場、債券市場及商品市場之互動關係研究」，國立成功大學財務金融研究所碩士論文。
2. 陳玉樹 (2011)，「原物料指數與股市、匯市之關聯性的研究」，國立政治大學金融研究所碩士論文。
3. 張瀞之、劉錫謙 (2012)，「時間序列方法探討波羅的海綜合運價指數與運輸類股之研究─以美國與臺灣為研究對象」，台灣銀行季刊，第六十一卷第二期，頁191∼207。
4. 陳永順 (2012)，「散裝船經營學-理論與實務」，麗文文化事業。
5. 蕭堯仁 (2013)，「散裝船運價與貨櫃船運價領先落後關係與波動傳遞效果之研究」，國立臺灣海洋大學航運管理學系博士論文。
二、英文部分
1. Alizadeh, A. H. (2013). Trading volume and volatility in the shipping forward freight market. Transportation Research Part E: Logistics and Transportation Review, 49(1), 250-265.
2. Alizadeh, A. H., Kappou, K., Tsouknidis, D., & Visvikis, I. (2015). Liquidity effects and FFA returns in the international shipping derivatives market. Transportation Research Part E: Logistics and Transportation Review, 76, 58-75.
3. Andriosopoulos, K., Doumpos, M., Papapostolou, N. C., & Pouliasis, P. K. (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.
4. Antonakakis, N., & Kizys, R. (2015). Dynamic spillovers between commodity and currency markets. International Review of Financial Analysis, 41, 303-319.
5. Alter, A., & Beyer, A. (2014). The dynamics of spillover effects during the European sovereign debt turmoil. Journal of Banking & Finance, 42, 134-153.
6. Batchelor, R., Alizadeh, A., & Visvikis, I. (2007). Forecasting spot and forward prices in the international freight market. International Journal of Forecasting, 23(1), 101-114.
7. Boltzmann, L. (1866). Über die mechanische Bedeutung des zweiten Hauptsatzes der Wärmetheorie:(vorgelegt in der Sitzung am 8. Februar 1866). Staatsdruckerei.
8. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of econometrics, 31(3), 307-327.
9. Chistè, C., & Van Vuuren, G. (2014). Investigating the cyclical behaviour of the dry bulk shipping market. Maritime Policy & Management, 41(1), 1-19.
10. Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the Econometric Society, 987-1007.
11. Daugherty, M. S., & Jithendranathan, T. (2015). A study of linkages between frontier markets and the US equity markets using multivariate GARCH and transfer entropy. Journal of Multinational Financial Management, 32, 95-115.
12. Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American statistical association, 74(366a), 427-431.
13. Dimpfl, T., & Peter, F. J. (2014). The impact of the financial crisis on transatlantic information flows: An intraday analysis. Journal of International Financial Markets, Institutions and Money, 31, 1-13.
14. Engle, R. F., & Kroner, K. F. (1995). Multivariate simultaneous generalized ARCH. Econometric theory, 11(01), 122-150.
15. Goulielmos, A. M., & Psifia, M. E. (2011). Forecasting short-term freight rate cycles: do we have a more appropriate method than a normal distribution?. Maritime Policy & Management, 38(6), 645-672.
16. Gusanu, A., Merika, A. A., and Triantafyllou, A. (2012). Is There a Lead-lag Relationship between Freight Rates and Stock Returns in the Drybulk Shipping Industry. Unpublished Manuscript, 1-34.
17. Li, J., Liang, C., Zhu, X., Sun, X., & Wu, D. (2013). Risk contagion in Chinese banking industry: A Transfer Entropy-based analysis. Entropy, 15(12), 5549-5564.
18. Jin, X., Lin, S. X., & Tamvakis, M. (2012). Volatility transmission and volatility impulse response functions in crude oil markets. Energy Economics, 34(6), 2125-2134.
19. Jin, X., & An, X. (2016). Global financial crisis and emerging stock market contagion: A volatility impulse response function approach. Research in International Business and Finance, 36, 179-195.
20. Kavussanos, M. G., & Nomikos, N. K. (2003). Price discovery, causality and forecasting in the freight futures market. Review of Derivatives Research, 6(3), 203-230.
21. Kavussanos, M. G., & Visvikis, I. D. (2004). Market interactions in returns and volatilities between spot and forward shipping freight markets. Journal of Banking & Finance, 28(8), 2015-2049.
22. Kavussanos, M., Visvikis, I., & Dimitrakopoulos, D. (2010). Information linkages between Panamax freight derivatives and commodity derivatives markets. Maritime Economics & Logistics, 12(1), 91-110.
23. Kavussanos, M. G., Visvikis, I. D., & Dimitrakopoulos, D. N. (2014). Economic spillovers between related derivatives markets: The case of commodity and freight markets. Transportation Research Part E: Logistics and Transportation Review, 68, 79-102.
24. Kou, Y., & Luo, M. (2015). Modelling the Relationship between Ship Price and Freight Rate with Structural Changes. Journal of Transport Economics and Policy (JTEP), 49(2), 276-294.
25. Kullback, S. (1968). Information theory and statistics. Courier Corporation.
26. Merikas, A. G., Merika, A. A., & Koutroubousis, G. (2008). Modelling the investment decision of the entrepreneur in the tanker sector: choosing between a second-hand vessel and a newly built one. Maritime Policy & Management, 35(5), 433-447.
27. Morales, L., & Andreosso-O`Callaghan, B. (2014). The global financial crisis: World market or regional contagion effects?. International Review of Economics & Finance, 29, 108-131.
28. Olson, E., Vivian, A. J., & Wohar, M. E. (2014). The relationship between energy and equity markets: Evidence from volatility impulse response functions. Energy Economics, 43, 297-305.
29. Paas, T., & Kuusk, A. (2012). Contagion of financial crises: what does the empirical evidence show?. Baltic Journal of Management, 7(1), 25-48.
30. Phillips, P. C., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335-346.
31. Shannon, C. E. (1951). Prediction and entropy of printed English. Bell system technical journal, 30(1), 50-64.
32. Scarsi, R. (2007). The bulk shipping business: market cycles and shipowners’ biases. Maritime Policy & Management, 34(6), 577-590.
33. Said, S. E., & Dickey, D. A. (1984). Testing for unit roots in autoregressive-moving average models of unknown order. Biometrika, 71(3), 599-607.
34. Schreiber, T. (2000). Measuring information transfer. Physical review letters, 85(2), 461.
35. Stopford, M. (2005). China in Transition: Its impact on shipping in the last decade and the next. Marintec, China, 6-9.
36. Stopford, M. (2009). Maritime Economics 3e. Routledge.
37. Thorsen, I. S. (2010). Dry bulk shipping and business cycles (Doctoral dissertation, Norges Handelshøyskole).
38. Tola, A., & Wälti, S. (2015). Deciphering financial contagion in the euro area during the crisis. The Quarterly Review of Economics and Finance, 55, 108-123.
39. Tsolakis, S. D., Cridland, C., & Haralambides, H. E. (2003). Econometric modelling of second-hand ship prices. Maritime Economics & Logistics, 5(4), 347-377.
40. Wayne, A. F. (1976). Introduction to statistical time series.
41. UNCTAD. (2015). Review of maritime transport.
42. Yu, T. H. E., Bessler, D. A., & Fuller, S. W. (2007). Price dynamics in US grain and freight markets. Canadian Journal of Agricultural Economics/Revue canadienne d`agroeconomie, 55(3), 381-397.
描述 碩士
國立政治大學
經濟學系
103258001
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0103258001
資料類型 thesis
dc.contributor.advisor 林靖zh_TW
dc.contributor.advisor Lin, Jingen_US
dc.contributor.author (Authors) 王守杰zh_TW
dc.contributor.author (Authors) Wang, Shou Jieen_US
dc.creator (作者) 王守杰zh_TW
dc.creator (作者) Wang, Shou Jieen_US
dc.date (日期) 2016en_US
dc.date.accessioned 1-Jul-2016 15:23:27 (UTC+8)-
dc.date.available 1-Jul-2016 15:23:27 (UTC+8)-
dc.date.issued (上傳時間) 1-Jul-2016 15:23:27 (UTC+8)-
dc.identifier (Other Identifiers) G0103258001en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/98639-
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) 103258001zh_TW
dc.description.abstract (摘要) 本研究用金融傳導的角度，從散裝海運產業切入，利用標普高盛商品指數、加權遠期運費協議指數、波羅的海運費指數、道瓊全球航運指數以及美元指數，以傳遞熵與BEKK-GARCH模型，探討2008年3月至2016年3月之散裝海運產業金融傳導因子，在多次金融危機中，散裝海運產業金融傳導因子的領先落後關係、短期報酬外溢效果與長期波動傳遞效果，以及對航運類股之影響。
本研究成果可從投資策略與經濟意涵兩方面呈現，在投資策略上，根據實證結果，在金融危機期間，資訊從道瓊全球航運指數流向波羅的海乾散貨運價指數，再流向加權遠期運費協議指數，代表股票市場領先運費市場，而運費市場又領先遠期運費協議市場，而每個期間的加權遠期運費協議指數對波羅的海乾散貨運價指數皆為正向顯著關係，波羅的海乾散貨運價指數與道瓊全球航運指數間皆為雙邊正向顯著關係，本研究建議預測波羅的海乾散貨運價指數的散裝海運產業業者與投資人，可以道瓊全球航運指數與加權遠期運費協議指數作為先行指標。
在經濟意涵方面，根據實證結果，金融危機期間，金融市場動盪程度提高，連帶影響散裝海運運價價格波動劇烈，使得散裝海運產業業者與投資人的避險需求提升，由於波羅的海乾散貨運價指數為散裝海運產業業者的每日報價，並非金融市場交易之結果，故散裝海運產業業者與投資人可以參考商品市場、股票市場、外匯市場及運費市場的資訊進行避險操作。		zh_TW
dc.description.tableofcontents 第壹章 緒論 1
第一節 研究動機 1
第二節 研究目的 3
第三節 研究限制 4
第四節 研究流程 5
第貳章 散裝海運產業與市場特性 6
第一節 散裝海運產業與景氣循環 6
第二節 散裝海運市場與特性分析 9
第三節 散裝海運金融市場分析 13
第四節 散裝海運產業金融傳導因子 17
第参章 文獻探討 20
第一節 金融傳導之文獻回顧 20
第二節 金融傳導之方法論比較 23
第三節 傳遞熵之文獻探討 25
第四節 BEKK模型之相關文獻 27
第肆章 研究設計 30
第一節 變數定義與衡量 30
第二節 研究方法 34
第三節 實證流程 37
第四節 研究假說與模型設定 38
第伍章 實證結果與分析 43
第一節 資料描述 44
第二節 敘述性統計與單根檢定 48
第三節 傳遞熵實證結果 49
第四節 BEKK-GARCH模型實證結果 56
第陸章 結論與建議 70
第一節 研究成果與發現 70
第二節 經濟意涵與建議 72		zh_TW
dc.format.extent 1368533 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0103258001en_US
dc.subject (關鍵詞) BEKK-GARCH模型zh_TW
dc.subject (關鍵詞) 傳遞熵zh_TW
dc.subject (關鍵詞) 波羅的海乾散貨運價指數zh_TW
dc.subject (關鍵詞) 金融傳導zh_TW
dc.subject (關鍵詞) 金融海嘯zh_TW
dc.title (題名) 金融危機下散裝海運產業波動傳導對航運類股之影響zh_TW
dc.title (題名) The Impact of Dry Bulk Shipping Industry Volatility Diffusion on Shipping Stock Index in Financial Crisisen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) 一、中文部分
1. 林宏銘 (2010)，「美元、股票市場、債券市場及商品市場之互動關係研究」，國立成功大學財務金融研究所碩士論文。
2. 陳玉樹 (2011)，「原物料指數與股市、匯市之關聯性的研究」，國立政治大學金融研究所碩士論文。
3. 張瀞之、劉錫謙 (2012)，「時間序列方法探討波羅的海綜合運價指數與運輸類股之研究─以美國與臺灣為研究對象」，台灣銀行季刊，第六十一卷第二期，頁191∼207。
4. 陳永順 (2012)，「散裝船經營學-理論與實務」，麗文文化事業。
5. 蕭堯仁 (2013)，「散裝船運價與貨櫃船運價領先落後關係與波動傳遞效果之研究」，國立臺灣海洋大學航運管理學系博士論文。
二、英文部分
1. Alizadeh, A. H. (2013). Trading volume and volatility in the shipping forward freight market. Transportation Research Part E: Logistics and Transportation Review, 49(1), 250-265.
2. Alizadeh, A. H., Kappou, K., Tsouknidis, D., & Visvikis, I. (2015). Liquidity effects and FFA returns in the international shipping derivatives market. Transportation Research Part E: Logistics and Transportation Review, 76, 58-75.
3. Andriosopoulos, K., Doumpos, M., Papapostolou, N. C., & Pouliasis, P. K. (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.
4. Antonakakis, N., & Kizys, R. (2015). Dynamic spillovers between commodity and currency markets. International Review of Financial Analysis, 41, 303-319.
5. Alter, A., & Beyer, A. (2014). The dynamics of spillover effects during the European sovereign debt turmoil. Journal of Banking & Finance, 42, 134-153.
6. Batchelor, R., Alizadeh, A., & Visvikis, I. (2007). Forecasting spot and forward prices in the international freight market. International Journal of Forecasting, 23(1), 101-114.
7. Boltzmann, L. (1866). Über die mechanische Bedeutung des zweiten Hauptsatzes der Wärmetheorie:(vorgelegt in der Sitzung am 8. Februar 1866). Staatsdruckerei.
8. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of econometrics, 31(3), 307-327.
9. Chistè, C., & Van Vuuren, G. (2014). Investigating the cyclical behaviour of the dry bulk shipping market. Maritime Policy & Management, 41(1), 1-19.
10. Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the Econometric Society, 987-1007.
11. Daugherty, M. S., & Jithendranathan, T. (2015). A study of linkages between frontier markets and the US equity markets using multivariate GARCH and transfer entropy. Journal of Multinational Financial Management, 32, 95-115.
12. Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American statistical association, 74(366a), 427-431.
13. Dimpfl, T., & Peter, F. J. (2014). The impact of the financial crisis on transatlantic information flows: An intraday analysis. Journal of International Financial Markets, Institutions and Money, 31, 1-13.
14. Engle, R. F., & Kroner, K. F. (1995). Multivariate simultaneous generalized ARCH. Econometric theory, 11(01), 122-150.
15. Goulielmos, A. M., & Psifia, M. E. (2011). Forecasting short-term freight rate cycles: do we have a more appropriate method than a normal distribution?. Maritime Policy & Management, 38(6), 645-672.
16. Gusanu, A., Merika, A. A., and Triantafyllou, A. (2012). Is There a Lead-lag Relationship between Freight Rates and Stock Returns in the Drybulk Shipping Industry. Unpublished Manuscript, 1-34.
17. Li, J., Liang, C., Zhu, X., Sun, X., & Wu, D. (2013). Risk contagion in Chinese banking industry: A Transfer Entropy-based analysis. Entropy, 15(12), 5549-5564.
18. Jin, X., Lin, S. X., & Tamvakis, M. (2012). Volatility transmission and volatility impulse response functions in crude oil markets. Energy Economics, 34(6), 2125-2134.
19. Jin, X., & An, X. (2016). Global financial crisis and emerging stock market contagion: A volatility impulse response function approach. Research in International Business and Finance, 36, 179-195.
20. Kavussanos, M. G., & Nomikos, N. K. (2003). Price discovery, causality and forecasting in the freight futures market. Review of Derivatives Research, 6(3), 203-230.
21. Kavussanos, M. G., & Visvikis, I. D. (2004). Market interactions in returns and volatilities between spot and forward shipping freight markets. Journal of Banking & Finance, 28(8), 2015-2049.
22. Kavussanos, M., Visvikis, I., & Dimitrakopoulos, D. (2010). Information linkages between Panamax freight derivatives and commodity derivatives markets. Maritime Economics & Logistics, 12(1), 91-110.
23. Kavussanos, M. G., Visvikis, I. D., & Dimitrakopoulos, D. N. (2014). Economic spillovers between related derivatives markets: The case of commodity and freight markets. Transportation Research Part E: Logistics and Transportation Review, 68, 79-102.
24. Kou, Y., & Luo, M. (2015). Modelling the Relationship between Ship Price and Freight Rate with Structural Changes. Journal of Transport Economics and Policy (JTEP), 49(2), 276-294.
25. Kullback, S. (1968). Information theory and statistics. Courier Corporation.
26. Merikas, A. G., Merika, A. A., & Koutroubousis, G. (2008). Modelling the investment decision of the entrepreneur in the tanker sector: choosing between a second-hand vessel and a newly built one. Maritime Policy & Management, 35(5), 433-447.
27. Morales, L., & Andreosso-O`Callaghan, B. (2014). The global financial crisis: World market or regional contagion effects?. International Review of Economics & Finance, 29, 108-131.
28. Olson, E., Vivian, A. J., & Wohar, M. E. (2014). The relationship between energy and equity markets: Evidence from volatility impulse response functions. Energy Economics, 43, 297-305.
29. Paas, T., & Kuusk, A. (2012). Contagion of financial crises: what does the empirical evidence show?. Baltic Journal of Management, 7(1), 25-48.
30. Phillips, P. C., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335-346.
31. Shannon, C. E. (1951). Prediction and entropy of printed English. Bell system technical journal, 30(1), 50-64.
32. Scarsi, R. (2007). The bulk shipping business: market cycles and shipowners’ biases. Maritime Policy & Management, 34(6), 577-590.
33. Said, S. E., & Dickey, D. A. (1984). Testing for unit roots in autoregressive-moving average models of unknown order. Biometrika, 71(3), 599-607.
34. Schreiber, T. (2000). Measuring information transfer. Physical review letters, 85(2), 461.
35. Stopford, M. (2005). China in Transition: Its impact on shipping in the last decade and the next. Marintec, China, 6-9.
36. Stopford, M. (2009). Maritime Economics 3e. Routledge.
37. Thorsen, I. S. (2010). Dry bulk shipping and business cycles (Doctoral dissertation, Norges Handelshøyskole).
38. Tola, A., & Wälti, S. (2015). Deciphering financial contagion in the euro area during the crisis. The Quarterly Review of Economics and Finance, 55, 108-123.
39. Tsolakis, S. D., Cridland, C., & Haralambides, H. E. (2003). Econometric modelling of second-hand ship prices. Maritime Economics & Logistics, 5(4), 347-377.
40. Wayne, A. F. (1976). Introduction to statistical time series.
41. UNCTAD. (2015). Review of maritime transport.
42. Yu, T. H. E., Bessler, D. A., & Fuller, S. W. (2007). Price dynamics in US grain and freight markets. Canadian Journal of Agricultural Economics/Revue canadienne d`agroeconomie, 55(3), 381-397.		zh_TW