建議與未來研究方向

國

立 政 治 大 學

‧

N a

tio na

l C h engchi U ni ve rs it y

40

第二節 建議與未來研究方向

在研究方法上，本研究是假設各銀行發生財務危機事件是互相獨立，然而 近年來，有許多學者發現信用風險傳染在金融海嘯時期與銀行的違約有重大相 關，因此後續研究可以加入信用風險傳染因子或是脆弱因子(frailty)來解決不獨 立的問題;此外，在變數選取方面，本研究並未考量資產負債表外交易項目，主 要原因為過去金融當局過度放任金融業，衍生性商品交易僅以附註形式呈現，

並未以公允價值衡量，因此資料殘缺不全。金融海嘯過後，金融當局開始重視 表外資產揭露，因此後續研究可考量在模型中加入資產負債表外交易項目，以 提升模型預測能力。最後過去學者也建議可用指數平滑法估計基期風險。

‧

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.

‧ 國

立 政 治 大 學

‧

N a

tio na

l C h engchi U ni ve rs it y

45

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.