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題名 國外金融機構違約預警模型--Merton模型之應用
The Default Predicted Model of Foreign Financial Institutions--An Application of Merton Model作者 郭名峻 貢獻者 蔡政憲
郭名峻關鍵詞 信用風險衡量模型
違約事件
Merton模型
預期違約機率
財務變數
Logistic迴歸日期 2012 上傳時間 1-Jul-2013 17:59:04 (UTC+8) 摘要 有鑑於信用風險衡量模型之廣泛使用，以及預測金融機構違約事件之重要性，本研究欲建立能有效預測金融機構違約事件之模型。其中Merton模型之概念被廣泛的應用，包含著名之KMV公司亦以Merton模型之概念建立信用風險管理機制，因此本研究選擇Merton模型之產出-預期違約機率(Expected Default Frequency, EDF)作為預測違約事件之主要變數。 本研究以國外56家金融機構，於2007至2009年共140筆樣本資料，資料內容包含股價以及財務變數。實證方法為先以各公司之股價資訊透過Merton模型計算各樣本之預期違約機率，作為Logistic迴歸模型之自變數進行分析。之後另外加入財務變數嘗試增進模型之解釋能力。此外，本研究亦修正模型之設定以檢視在更貼近真實世界的假設下，模型之預測能力是否有提升。本研究之實證結果發現，單以預期違約機率所建立之違約預測模型即有良好之預測能力，即使再加入其他變數並進行假設的修正，對於模型預測效果提升並不顯著。因此本研究肯定Merton模型以公司之股價資訊衡量違約風險之概念。 參考文獻 1.Altman, E. I, 1968, Financial Ratios, Discriminant Analysis and the Prediction ofCorporate Bankruptcy, Journal of Finance, 23, 578-609.2.Altman, E.I., Haldeman R. G., and Narayanan P., 1977, ZETA analysis: A New Model to Identity Bankruptcy Risk of Corporations, Journal of Banking and Finance, 64-75.3.Vulpes, G., and Brasili, A., 2006, Banking integration and co-movements in EU banks’ fragility, University Library of Munich, Germany.4.Aziz, A., and Lawson, G. H., 1989, Cash Flow Reporting and Financial Distress Model: Testing of Hypotheses, Financial Management, 18, 55-63.5.Beaver, W.H., 1966, Financial Ratios as Predictors of Failure, Journal ofAccounting, 77-111.6.Benos, A., and Papanastasopoulos, G., 2007, Extending the Merton model: A hybrid approach to assessing credit quality, Mathematical and computer modelling, 46, 47-68.7.Black, F., and Scholes, M., 1973, The Pricing of Options and Corporate Liabilities, Journal of Political Economy, 81, 637-659.8.Coats, P. K., and Fant, L. F., 1993, Recognizing Financial Distress Using a NeuralNetwork Tool, Financial Management, 22, 142-155.9.Crosbie, P. and Bohn, J., 2003, Modeling Default Risk, KMV corporation.10.Delianedis, G. and Geske, R., 1998, Credit Risk and Risk Neutral Default Probabilities: Information About Migrations and Defaults, Anderson School of Management.11.Duan, J. C., and Wang, T, 2012, Measuring Distance-to-Default for Financial and Non-Financial Firms, Global Credit Review, 2, 95-108.12.Gentry, J. A., Newbold, P. and Whitford, D. T., 1985. Classifying Bankrupt Firms with Funds Flow Components, The Journal of Accounting Research , 23, 146-160.13.Gropp, R., Vesala, J., and Vulpes, G., 2004, Market indicators, bank fragility, and indirect market discipline, Economic Policy Review, 10.14.Chan-Lau, J.A., Jobert, A., and Kong, J., 2004, An Option-Based Approach to Bank Vulnerabilities in Emerging Markets, International Monetary Fund.15.Koh, H. C. and Tan, S. S., 1999, A neural network approach to the prediction of going concern status, Accounting and Business Research, 29, 211-216.16.Le Courtois, O., and Quittard-Pinon, F., 2006, Risk-neutral and actual default probabilities with an endogenous bankruptcy jump-diffusion model, Asia-Pacific Financial Markets, 13, 11-39.17.Martin, D., 1977, Early Warning of bank failure: A logit regression approach, Journal of Banking and Finance, 1, 249-276.18.Merton, R. C., 1974, On the Pricing of Corporate Debt: The Risk Structure of Interest Rates, Journal of Finance, 29, 449-70.19.Nasir, M.L., John, R.I., Bennett, S.C., Russell, D.M., and Patel, A., 2000, Predicting Corporate Bankruptcy using Artificial Neural Networks, Journal of Applied Accounting Research, 5, 30-52.20.Ohlson, J. A. 1980, Financial ratios and the probabilistic prediction of bankruptcy, Journal of Accounting Research, 18, 109-131.21.Pompe, P. and Bilderbeek, J., 2005, The Prediction of Bankruptcy of Small- and Medium-Sized Industrial Firms, Journal of Business Venturing, 20, 847-868.22.Lu, Y., 2008, Default Forecasting in KMV, Oriel College, University of Oxford. 描述 碩士
國立政治大學
風險管理與保險研究所
100358019
101資料來源 http://thesis.lib.nccu.edu.tw/record/#G0100358019 資料類型 thesis dc.contributor.advisor 蔡政憲 zh_TW dc.contributor.author (Authors) 郭名峻 zh_TW dc.creator (作者) 郭名峻 zh_TW dc.date (日期) 2012 en_US dc.date.accessioned 1-Jul-2013 17:59:04 (UTC+8) - dc.date.available 1-Jul-2013 17:59:04 (UTC+8) - dc.date.issued (上傳時間) 1-Jul-2013 17:59:04 (UTC+8) - dc.identifier (Other Identifiers) G0100358019 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/58733 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 風險管理與保險研究所 zh_TW dc.description (描述) 100358019 zh_TW dc.description (描述) 101 zh_TW dc.description.abstract (摘要) 有鑑於信用風險衡量模型之廣泛使用，以及預測金融機構違約事件之重要性，本研究欲建立能有效預測金融機構違約事件之模型。其中Merton模型之概念被廣泛的應用，包含著名之KMV公司亦以Merton模型之概念建立信用風險管理機制，因此本研究選擇Merton模型之產出-預期違約機率(Expected Default Frequency, EDF)作為預測違約事件之主要變數。 本研究以國外56家金融機構，於2007至2009年共140筆樣本資料，資料內容包含股價以及財務變數。實證方法為先以各公司之股價資訊透過Merton模型計算各樣本之預期違約機率，作為Logistic迴歸模型之自變數進行分析。之後另外加入財務變數嘗試增進模型之解釋能力。此外，本研究亦修正模型之設定以檢視在更貼近真實世界的假設下，模型之預測能力是否有提升。本研究之實證結果發現，單以預期違約機率所建立之違約預測模型即有良好之預測能力，即使再加入其他變數並進行假設的修正，對於模型預測效果提升並不顯著。因此本研究肯定Merton模型以公司之股價資訊衡量違約風險之概念。 zh_TW dc.description.tableofcontents 第一章 緒論 3第一節 研究動機與目的 3第二節 研究方法與流程 5第二章 信用風險衡量模型 7第一節 傳統違約預測模型 7第二節 新興信用風險衡量方法 11第三章 研究模型介紹 15第一節 Merton模型介紹 16第二節 KMV公司之信用風險衡量方法 21第三節 風險中立假設之探討 23第四章 實證分析 25第一節 實證樣本 25第二節 變數選擇 27第三節 實證結果 28第五章 結論與建議 48第一節 結論 48 第二節 研究限制與建議 49參考文獻 50 zh_TW dc.format.extent 1075725 bytes - dc.format.mimetype application/pdf - dc.language.iso en_US - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0100358019 en_US dc.subject (關鍵詞) 信用風險衡量模型 zh_TW dc.subject (關鍵詞) 違約事件 zh_TW dc.subject (關鍵詞) Merton模型 zh_TW dc.subject (關鍵詞) 預期違約機率 zh_TW dc.subject (關鍵詞) 財務變數 zh_TW dc.subject (關鍵詞) Logistic迴歸 zh_TW dc.title (題名) 國外金融機構違約預警模型--Merton模型之應用 zh_TW dc.title (題名) The Default Predicted Model of Foreign Financial Institutions--An Application of Merton Model en_US dc.type (資料類型) thesis en dc.relation.reference (參考文獻) 1.Altman, E. I, 1968, Financial Ratios, Discriminant Analysis and the Prediction ofCorporate Bankruptcy, Journal of Finance, 23, 578-609.2.Altman, E.I., Haldeman R. G., and Narayanan P., 1977, ZETA analysis: A New Model to Identity Bankruptcy Risk of Corporations, Journal of Banking and Finance, 64-75.3.Vulpes, G., and Brasili, A., 2006, Banking integration and co-movements in EU banks’ fragility, University Library of Munich, Germany.4.Aziz, A., and Lawson, G. H., 1989, Cash Flow Reporting and Financial Distress Model: Testing of Hypotheses, Financial Management, 18, 55-63.5.Beaver, W.H., 1966, Financial Ratios as Predictors of Failure, Journal ofAccounting, 77-111.6.Benos, A., and Papanastasopoulos, G., 2007, Extending the Merton model: A hybrid approach to assessing credit quality, Mathematical and computer modelling, 46, 47-68.7.Black, F., and Scholes, M., 1973, The Pricing of Options and Corporate Liabilities, Journal of Political Economy, 81, 637-659.8.Coats, P. K., and Fant, L. F., 1993, Recognizing Financial Distress Using a NeuralNetwork Tool, Financial Management, 22, 142-155.9.Crosbie, P. and Bohn, J., 2003, Modeling Default Risk, KMV corporation.10.Delianedis, G. and Geske, R., 1998, Credit Risk and Risk Neutral Default Probabilities: Information About Migrations and Defaults, Anderson School of Management.11.Duan, J. C., and Wang, T, 2012, Measuring Distance-to-Default for Financial and Non-Financial Firms, Global Credit Review, 2, 95-108.12.Gentry, J. A., Newbold, P. and Whitford, D. T., 1985. Classifying Bankrupt Firms with Funds Flow Components, The Journal of Accounting Research , 23, 146-160.13.Gropp, R., Vesala, J., and Vulpes, G., 2004, Market indicators, bank fragility, and indirect market discipline, Economic Policy Review, 10.14.Chan-Lau, J.A., Jobert, A., and Kong, J., 2004, An Option-Based Approach to Bank Vulnerabilities in Emerging Markets, International Monetary Fund.15.Koh, H. C. and Tan, S. S., 1999, A neural network approach to the prediction of going concern status, Accounting and Business Research, 29, 211-216.16.Le Courtois, O., and Quittard-Pinon, F., 2006, Risk-neutral and actual default probabilities with an endogenous bankruptcy jump-diffusion model, Asia-Pacific Financial Markets, 13, 11-39.17.Martin, D., 1977, Early Warning of bank failure: A logit regression approach, Journal of Banking and Finance, 1, 249-276.18.Merton, R. C., 1974, On the Pricing of Corporate Debt: The Risk Structure of Interest Rates, Journal of Finance, 29, 449-70.19.Nasir, M.L., John, R.I., Bennett, S.C., Russell, D.M., and Patel, A., 2000, Predicting Corporate Bankruptcy using Artificial Neural Networks, Journal of Applied Accounting Research, 5, 30-52.20.Ohlson, J. A. 1980, Financial ratios and the probabilistic prediction of bankruptcy, Journal of Accounting Research, 18, 109-131.21.Pompe, P. and Bilderbeek, J., 2005, The Prediction of Bankruptcy of Small- and Medium-Sized Industrial Firms, Journal of Business Venturing, 20, 847-868.22.Lu, Y., 2008, Default Forecasting in KMV, Oriel College, University of Oxford. zh_TW
