本研究欲探討在COVID-19疫情下，影響海運運價的因素是否有改變。因此，選取佛雷格斯波羅的海全球貨櫃指數 (FBX)以及波羅的海散裝指數 (BDI)分別作為貨櫃運輸及散裝運輸的運費指標，並選取各項經濟變數，包括布蘭特原油價格、西德州原油價格、國際鋼鐵價格指數、全球GDP和全球實際經濟活動指數，另外，也採用每日新增病例數作為代表COVID-19疫情發展程度的指標，再對變數做單根檢定 (Unit Root Test)、格蘭傑因果關係檢定 (Granger Causality Test)、共整合檢定 (Co-integration)及誤差修正模型 (Vector Error Correction Model, VECM)，探討各項經濟變數及疫情對海運運價的因果關係。
實證分析的結果發現，疫情發生前影響海運運價的因素，疫情發生之後都失去其因果關係及影響力，原油價格尤為明顯。而從分析結果也可以發現COVID-19的每日新增病例數與海運運價具有因果關係，且對於貨櫃航運的運價比起散裝航運的運價更為明顯。COVID-19疫情改變人類的生活，也衝擊了海運市場，疫情爆發之初，雖然產生短暫的負面衝擊，在2020年中過後，市場出現需求大於供給的情況，造成運價大漲，COVID-19疫情反而對海運運價有正向的影響力。因此，影響運價的因素不會持續不變，當重大事件發生時，如疫情，會使得原本影響運價的因素失去其與運價的因果關係，而與事件相關的數據則會與運價產生因果關係。

This study intends to explore whether the factors affecting shipping freight rates have changed under the COVID-19 pandemic. We selected the Freightos Baltic Global Container Index (FBX) and Baltic Dry Index (BDI) as the freight indicators for container transportation and bulk transportation respectively. We also selected various economic variables, including Brent crude oil prices, West Texas Intermediate, International Steel Price Index, Global GDP and Index of Global Real Economic Activity. In addition, the number of new confirmed cases per day is also used as an indicator to represent the degree of development of the COVID-19 pandemic. In this study, we use Granger Causality Test, Co-integration Test and Vector Error Correction Model (VECM) to explore the causal relationship of various economic variables and epidemics on shipping freight rates.
The results of the empirical analysis found that the factors affecting shipping freight rates before the outbreak of the epidemic have lost their causality and influence after the outbreak, especially the price of crude oil. From the analysis results, it can also be found that the daily number of new cases of COVID-19 has a causal relationship with shipping freight rates, and the relationship with the container shipping freight rates is more obvious than the relationship with bulk shipping freight rate. The COVID-19 epidemic has changed people’s lives and also impacted the shipping market. At the beginning of the epidemic, there was a short-term negative impact. After mid-2020, the market demand exceeded supply, resulting in a sharp increase in freight rates. The epidemic has a positive influence on ocean freight rates. Thus, the factors affecting the freight rate will not remain unchanged. When a major event occurs, such as an epidemic, the factors that originally affected the freight rate will lose its causal relationship with the freight rate, and the data related to the event will be related to the freight rate.

第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 4
1.3 研究流程 4
第二章 文獻回顧 6
2.1 影響海運運價的因素 6
2.2時間序列研究方法 10
第三章 研究方法 13
3.1 單根檢定 14
3.1.1 Augmented Dickey-Fuller (ADF) 單根檢定 15
3.1.2 Phillips-Perron (PP)單根檢定 16
3.1.3 Kwiatkowski-Phillips-Schmidt-Shin (KPSS)單根檢定 17
3.2 共整合檢定 (Co-integration Test) 18
3.3 向量誤差修正模型 (Vector Error Correction Model, VECM) 19
3.4 向量自我迴歸模型 (Vector Autoregressive Model, VAR Model) 20
3.5 格蘭傑因果關係檢定 (Granger Causality Test) 21
第四章 實證研究 23
4.1 資料分析 23
4.1.1 佛雷格斯波羅的海全球貨櫃指數 23
4.1.2波羅的海散裝指數 23
4.1.3布蘭特原油價格 23
4.1.4西德州原油價格 24
4.1.5國際鋼鐵價格指數 24
4.1.6全球國內生產毛額總值 24
4.1.7全球實際經濟活動指數 24
4.1.8每日新增病例數 25
4.2單根檢定(Unit Root Test) 26
4.2.1 Augmented Dickey-Fuller (ADF) 單根檢定 26
4.2.2 Phillips-Perron (PP)單根檢定 31
4.2.3 Kwiatkowski-Phillips-Schmidt-Shin (KPSS)單根檢定 34
4.3 共整合檢定(Cointegration Test) 37
4.3.1 疫情發生前 (2019年)各變數間之Johansen共整合檢定 37
4.3.2 疫情發生後 (2020年)各變數間之Johansen共整合檢定 39
4.4向量修正誤差模型(VECM) 41
4.4.1 疫情前 (2019年)各變數與運價指數之VECM分析 41
4.4.2 疫情後(2020年)各變數與運價指數之VECM分析 44
4.4.3 疫情前後各變數與運價指數之VECM結果比較 46
4.5 格蘭傑因果關係檢定(Granger Causality Test) 47
第五章 結論與建議 51
5.1 結論 51
5.2 後續研究建議 54
參考資料 55
中文文獻 55
英文文獻 56

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