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題名 離散型風險模型應用於銀行財務預警系統
Application of Discrete-time Hazard Model in forecasting bankruptcy in banking industry作者 蕭文彥 貢獻者 林士貴
蕭文彥關鍵詞 銀行
銀行財務危機
財務預警模型
離散型風險模型
bank
bank failure
early warning system
discrete time hazard mode日期 2012 上傳時間 2-Sep-2013 16:04:55 (UTC+8) 摘要 本財務預警模型研究延續Shumway(2001)年所提出的離散型風險模型(Discrete-time Hazard Model)架構，即Shumway 所稱之多期邏輯斯迴歸模型(Multiperiod logistic regression model) ，來建立銀行財務預警模型。不同於Shumway所提出的Log 基期風險式，研究者根據實際財務危機發生機率圖提出Quadratic 基期風險式。由於離散型風險模型考量與時間相依共變量(Time-dependent covariate)，該模型可以納入隨時間變動的的市場與總體變數，這是單期模型無法達到的。實證結果顯示，不論是否有加入總體與市場變數，Quadratic 基期風險式離散型模型在樣本內檢測表現都比單期模型與Log 基期風險式離散型模型好，研究亦顯示樣本外的預測Quadratic基期風險式在大多數情況都優於Log 基期風險式與單期模型
This paper continues Shumway(2001) studies on discrete time hazard model, the so called multi-period logistic regression model, to develop a bank failure early warning model . Different from log baseline hazard form proposed by Shumway, author present quadratic baseline hazard form based on the pattern of real default rate. By incorporating time-varying covariates, our model enables us to utilize macroeconomic and market variables, which cannot be incorporated into in a one-period model. We find that our model significantly outperforms the single period logit model and Log baseline hazard model with and without the macroeconomic and market variables at in-sample estimation. The improvement in accuracy comes both from the time-series bank-specific variables and from the time-series macroeconomic variables. Our research also shows that quadratic baseline hazard model outperforms Log baseline hazard model and single period logit model in out-of-sample prediction.參考文獻 林妙宜. (2002). 公司信用風險之衡量, 政治大學金融研究所碩士論文. 徐美珍. (2004). 企業財務危機之預測, 政治大學統計學系碩士論文.卜志豪. (2009). 多期邏輯斯迴歸模型應用在企業財務危機預測之研究, 政治大學統計系碩士論文.陳業寧, 王衍智, & 許鴻英. (2004). 台灣企業財務危機之預測: 信用評分法與選擇權評價法孰優?. 風險管理學報. 李君屏,陳宏輝.(2007). 存款保險之評價：信用風險模型之應用, 風險管理學報.黃瑞卿, 吳中書, 林金龍, & 蕭兆祥. (2012). 台灣企業財務危機因子的實證研究, 台灣金融財務季刊Altman, E. I. (1968). Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. The Journal of Finance, 23(4), 589-609. Altman, E. I., & Hotchkiss, E. (2006). Corporate financial distress and bankruptcy: Predict and avoid bankruptcy, analyze and invest in distressed debt (Vol. 289)Altman, E. I., & Saunders, A. (1997). Credit risk measurement: Developments over the last 20 years. Journal of Banking & Finance, 21(11), 1721-1742. Arena, M. (2008). Bank failures and bank fundamentals: A comparative analysis of Latin America and East Asia during the nineties using bank-level data. Journal of Banking & Finance, 32(2), 299-310. Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of accounting research, 71. Beaver, W. H., McNichols, M. F., & Rhie, J.-W. (2005). Have financial statements become less informative? Evidence from the ability of financial ratios to predict bankruptcy. Review of Accounting Studies, 10(1), 93-122. Bedendo, M., & Bruno, B. (2012). Credit risk transfer in US commercial banks: What changed during the 2007–2009 crisis? Journal of Banking & Finance. Begley, J., Ming, J., & Watts, S. (1996). Bankruptcy classification errors in the 1980s: An empirical analysis of Altman`s and Ohlson`s models. Review of Accounting Studies, 1(4), 267-284. Bharath, S., & Shumway, T. (2004). Forecasting default with the KMV-Merton model. Paper presented at the AFA 2006 Boston Meetings Paper. Bharath, S. T., & Shumway, T. (2008). Forecasting default with the Merton distance to default model. Review of Financial Studies, 21(3), 1339-1369. Bonfim, D. (2009). Credit risk drivers: Evaluating the contribution of firm level information and of macroeconomic dynamics. Journal of Banking & Finance, 33(2), 281-299. Brown, C. C. (1975). On the use of indicator variables for studying the time-dependence of parameters in a response-time model. Biometrics, 31(4), 863-872. Campbell, J. Y., Hilscher, J., & Szilagyi, J. (2008). In search of distress risk. The Journal of Finance, 63(6), 2899-2939. Carling, K., Jacobson, T., Lindé, J., & Roszbach, K. (2007). Corporate credit risk modeling and the macroeconomy. Journal of Banking & Finance, 31(3), 845-868. Cole, R., Gunther, J., & Cornyn, B. (1995). FIMS: A New Financial Institutions Monitoring System for Banking Organizations. Federal Reserve Bulletin, 81, 1-15. Cole, R. A., & Gunther, J. W. (1998). Predicting bank failures: A comparison of on-and off-site monitoring systems. Journal of Financial Services Research, 13(2), 103-117. Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society. Series B (Methodological), 187-220. Crouhy, M., Galai, D., & Mark, R. (2000). A comparative analysis of current credit risk models. Journal of Banking & Finance, 24(1), 59-117. Demirgüç-Kunt, A. (1989). Modeling large commercial-bank failures: a simultaneous-equation analysis: Federal Reserve Bank of Cleveland, Research Department.Duffie, D., Saita, L., & Wang, K. (2007). Multi-period corporate default prediction with stochastic covariates. Journal of Financial Economics, 83(3), 635-665. Espahbodi, P. (1991). Identification of problem banks and binary choice models. Journal of Banking & Finance, 15(1), 53-71. Gajewski, G. R. (1989). Assessing the risk of bank failure. Paper presented at the Federal Reserve Bank of Chicago Proceedings.Hillegeist, S. A., Keating, E. K., Cram, D. P., & Lundstedt, K. G. (2004). Assessing the probability of bankruptcy. Review of Accounting Studies, 9(1), 5-34. Hoggarth, G., Reis, R., & Saporta, V. (2002). Costs of banking system instability: some empirical evidence. Journal of Banking & Finance, 26(5), 825-855. Hull, J. (1989). Assessing credit risk in a financial institution`s off-balance sheet commitments. Journal of Financial and Quantitative Analysis, 24(4), 489-501. Jagtiani, J., & Lemieux, C. (2001). Market discipline prior to bank failure. Journal of Economics and Business, 53(2), 313-324. Lane, W. R., Looney, S. W., & Wansley, J. W. (1986). An application of the Cox proportional hazards model to bank failure. Journal of Banking & Finance, 10(4), 511-531. Lawless, J. F. (2003). Statistical models and methods for lifetime data (Vol. 362): John Wiley & Sons.Lee, S. H., & Urrutia, J. L. (1996). Analysis and prediction of insolvency in the property-liability insurance industry: a comparison of logit and hazard models. Journal of Risk and Insurance, 121-130. Lennox, C. (1999). Identifying failing companies: a re-evaluation of the logit, probit and DA approaches. Journal of Economics and Business, 51(4), 347-364. Levine, R. (2005). Finance and growth: theory and evidence. Handbook of economic growth, 1, 865-934. Martin, D. (1977). Early warning of bank failure: A logit regression approach. Journal of Banking & Finance, 1(3), 249-276. Meyer, P. A., & Pifer, H. W. (1970). Prediction of bank failures. The Journal of Finance, 25(4), 853-868. Nam, C. W., Kim, T. S., Park, N. J., & Lee, H. K. (2008). Bankruptcy prediction using a discrete‐time duration model incorporating temporal and macroeconomic dependencies. Journal of Forecasting, 27(6), 493-506. Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of accounting research, 18(1), 109-131. Pettway, R. H., & Sinkey, J. F. (1980). Establishing On‐Site Bank Examination Priorities: An Early‐Warning System Using Accounting and Market Information. The Journal of Finance, 35(1), 137-150. Shumway, T. (2001). Forecasting bankruptcy more accurately: A simple hazard model*. The Journal of Business, 74(1), 101-124. Singer, J. D., & Willett, J. B. (1993). It’s about time: Using discrete-time survival analysis to study duration and the timing of events. Journal of Educational and Behavioral Statistics, 18(2), 155-195. Singer, J. D., & Willett, J. B. (2003). Applied longitudinal data analysis: Modeling change and event occurrence: Oxford university press.Sinkey, J. F. (1975). A multivariate statistical analysis of the characteristics of problem banks. The Journal of Finance, 30(1), 21-36. Thomson, J. B. (1992). Modeling the bank regulator`s closure option: a two-step logit regression approach. Journal of Financial Services Research, 6(1), 5-23. Tutz, G., & Pritscher, L. (1996). Nonparametric estimation of discrete hazard functions. Lifetime Data Analysis, 2(3), 291-308. West, R. C. (1985). A factor-analytic approach to bank condition. Journal of Banking & Finance, 9(2), 253-266. Whalen, G. (1991). A proportional hazards model of bank failure: an examination of its usefulness as an early warning tool. Federal Reserve Bank of Cleveland Economic Review, 27(1), 21-31. Wheelock, D. C., & Wilson, P. W. (2000). Why do banks disappear? The determinants of US bank failures and acquisitions. Review of Economics and Statistics, 82(1), 127-138. Zmijewski, M. E. (1984). Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research, 22, 59-82. 描述 碩士
國立政治大學
金融研究所
100352006
101資料來源 http://thesis.lib.nccu.edu.tw/record/#G1003520061 資料類型 thesis dc.contributor.advisor 林士貴 zh_TW dc.contributor.author (Authors) 蕭文彥 zh_TW dc.creator (作者) 蕭文彥 zh_TW dc.date (日期) 2012 en_US dc.date.accessioned 2-Sep-2013 16:04:55 (UTC+8) - dc.date.available 2-Sep-2013 16:04:55 (UTC+8) - dc.date.issued (上傳時間) 2-Sep-2013 16:04:55 (UTC+8) - dc.identifier (Other Identifiers) G1003520061 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/59312 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 金融研究所 zh_TW dc.description (描述) 100352006 zh_TW dc.description (描述) 101 zh_TW dc.description.abstract (摘要) 本財務預警模型研究延續Shumway(2001)年所提出的離散型風險模型(Discrete-time Hazard Model)架構，即Shumway 所稱之多期邏輯斯迴歸模型(Multiperiod logistic regression model) ，來建立銀行財務預警模型。不同於Shumway所提出的Log 基期風險式，研究者根據實際財務危機發生機率圖提出Quadratic 基期風險式。由於離散型風險模型考量與時間相依共變量(Time-dependent covariate)，該模型可以納入隨時間變動的的市場與總體變數，這是單期模型無法達到的。實證結果顯示，不論是否有加入總體與市場變數，Quadratic 基期風險式離散型模型在樣本內檢測表現都比單期模型與Log 基期風險式離散型模型好，研究亦顯示樣本外的預測Quadratic基期風險式在大多數情況都優於Log 基期風險式與單期模型 zh_TW dc.description.abstract (摘要) This paper continues Shumway(2001) studies on discrete time hazard model, the so called multi-period logistic regression model, to develop a bank failure early warning model . Different from log baseline hazard form proposed by Shumway, author present quadratic baseline hazard form based on the pattern of real default rate. By incorporating time-varying covariates, our model enables us to utilize macroeconomic and market variables, which cannot be incorporated into in a one-period model. We find that our model significantly outperforms the single period logit model and Log baseline hazard model with and without the macroeconomic and market variables at in-sample estimation. The improvement in accuracy comes both from the time-series bank-specific variables and from the time-series macroeconomic variables. Our research also shows that quadratic baseline hazard model outperforms Log baseline hazard model and single period logit model in out-of-sample prediction. en_US dc.description.tableofcontents 第一章 緒論 1第一節 研究背景 1第二節 研究動機與目的 2第三節 研究流程 3第二章 文獻回顧 5第一節 早期財務危機預警模型:多變量區別分析 5第二節 後期財務危機預警模型:Logit / Probit model 6第三節 近代財務危機預警模型: 存活分析之風險模型 7第三章 研究方法 10第一節 多變量區別分析 10第二節 邏輯斯迴歸模型 11第三節 離散型風險模型 13第四章 資料描述 19第一節 資料來源 19第二節 銀行財務危機定義 19第三節 解釋變數定義 20第四節 模型預測評比 22第五章 實證結果 24第一節 資料敘述統計量 24第二節 樣本內模型訓練 27第三節 樣本外的模型預測 34第六章 結論與建議 39第一節 結論 39第二節 建議與未來研究方向 40參考文獻 41 zh_TW dc.format.extent 940820 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/#G1003520061 en_US dc.subject (關鍵詞) 銀行 zh_TW dc.subject (關鍵詞) 銀行財務危機 zh_TW dc.subject (關鍵詞) 財務預警模型 zh_TW dc.subject (關鍵詞) 離散型風險模型 zh_TW dc.subject (關鍵詞) bank en_US dc.subject (關鍵詞) bank failure en_US dc.subject (關鍵詞) early warning system en_US dc.subject (關鍵詞) discrete time hazard mode en_US dc.title (題名) 離散型風險模型應用於銀行財務預警系統 zh_TW dc.title (題名) Application of Discrete-time Hazard Model in forecasting bankruptcy in banking industry en_US dc.type (資料類型) thesis en dc.relation.reference (參考文獻) 林妙宜. (2002). 公司信用風險之衡量, 政治大學金融研究所碩士論文. 徐美珍. (2004). 企業財務危機之預測, 政治大學統計學系碩士論文.卜志豪. (2009). 多期邏輯斯迴歸模型應用在企業財務危機預測之研究, 政治大學統計系碩士論文.陳業寧, 王衍智, & 許鴻英. (2004). 台灣企業財務危機之預測: 信用評分法與選擇權評價法孰優?. 風險管理學報. 李君屏,陳宏輝.(2007). 存款保險之評價：信用風險模型之應用, 風險管理學報.黃瑞卿, 吳中書, 林金龍, & 蕭兆祥. (2012). 台灣企業財務危機因子的實證研究, 台灣金融財務季刊Altman, E. I. (1968). Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. The Journal of Finance, 23(4), 589-609. Altman, E. I., & Hotchkiss, E. (2006). Corporate financial distress and bankruptcy: Predict and avoid bankruptcy, analyze and invest in distressed debt (Vol. 289)Altman, E. I., & Saunders, A. (1997). Credit risk measurement: Developments over the last 20 years. Journal of Banking & Finance, 21(11), 1721-1742. Arena, M. (2008). Bank failures and bank fundamentals: A comparative analysis of Latin America and East Asia during the nineties using bank-level data. Journal of Banking & Finance, 32(2), 299-310. Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of accounting research, 71. Beaver, W. H., McNichols, M. F., & Rhie, J.-W. (2005). Have financial statements become less informative? Evidence from the ability of financial ratios to predict bankruptcy. Review of Accounting Studies, 10(1), 93-122. Bedendo, M., & Bruno, B. (2012). Credit risk transfer in US commercial banks: What changed during the 2007–2009 crisis? Journal of Banking & Finance. Begley, J., Ming, J., & Watts, S. (1996). Bankruptcy classification errors in the 1980s: An empirical analysis of Altman`s and Ohlson`s models. Review of Accounting Studies, 1(4), 267-284. Bharath, S., & Shumway, T. (2004). Forecasting default with the KMV-Merton model. Paper presented at the AFA 2006 Boston Meetings Paper. Bharath, S. T., & Shumway, T. (2008). Forecasting default with the Merton distance to default model. Review of Financial Studies, 21(3), 1339-1369. Bonfim, D. (2009). Credit risk drivers: Evaluating the contribution of firm level information and of macroeconomic dynamics. Journal of Banking & Finance, 33(2), 281-299. Brown, C. C. (1975). On the use of indicator variables for studying the time-dependence of parameters in a response-time model. Biometrics, 31(4), 863-872. Campbell, J. Y., Hilscher, J., & Szilagyi, J. (2008). In search of distress risk. The Journal of Finance, 63(6), 2899-2939. Carling, K., Jacobson, T., Lindé, J., & Roszbach, K. (2007). Corporate credit risk modeling and the macroeconomy. Journal of Banking & Finance, 31(3), 845-868. Cole, R., Gunther, J., & Cornyn, B. (1995). FIMS: A New Financial Institutions Monitoring System for Banking Organizations. Federal Reserve Bulletin, 81, 1-15. Cole, R. A., & Gunther, J. W. (1998). Predicting bank failures: A comparison of on-and off-site monitoring systems. Journal of Financial Services Research, 13(2), 103-117. Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society. Series B (Methodological), 187-220. Crouhy, M., Galai, D., & Mark, R. (2000). A comparative analysis of current credit risk models. Journal of Banking & Finance, 24(1), 59-117. Demirgüç-Kunt, A. (1989). Modeling large commercial-bank failures: a simultaneous-equation analysis: Federal Reserve Bank of Cleveland, Research Department.Duffie, D., Saita, L., & Wang, K. (2007). Multi-period corporate default prediction with stochastic covariates. Journal of Financial Economics, 83(3), 635-665. Espahbodi, P. (1991). Identification of problem banks and binary choice models. Journal of Banking & Finance, 15(1), 53-71. Gajewski, G. R. (1989). Assessing the risk of bank failure. Paper presented at the Federal Reserve Bank of Chicago Proceedings.Hillegeist, S. A., Keating, E. K., Cram, D. P., & Lundstedt, K. G. (2004). Assessing the probability of bankruptcy. Review of Accounting Studies, 9(1), 5-34. Hoggarth, G., Reis, R., & Saporta, V. (2002). Costs of banking system instability: some empirical evidence. Journal of Banking & Finance, 26(5), 825-855. Hull, J. (1989). Assessing credit risk in a financial institution`s off-balance sheet commitments. Journal of Financial and Quantitative Analysis, 24(4), 489-501. Jagtiani, J., & Lemieux, C. (2001). Market discipline prior to bank failure. Journal of Economics and Business, 53(2), 313-324. Lane, W. R., Looney, S. W., & Wansley, J. W. (1986). An application of the Cox proportional hazards model to bank failure. Journal of Banking & Finance, 10(4), 511-531. Lawless, J. F. (2003). Statistical models and methods for lifetime data (Vol. 362): John Wiley & Sons.Lee, S. H., & Urrutia, J. L. (1996). Analysis and prediction of insolvency in the property-liability insurance industry: a comparison of logit and hazard models. Journal of Risk and Insurance, 121-130. Lennox, C. (1999). Identifying failing companies: a re-evaluation of the logit, probit and DA approaches. Journal of Economics and Business, 51(4), 347-364. Levine, R. (2005). Finance and growth: theory and evidence. Handbook of economic growth, 1, 865-934. Martin, D. (1977). Early warning of bank failure: A logit regression approach. Journal of Banking & Finance, 1(3), 249-276. Meyer, P. A., & Pifer, H. W. (1970). Prediction of bank failures. The Journal of Finance, 25(4), 853-868. Nam, C. W., Kim, T. S., Park, N. J., & Lee, H. K. (2008). Bankruptcy prediction using a discrete‐time duration model incorporating temporal and macroeconomic dependencies. Journal of Forecasting, 27(6), 493-506. Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of accounting research, 18(1), 109-131. Pettway, R. H., & Sinkey, J. F. (1980). Establishing On‐Site Bank Examination Priorities: An Early‐Warning System Using Accounting and Market Information. The Journal of Finance, 35(1), 137-150. Shumway, T. (2001). Forecasting bankruptcy more accurately: A simple hazard model*. The Journal of Business, 74(1), 101-124. Singer, J. D., & Willett, J. B. (1993). It’s about time: Using discrete-time survival analysis to study duration and the timing of events. Journal of Educational and Behavioral Statistics, 18(2), 155-195. Singer, J. D., & Willett, J. B. (2003). Applied longitudinal data analysis: Modeling change and event occurrence: Oxford university press.Sinkey, J. F. (1975). A multivariate statistical analysis of the characteristics of problem banks. The Journal of Finance, 30(1), 21-36. Thomson, J. B. (1992). Modeling the bank regulator`s closure option: a two-step logit regression approach. Journal of Financial Services Research, 6(1), 5-23. Tutz, G., & Pritscher, L. (1996). Nonparametric estimation of discrete hazard functions. Lifetime Data Analysis, 2(3), 291-308. West, R. C. (1985). A factor-analytic approach to bank condition. Journal of Banking & Finance, 9(2), 253-266. Whalen, G. (1991). A proportional hazards model of bank failure: an examination of its usefulness as an early warning tool. Federal Reserve Bank of Cleveland Economic Review, 27(1), 21-31. Wheelock, D. C., & Wilson, P. W. (2000). Why do banks disappear? The determinants of US bank failures and acquisitions. Review of Economics and Statistics, 82(1), 127-138. Zmijewski, M. E. (1984). Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research, 22, 59-82. zh_TW
