博碩士論文 92428005 詳細資訊




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姓名 李智芳(Chih-Fang Li)  查詢紙本館藏   畢業系所 財務金融學系
論文名稱 中小企業信保案件之違約機率、回收率與信用風險值的實證研究
(The Empirical Study in PD、LGD and Credit VaR of SME Guaranteed Loans)
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摘要(中) 本研究探討中小企業信保案件之信用風險,文章一開始先對國外以及台灣的中小企業信保制度做詳細完整的介紹,以利讀者對信保制度及其可能存在之風險有進一步的認識與瞭解。接下來選取90年在金融聯合徵信中心有註記為信用擔保之案件作為研究樣本,利用Moody’s所發展的PFM模型計算違約機率,以LossCalc模型計算回收率,最後再利用此兩信用風險因子計算信用風險值。
實證研究對信保樣本做以下七大特性的檢定:(1)產業別,(2)公司規模, (3)授信額度和其佔借款公司資產總額或負債總額的比率,(4)核貸銀行去年的逾放比,(5)借款公司的往來銀行家數,(6)會計師查核意見,(7)過去一年是否有延遲還款記錄。結果發現,在違約機率方面,會隨著公司規模、授信金額以及往來銀行家數遞增而上升;傳統產業的違約機率最高。就回收率而言,擔保比率、違約暴顯比率、違約機率、產業指數報酬率、GDP成長率以及重貼現率都是顯著的預測因子。當信保基金對違約案件執行代位清償時,不同的授信金額、核貸銀行前一年逾放比以及借款公司往來銀行家數間的回收率亦有顯著差異;此外,有擔保品的授信案件回收率明顯高於沒有擔保品的違約案件;以土地及廠房為擔保品的回收率略高於信保基金作保案件的回收率。而公司的各種貸款額度使用率與回收之間也有反向的關係存在。若看投組部位的信用風險值,則電子業、核貸銀行前一年逾放比以及往來銀行家數較高的案件信用風險值也偏高。
最後,由於中小企業的會計制度較不透明,會計師查核意見有人為操縱的可能,所以對信用風險的三大要素都沒有區別能力。而過去一年是否有延遲還款記錄則受限於樣本資料的不足,有延遲還款記錄的企業低於30家,也不具有信用風險的鑑別能力。
摘要(英) This paper explores the credit risk of Small Medium Enterprise Gauranteed (SMEG) loans. In order to make readers be more familiar with SMEG, the first part of this article introduces the foreign and domestic SMEG system. Subsequently, including in the sample firms with record of government guarantee in 2001 JCIC database, we calculate PD and RR by Moody’s PFM and LossCalc model separately. Finally, we use these two factors to get VaR.
The empirical research tests the following seven characteristics: 1.industry, 2.firm scale, 3.loan size and the ratio of loan amount to total asset or liability, 4.bank NPL ratio of last year, 5. relationship with banks, 6.accountant auditing opinion, 7.proponed-payment record.The results reveal that PD increases according to scale, loan size and the number of banks rises. Traditional industry has the highest PD;Collateral ratio、 default exposure ratio、PD、return of industry index、GDP growth rate and interest rate are influential expected factors of RR.When the SMEG exercises replacement payment for default loans, there are significant differences among loan size、last year NPL ratio and the number of relative banks. Besides, collateral loans have higher RR than credit loans; while loans mortgaged by land and plant have higher RR than SMEG loans. The usage ratio has negative relationship with RR. Portfolios of Electronic industry、higher NPL ratio and more relative banks have higher credit VaR..
Finally, no matter in PD, RR or VaR, accountant auditing opinion and record of proponed-payment has no distinctive ability. Owing to the less transparency of the accounting system of SME, accountant auditing opinion tends to be manipulated. The number of proponed-payment sample, less than 30 firms, is not sufficient to show the credit risk.
關鍵字(中) ★ 中小企業信保基金
★ KMV
★ PFM
★ 回收率
★ 信用風險值
關鍵字(英) ★ Small Medium Enterprise Guarantee
★ KMV
★ PFM
★ Rec
論文目次 目錄
第一章 緒論 1
第一節 研究背景 1
第二節 研究目的 1
第三節 研究架構 2
第二章 中小企業信保制度概觀 4
第一節 國外中小企業保證體系簡介 4
第二節 台灣中小企業信保基金 16
第三章 文獻回顧 30
第一節 違約機率文獻 30
第二節 回收率文獻 31
第三節 中小型企業信用風險相關文獻 33
第四章 研究方法 35
第一節 違約機率之計算 35
第二節 回收率之計算 39
第三節 信用風險值衡量 44
第五章 實證結果 47
第一節 中小企業信用保證案件的違約機率 47
第二節 中小企業信用保證案件的回收率 60
第三節 中小企業信用保證案件的信用風險值 75
第六章 結論與建議 78
參考文獻 ……………………………………………………………… 80
附錄 ……………………………………………………………………85
圖目錄
圖2-1 美國、英國及加拿大的累積違約率 13
圖3-1 授權保證流程圖 19
圖3-2 專案保證流程圖 20
圖4-1 歷年保證件數 23
圖4-2 信用保證利用率 23
圖4-3 影響回收率之因素四大構面 33
圖4-4 KMV法下公司違約發生示意圖 35
圖 5-1 87至89年信保案件的回收率概況..……………………………….…74
圖5-2 利用LossCalc方法推估民國90年信保案件的回收率分佈……...82
表目錄
表2-1 中小企業信保基金保證成數分級標準 22
表5-1 利用KMV法求解VA及 時,需要蒐集的上市櫃公司資料 50
表5-2 利用KMV法求算上市櫃公司的VA及 ,需要投入的變數及結果 51
表5-3 上市櫃和中小企業在 PFM產業比對法的主要輸入數據 54
表5-4 產業比對法的產業種類、上市櫃和中小企業的家數 55
表5-5 利用各產業上市櫃公司的財務數據,估計迴歸模型的參數 56
表5-6 依照信保案件的財務屬性,描述其平均違約機率 61
表5-7 國內外主要研究文獻的回收率比較 63
表5-8 回收率模型的五種影響構面及變數衡量 64
表5-9 回收率之影響構面及變數的敘述統計分析 68
表5-10 回收率預測模型的實證結果 70
表5-11 中小企業過去六年違約樣本提供擔保品的概況 72
表5-12 中小企業的回收率會否受到有無提供擔保品而影響 72
表5-13 國內外主要研究文獻針對有無提供擔保品而比較其平均回收率 73
表5-14 中小企業的回收率會否隨著擔保品種類而存在差異 73
表5-15 主要文獻針對提供保證或擔保品計算而得的回收率 73
表5-16 各類型放款的額度使用率(以四分位數衡量) 74
表5-17 各類型放款的額度使用率與回收率之卡方獨立性檢定 74
表5-18 依照信保案件的財務屬性,描述其平均回收率 76
表5-19 依照信保案件之財務屬性,描述其平均信用風險值 79
參考文獻 中文文獻
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指導教授 陳錦村(Jing-Twen Chen) 審核日期 2005-7-21
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