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研究生:周怡君
研究生(外文):Yi-Chun Chou
論文名稱:信用違約交換價格之決定因子:平衡縱列數據方法
論文名稱(外文):Determinants of Credit Default Swaps:A Panel Regression Approach
指導教授:黃宜侯黃宜侯引用關係
指導教授(外文):Alex YiHou Huang
學位類別:碩士
校院名稱:元智大學
系所名稱:財務金融學系
學門:商業及管理學門
學類:財務金融學類
論文種類:學術論文
論文出版年:2009
畢業學年度:97
語文別:英文
論文頁數:81
中文關鍵詞:信用違約交換決定因子縱列數據迴歸
外文關鍵詞:CDSCredit default SwapDeterminantsPanel Regression
相關次數:
  • 被引用被引用:0
  • 點閱點閱:145
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此篇論文的研究目的為使用平衡縱列數據資料(balance panel data)來觀察哪些變數為決定信用違約交換價差之重要因子。過去已經有許多文獻研究信用違約交換之決定因子,然而此篇論文主有有兩點與之前的研究不同。首先我們的資料包含197家公司2003-2006年間之日資料,資料總筆數大於一千筆。此外,更重要的是我們考慮了更完整的變數,包含了結構模型中之理論變數,股價報酬,債券溢酬,總經因子,會計項目還有信用評等。此篇論文的研究結果和先前學者一致,我們發現結構模型隱含的變數、和信用評等是決定信用違約交換價格之重要因素,而債券溢酬對信用違約交換的價格之解釋能力也很大,加入此項變數後調整後R平方上升了13%。而將所有的變數放入迴歸中使用固定效益法下,R平方高達84%;而在子樣本中,而這些變數的解釋能力在信評最差的公司那一組高達90%,在FIRE產業甚至高達93%。此外,我們的發現也証實先前學者的發現,相較於信評佳的公司,信用評等的解釋能力對信評差的公司較大。而此篇論文更發現,和信用評等的特徵相似,公司的槓桿程度和債券溢價對信評差的公司之信用違約交換的解釋能力較信評佳的公司大。此外,此篇論文使用RESET方法檢測出信用違約交換價格和股票報酬/債券溢酬存在非線性關係。
The key purpose of this research is to observe the determinants of CDS spreads with a large balanced panel data. Many prior researches have devoted on this topic, however this research differ from them in two major aspects. First, a complete balance panel data is utilized containing 197 companies and more than 1000 daily data from 2003 to 2006. Second and more importantly, a comprehensive list of variables is accounted including theoretical variables, stock market info, industries, bond yield-spreads, macro-financial variables, accounting items, and credit rating. Our finding is similar to previous papers, we find the variables which are implied by structure model and credit rating plays an important role on the determinants of CDS. The explanatory power of bond yield-spreads is also great, after taking it into POLS regression, adjusted R-square increase 13%. In the FE approach, after taking all above variables into the model, we observe that the R-square can reach nearly 84% in the whole sample; in the subsamples, the R-square of the lowest credit rating firms reaches about 90%, and The R-square of FIRE industry even reaches approximately 93%. Moreover, consistent with previous research, the explanatory power of credit rating for low credit rating firms is greater than high credit rating firms. Our results suggest that not only the credit rating, the low credit firms is more sensitive to bond yield-spreads and leverage than high credit firms as well. Further, we adopt the RESET approach and detect that the nonlinear relationship exists between CDS spreads and stock return/bond yield-spreads.
Content
I.Introduction ..........................................1

II.Literature Review ....................................4

III.Methodology.........................................15

IV.Data & Empirical Results.............................20
IV.1.Data Description..............................20
IV.2.Variable Explanation..........................21
IV.3.Summary Statistics............................25
IV.4.Test of Lead-lag and RESET Relationship.......28
IV.5.Panel Regression Outcome......................30
IV.6.Fixed Effect Models Grouped by Credit Rating and Industry...........................................37

V.Conclusion............................................41
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