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博碩士論文 etd-0626111-173455 詳細資訊
Title page for etd-0626111-173455
論文名稱
Title
以服務導向架構整合信用風險模型之研究
A Study on Integrating Credit Risk Models via Service-Oriented Architecture
系所名稱
Department
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
134
研究生
Author
指導教授
Advisor
召集委員
Convenor
口試委員
Advisory Committee
口試日期
Date of Exam
2011-06-17
繳交日期
Date of Submission
2011-06-26
關鍵字
Keywords
服務導向架構、違約機率、時間變異聯合估計模型、風險中立機率測度模型、Moody's KMV違約點修正模型、回復率
Recovery Rate, Probabilities of Default, Service-Oriented Architecture, Time-Varying Jointly Estimated Model, Moody's KMV Model with Default Point Modified, Risk-Neutral Probability Measure Model
統計
Statistics
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中文摘要
本研究透過服務導向架構(Service-Oriented Architecture,SOA)概念將信用風險模型予以結合,建立一套以銀行端用戶為使用者之「服務導向架構信用風險模型系統」,系統中使用者輸入各企業之財務相關資料以及選定運算偏好路徑後,可求算出各種信用風險相關資訊結果,包括違約機率、回復率、預估之資產市值及其波動度、違約點、違約距離以及四大財務特性主成份分析指標等,除了以數值方式表示之外,系統亦將依照資料運算結果繪製線圖提供使用者一併作為參考之用。
參與本研究系統開發之模型涵蓋以門檻迴歸更新違約點定義之 Moody’s KMV 違約點修正模型(Moody’s KMV Model with Default Point Modified)、風險中立機率測度模型(Risk-Neutral Probability Measure Model)與時間變異聯合估計模
型(Time-Varying Jointly Estimated Model),以往研究指出各模型皆可提供實用之信用風險資訊,本研究利用中小企業信用保證基金(Small and Medium Enterprise Credit Guarantee Fund)資料作為系統測試樣本,發現上述三模型不但可透過設計有效結合成為一套評估企業信用風險與產業信用風險之流程,此流程亦具備參考價值,未來可供業界運用。
系統要求使用者輸入之財務相關資料皆與參與之模型有關,包含借款金額、資產帳面價值、成長能力相關比率(如營收成長率、稅前淨利成長率等)、經營能力相關比率(如應收帳款週轉率、存貨週轉率等)、償債能力相關比率(如流動比率、
負債比率等)、獲利能力相關比率(如資產報酬率、淨值報酬率等)以及計算Moody’s KMV 違約點修正模型中違約點門檻之資料(如長短期負債)。除輸入資料外,使用者亦需選擇產業名稱、所使用之違約點門檻、結合時間變異聯合估計模型之加權方式、資料年度與借款利率,以構成系統運算路徑,如此便可交付系統求算結果。
在系統連結各模型之過程中,使用者可選擇使用借款金額加權或資產市值加權之方式求算加權平均產業違約機率與加權平均產業回復率,隨後再進入時間變異聯合估計模型求算結果,研究中亦以四分位法假設景氣模擬情況,區分為景氣活絡與景氣欠佳兩種情況,與原始估算結果一併列示;再以上確界策略(Supremum Strategy)曲線與下確界策略(Infimum Strategy)曲線讓使用者對於「時間變異產業邊際違約機率」之最佳情況與最差情況有所掌握。
Abstract
This thesis establishes an information system which combines three credit risk models through Service-Oriented Architecture (SOA). The system requires the bank
user inputting finance-related data and selecting options to generate a series of credit risk related results, including the probabilities of default, the recovery rates, the expected market value of assets, the volatilities of the expected market value of assets, the default points, the default distances, and four indexes from principal components
analyses. In addition to exhibiting the numerical results, graphical results are also available for the user.
Three credit risk models joining this system are the Moody’s KMV Model with Default Point Modified, the Risk-Neutral Probability Measure Model, and the Time-Varying Jointly Estimated Model. Several previous researches have demonstrated the validity of these credit risk models, hence the purpose of this study is not to examine the practicability of these models, but to see if these models are capable of connecting each other effectively and eventually establishing a process to
evaluate the credit risk of enterprises and industries by the use of testing samples. Testing samples are data from Taiwan Small and Medium Enterprise Credit Guarantee
Fund.
The finance-related data includes the loan amounts, the book value of assets, the data used to calculate the default point threshold (such as the short-term debt and the long-term debt), and the financial ratios with regard to growth ability (such as the revenue growth rate and the profit growth rate before tax), operation ability (such as the accounts receivable turnover rate and the inventory turnover rate), liability-paying ability (such as the current ratio and the debt ratio), and profitability (such as the return on assets and the return on equity). In addition to inputting the finance-related data, the system also require the user selecting the industrial category, the default point threshold, the way data being weighted, the data period, and the borrowing rates from the option page for every enterprise in order to acquire the results.
Among the computing process, user is required to select weighted average method, either weighted by loan amounts or weighted by market value of assets, to obtain “the weighted average probability of default of the industry” and “the weighted average recovery rate of the industry” which are both used by the Time-Varying Jointly Estimated Model. This study also makes use of quartiles to simulate the situation when the user is near the bottom and top of the business cycle. Furthermore, the “Supremum Strategy” and the “Infimum Strategy” are added to this study to let the user realize the best condition and the worse condition of the “Time-Varying Industrial Marginal Probabilities of Default”.
目次 Table of Contents
論文審定書............................................................................... i
誌謝 ......................................................................................... ii
中文摘要................................................................................. iii
英文摘要.................................................................................. v
第一章緒論.............................................................................. 1
第一節研究背景...................................................................... 1
第二節研究動機...................................................................... 8
第三節研究目的.................................................................... 10
第四節研究流程.................................................................... 11
第二章信用風險管理模型主要文獻回顧與探討................ 12
第一節 Look Backward 模型 ............................................. 13
第二節 Look Forward 模型.................................................. 15
第三章研究方法..................................................................... 20
第一節系統架構與方法步驟................................................. 20
第二節 Moody’s KMV 違約點修正模型 .............................. 22
第三節風險中立機率測度模型............................................. 27
第四節時間變異聯合估計模型............................................. 34
第五節服務導向架構運用流程............................................. 39
第四章系統測試..................................................................... 56
第一節測試樣本資料來源及說明......................................... 56
第二節介面操作流程示例..................................................... 67
第五章研究結論、限制與建議........................................... 104
第一節研究結論................................................................... 104
第二節研究限制與建議....................................................... 106
附錄軟體開發預算模擬....................................................... 109
參考文獻............................................................................... 117
參考文獻 References
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2. 中小企業信用保證基金 95 年度委託研究計畫,「中小企業信保基金主要保證業務-違約機率與信用風險之評估」研究保告,2006。
3. 王瑞銘 (2008),「利用分量迴歸法探討KMV 信用風險模型:違約點定義之檢討」,國立中山大學財務管理研究所碩士論文。
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5. 吳紫菱 (2008),「企業償債能力與信用風險關係之研究 - KMV 模型的再探討」,國立中山大學財務管理研究所碩士論文。
6. 李怡玫 (2009),「內生化信用風險模型:回復率、違約機率與景氣情況」,國立中山大學財務管理研究所碩士論文。
7. 李佳穎 (2009),「回復率、違約機率與信用評等間關係之研究 - 以上市企業有發行公司債者為研究對象」,國立中山大學財務管理研究所碩士論文。
8. 林玟町 (2008),「營運能力能成為KMV信用風險模型的修正指標嗎? - 以台灣上市櫃公司為例」,國立中山大學財務管理研究所碩士論文。
9. 林裕勝 (2008),「選擇權交易者是否能以隱含波動偏態預測危機?以台灣和美國市場為例」,朝陽科技大學財務金融研究所碩士論文。
10. 張瓈文 (2008),「中小企業融資違約機率暨信用保證費率之評估 - 以台灣 F銀行為例」,國立中山大學財務管理研究所碩士論文。
11. 莊元豪 (2008),「KMV 信用風險模型運用於台灣上市上櫃企業之適切性-模型違約點再修正」,國立中山大學財務管理研究所碩士論文。
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16. 郭照榮 (2006b),「中小企業信保基金主要保證業務違約機率與信用風險之評估」,中小企業信用保證基金實證研究計畫成果報告。
17. 郭照榮 (2008),「關於KMV信用風險評估模型的三個重要議題之研究」,行政院國家科學委員會專題研究計畫成果報告(NSC-96-2416-H-110-023)。
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19. 郭照榮 (2011),「信用保證放款承保風險之評估:以時間變異聯合估計模型及二項數方法預估其代償金額」,行政院國家科學委員會專題研究計畫成果報告,(NSC-99-2410-H-110-028)。
20. 郭月娟 (2008),「信用保證案件違約機率與信用風險之實證研究」,國立中山大學財務管理研究所碩士論文。
21. 黃千峯 (2008),「流動能力對台灣上市上櫃公司違約機率之影響 - 門檻迴歸模型之應用」,國立中山大學財務管理研究所碩士論文。
22. 楊適銓 (2008),「利用門檻迴歸探討KMV信用風險模型之違約點定義問題」,國立中山大學財務管理研究所碩士論文。
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二、 英文部分:
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York.
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pp. 253-262.
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