| 摘要(英) |
The rapid changes and violent competitions of the economy have not only let the enterprise face more risk and uncertainties but also let the banks have to take more risk on the commercial loan. Therefore, besides looking for the outstanding accomplishments and profits banks need to control the degrees of exposed risk and expected loss. There have been a lot of researches about how to measure the probability of default. But there is another thing that the banks pay more attention to. That is the recoveries from the enterprise that has defaulted. If the recoveries are low, the losses of the bank will be a lot. We attempt to fount a model to estimate the recovery rate. We also hope that banks can use the model to measure the loss of default before an enterprise really defaults. Additionally, we also discuss the relationship between default rate and recovery rate and the relationship between economy and recovery rate. When we establish the recovery predict model, we will consider default rate and economic situation.
According to the results of empirical study, the conclusions are as follow:
1. The relationship between the default rate and the recovery rate is negative. When the default rate is low, the recovery rate is high. In other words, when the default rate is high the recovery rate will be low.
2. Using the stock return and economic growth rate as the proxy variables of the economy. We find that when the economy blossom the recovery rate is high; when the economy decline the recovery rate is low.
3. The obvious variables in the model are the scale of corporate, default rate, use ratio, average recovery rate of collateral, numbers of correspondent banks, and stock index return.
4. The most important factor that influences the recovery rate is collateral. Economic situation is the second important factor. Therefore, the policy of making loans is the most important thing that determine the recovery rate. If banks request more collaterals banks will suffer less uncertainty. |
| 參考文獻 |
國內部分
1.陳肇榮（1983），「運用財務比率預測財務危機之實證研究」，政治大學企業管理研究所博士論文。
2.黃小玉（1988），「銀行放款信用評估模式之研究–最佳模式之選擇」，淡江大學管理科學研究所碩士論文。
3.張紘炬、潘玉葉，「財務預警分析與台灣股票上市公司財務基本資料關係之探討」，台北市銀月刊，第二十一卷第六期，第12至14頁。
4.黃俊雄（1994），「財務危機預警模式建立與驗證」，東吳大學管理科學研究所碩士論文。
5.陳錦村、許通安、林蔓蓁（1996），「銀行授信客戶違約風險之預測」，管理科學學報，第十三卷第二期，第173至195頁。
6.龔志明（1999），「財務危機預測模型之跨期性研究」，國立中山大學財務管理研究所碩士論文。
7.紀景耀（1999），「信用風險下可轉換公司債之評價」，政治大學金融學系碩士論文。
8.施淑萍（1999）「財務危機預警模式與財務危機企業財務特性之研究」，東吳大學會計學系碩士論文。
9.陳雅惠（2000），「以信用等級變動衡量信用風險–以無擔保公司債為例」，東吳大學會計學系碩士論文。
10.林建州（2001），「銀行授信客戶違約機率之衡量」，中山大學財務管理研究所碩士論文。
11.楊博仁（2002），「信用風險值–台灣企業違約後償還率之探討」，東吳大學會計學系碩士論文。
12.白欽元（2003），「國內中小企業財務危機預警模型」，交通大學經營管理研究所碩士論文。
13.卜怡君（2003），「運用灰關聯聚類與適應性類神經模糊推論系統於本國銀行業之信用評等」，台灣科技大學資訊管理學系碩士論文。
14.許志鈞（2003），「企業財務危機預警模式~基因演算法、倒傳遞網路與遞迴網路之應用」，台北大學企業管理學系碩士論文。
國外部分
1.Altman, Edward I, R.G. Haldeman and P. Narayanan（1977）,”Zeta analysis：A new model to identify bankruptcy risk of corporation”, Journal of Banking and Finance, Vol.1, June, pp.29-54.
2.Altman Edward I. and V. Kishore（1996）,”Almost Everything You Wanted To Know About Recoveries On Default Bonds”, Financial Analysts Journal, Nov/Dec, pp.57-64.
3.Altman Edward I., Andrea Resti and Andrea Sironi（2001）,”Analyzing and Explaining Default Recovery Rates”. Working paper.
4.Altman Edward I. and Mario Onorato（2003） “An Integrated Pricing Model for Defaultable Loans and Bonds”. Working paper.
5.Asarnow E. and Edwards D, Risk Management Associates（1995）,“Managing Loan Loss on Defaulted Bank Loans：A 24-year Study.”, Journal of Commercial Lending.
6.Beaver, W.H.（1966）,”Financial ratios as predictors of failures：Empirical research in accounting-selected studies”, Journal of Accounting Research, pp.71-111.
7.Carty, Lea V. and D. Lieberman（1996）,”Defaulted Bank Loan Recoveries”, Moody’s Special Comment, November, pp.1-9.
8.Esa Jakivuolle and Samu Peura（2000）,”A Model for Estimating Recovery Rates and Collateral Haircuts for Bank Loans”, Working paper.
9.Foster, B.P., Ward, T.J. and Woodroof（1998）,”An analysis of the usefulness of Debt defaults and Going Concern Opinions in Bankruptcy Risk Assessment.”, Journal of Accounting Auditing & Finance, Summer, pp.351-371.
10.Frye J.（2000a）,”Collateral Damage：A Sourca of Systematic Credit Risk”, Risk, April, pp91-94.
11.Frye J.（2000b）,”Depressing Recoveries”, Risk, November, pp106-111.
12.Gentry, J.A., P. Newbold and D.T. Whitford（1985）”Classifying Bankrupt Firms with Funds Flow Components”, The Journal of Accounting Research, Vol.23, No.1, pp.146-160.
13.Greg M. Gupton, Roger M. Stein（2002）,”LossCalcTM：Moody’s Model for Predicting Loss Given Dwfault”, Moody’s Special Comment, February, pp.3-34.
14.Gupton, Greg M., D. Gates, and Lea V. Carty（2000）,”Bank Loan Loss Given Default” Moody’s Special Comment, November, pp.1-15.
15.M. Pikhtin（2003）”Unexpected recovery risk”, Risk, August, p74-78.
16.Menard, Scott（1995）Applied Logistic Regression Analysis. Thousand Oaks, CA：SAGE Publications, Inc.
17.Nagelkerke, N. J. D（1991）”A note on a general definition of the coefficient of determination.”, Biometrika, Vol78, pp125-137.
18.Ohlson, J.A.（1980）”Financial Ratios and the Probability Prediction of Bankruptcy”, Journal of Accounting Research, Vol.18, No.1, pp.109-131.
19.Platt, H.D.and Marjorie B. Platt（1991）”A note on the use of industry-relative ratios bankruptcy prediction”, Journal of Banking and Finance Vol15, pp.1183-1194.
20.Til Schuermann（2003）”What Do We Know About Loss-Given-Default?”, Financial Institutions Center.
21.Yen-Ting Hu and William Perraudin（2002）,”The Dependence of Recovery Rates and Defaults”, Working paper. |
[Endnote RIS 格式]
[相關文章]
[文章引用]
[完整記錄]
[館藏目錄]
[檢視]
plurk
funp
live
udn
HD
myshare
netvibes
friend
youpush
delicious
baidu
Google bookmarks
del.icio.us
hemidemi
facebook
twitter
google
reddit