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| Title: | 金融傳導與海運業之信用風險：網路分析
Financial Contagion and Credit Risk in Shipping Industry: A Network Analysis |
| Authors: | 王紹安 |
| Contributors: | 林靖
何靜嫺
王紹安 |
| Keywords: | 信用風險
海運業
網絡結構
金融傳導
Credit risk
Shipping industry
Network structure
Financial contagion |
| Date: | 2017 |
| Issue Date: | 2017-08-28 11:51:55 (UTC+8) |
| Abstract: | 本文以上下游的網絡結構，來分析海運業信用風險與網絡結構與金融傳導關係，並以Altman Z-score衡量海運公司信用風險，以波動外溢指數估計網絡連接及衡量網絡結構和計算網絡中心度。本文也探討海運產業與其金融傳導因子間的動態外溢現象。本文分析四大目標:首先，探討海運公司是否存在高中心度而不倒的現象，其次，分析在上下游結構的網絡下，海運在整體網絡中的網絡中心度與信用風險的關係，再者，分析上游銀行的網絡中心度是否透過金融傳導影響下游海運公司的信用風險。最後，以DCC-GAECH分析海運產業與其金融傳導因子間的動態外溢現象。
針對四大目標，本文發現海運公司亦存在類似銀行高中心度而不倒的現象，且三種網絡中心度衡量方式中，緊密中心度是唯一顯著影響海運公司信用風險的網絡中心度，且緊密中心度的實證結果之模型解釋力最高，在網絡結構變數中，銀行對海運公司的金融傳導強度對海運公司的信用風險是正向且顯著的影響。其次，在上下游結構的網絡下，緊密中心度仍然是三種中心度中唯一顯著影響海運公司信用風險的網絡中心度，且海運公司的網絡中心度越高則信用風險越低，緊密中心度的實證結果之模型解釋力最高，呈現出與第一個研究目標相同的結果，在網絡結構變數中，銀行對海運公司的金融傳導強度對海運公司的信用風險是正向且顯著的影響。再者，本文發現以中介中心度和緊密中心度的衡量方式，銀行對銀行的網絡中心度透過金融傳導顯著影響下游海運公司的信用風險，以緊密中心度的衡量方式，銀行在整體網絡的中心度也會透過金融傳導顯著影響下游海運公司的信用風險。最後，海運業於其金融傳導因子之分析方面，本文發現乾散裝海運費率市場與道瓊全球航運指數存在顯著的長期外溢現象，道瓊全球航運指數與美元指數以及道瓊全球航運指數與高盛商品指數也存在顯著的長期外溢現象。
In the upstream and downstream network structure, this paper analyzes the relationship between credit risk and network structure and financial contagion in the shipping industry, and uses Altman Z-score to measure the credit risk of shipping companies, estimate the network connection and measure the network structure and calculate the network centrality by the volatility spillover index.
This paper also explores the dynamic spillover between shipping industry and its financial contagion factor. This paper analyzes the four goals: First, we analysis whether shipping company exists Too-Central-to-Fail phenomenon. Second, in the upstream and downstream structure of the network, we analysis the relationship between shipping credit risk and network centrality in the overall network. Third, we analysis whether the upstream bank`s network centrality affect the downstream shipping companies’ credit risk through the financial contagion. Finally, we use DCC-GARCH model to analyze the dynamic spillover between shipping industry and its financial contagion factor.
We find that the shipping companies also have Too-Central-to-Fail phenomenon which is similar to the bank. In three kinds of the measurement of network centrality, the Closeness centrality is the only network centrality which is significant impact on the shipping company`s credit risk, and the empirical result have the highest Adjusted R-squared. We also find that the financial contagion intensity from the bank to the shipping company has a positive and significant effect on the credit risk of the shipping company. Second, in the upstream and downstream structure of the network, the Closeness centrality is still the only network centrality which is significant impact on the shipping company`s credit risk. The shipping company`s network Closeness centrality is higher, the credit risk is lower. The Closeness centrality empirical result have the highest Adjusted R-squared. This result showing the same as the first research goal. In the network structure variable, the bank`s financial contagion strength to the shipping company has a positive and significant impact on the credit risk of the shipping company. Moreover, this paper discovers that the network centrality of the bank has a significant impact on the credit risk of the downstream shipping companies through financial contagion. The bank’s Closeness centrality in the whole network also has a significant impact on the credit risk of the downstream shipping companies through financial contagion. Finally, we examines the existence of long-term spillover within the dry bulk shipping market and the Dow Jones global shipping index, Dow Jones global shipping index and the dollar index, the Dow Jones global shipping index and Goldman Sachs Commodity index by employing DCC-GARCH model. |
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| Description: | 碩士
國立政治大學
經濟學系
104258010 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0104258010 |
| Data Type: | thesis |
| Appears in Collections: | [經濟學系] 學位論文
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