我国商业银行房地产信贷风险度量研究
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摘要
信贷资产是银行的主要资产,而银行又是积聚风险和进行风险交易的天然场所,信贷风险管理自然成为银行业永恒的话题,而我国现阶段进行房地产信贷风险管理的研究又显得尤为重要。
随着计量经济学、信息技术的发展和运用,国际上一些主要的金融机构纷纷开发了各种信贷风险度量模型。根据我国商业银行房地产信贷的特点,本文基于Credit Portfolio View模型的基本思想,构建房地产信贷风险度量模型。首先,本文对房地产信贷风险度量的涵义作了介绍,从可能性和必要性两方面分析房地产信贷风险度量的重要性;其次,对信贷风险度量模型的发展进行回顾,在详细对比和分析了4种现代主要的房地产信贷风险度量模型的基础上,结合我国商业银行的风险管理现状,以CPV模型的基本思想作为中国商业银行房地产信贷风险建模的依据。再次,本文选取2001年1月—2007年9月间的综合领先指标、国房景气指数和企业景气指数三个宏观经济变量值以及房地产信贷违约率的值,运用Eviews软件作回归分析,对房地产信贷风险进行度量,并进一步用新模型预测了2007年第四季度的房地产信贷违约率的值,通过与用指数平滑法预测出的值相比较,说明新建的回归模型具有很强的适用性。最后,从风险度量角度提出几点降低我国商业银行房地产信贷风险的建议。
本文得出的结论是,Credit Portfolio View模型在度量和预测房地产信贷违约率方面具有高度的合理性和有效性,可以为商业银行的信贷风险防范提供很好的依据。并且,证实房地产信贷违约率和宏观经济状况紧密相连,当经济恶化时,房地产信贷违约率上升;经济好转时,房地产信贷违约率下降。
Credit assets are dominating ones for banks, while banks are the natural market for accumulating and trading risks. As a result, credit risk managemet comes to be an eternal subject. At present, it is important for China to make research on credit risk management for real estate.
With the development of econometrics and computer technology, many major financial organizations developed various credit management models. According to the characteristics of the real estate credit in China, Credit Portfolio View model is choosed to measure and forecast the credit risk of real estate. First of all, this paper gives some details for the related concepts of real estate credit and especially points out that it is possible and necessary to carry out the credit risk measurement for real estate.Secondly, it reviews the development of credit risk measurement models and compares the third-generations credit risk ones. It combines with the conditions in Chinese commercial banks to show the advantage of using CPV model to measure and forecast the credit risk of real estate. Thirdly, in terms of choosing three macroeconomic varies (Composite Leading Indicator, Macroeconomic Index.and China Real Estate Climate Index), and the transformation value of the default probability of real estate credit. This paper gives an estimate of CPV model with the‘Eviews’software By comparing the fitted value and real value of the default probability of real estate credit, CPV model shows efficiency to measure the credit risk of real estate. Then, this model is used to forecast the default probability of real estate for Novemeber, 2007. Finally, based on the credit risk measurement, from possibilitiy and necessity aspects, the ways on how to decrease credit risk are given.
This paper can come to a conclusion that, CPV model is full of high rationality and efficiency to measure and forecast the default probability of real estate credit risk and can offer the robust references for the commercial banks to defense the credit risks. Meanwhile, it also confirms that default probability is linked to economy closely. When the economy worsens, default increases; when the economy becomes stronger, default decreases.
随着计量经济学、信息技术的发展和运用,国际上一些主要的金融机构纷纷开发了各种信贷风险度量模型。根据我国商业银行房地产信贷的特点,本文基于Credit Portfolio View模型的基本思想,构建房地产信贷风险度量模型。首先,本文对房地产信贷风险度量的涵义作了介绍,从可能性和必要性两方面分析房地产信贷风险度量的重要性;其次,对信贷风险度量模型的发展进行回顾,在详细对比和分析了4种现代主要的房地产信贷风险度量模型的基础上,结合我国商业银行的风险管理现状,以CPV模型的基本思想作为中国商业银行房地产信贷风险建模的依据。再次,本文选取2001年1月—2007年9月间的综合领先指标、国房景气指数和企业景气指数三个宏观经济变量值以及房地产信贷违约率的值,运用Eviews软件作回归分析,对房地产信贷风险进行度量,并进一步用新模型预测了2007年第四季度的房地产信贷违约率的值,通过与用指数平滑法预测出的值相比较,说明新建的回归模型具有很强的适用性。最后,从风险度量角度提出几点降低我国商业银行房地产信贷风险的建议。
本文得出的结论是,Credit Portfolio View模型在度量和预测房地产信贷违约率方面具有高度的合理性和有效性,可以为商业银行的信贷风险防范提供很好的依据。并且,证实房地产信贷违约率和宏观经济状况紧密相连,当经济恶化时,房地产信贷违约率上升;经济好转时,房地产信贷违约率下降。
Credit assets are dominating ones for banks, while banks are the natural market for accumulating and trading risks. As a result, credit risk managemet comes to be an eternal subject. At present, it is important for China to make research on credit risk management for real estate.
With the development of econometrics and computer technology, many major financial organizations developed various credit management models. According to the characteristics of the real estate credit in China, Credit Portfolio View model is choosed to measure and forecast the credit risk of real estate. First of all, this paper gives some details for the related concepts of real estate credit and especially points out that it is possible and necessary to carry out the credit risk measurement for real estate.Secondly, it reviews the development of credit risk measurement models and compares the third-generations credit risk ones. It combines with the conditions in Chinese commercial banks to show the advantage of using CPV model to measure and forecast the credit risk of real estate. Thirdly, in terms of choosing three macroeconomic varies (Composite Leading Indicator, Macroeconomic Index.and China Real Estate Climate Index), and the transformation value of the default probability of real estate credit. This paper gives an estimate of CPV model with the‘Eviews’software By comparing the fitted value and real value of the default probability of real estate credit, CPV model shows efficiency to measure the credit risk of real estate. Then, this model is used to forecast the default probability of real estate for Novemeber, 2007. Finally, based on the credit risk measurement, from possibilitiy and necessity aspects, the ways on how to decrease credit risk are given.
This paper can come to a conclusion that, CPV model is full of high rationality and efficiency to measure and forecast the default probability of real estate credit risk and can offer the robust references for the commercial banks to defense the credit risks. Meanwhile, it also confirms that default probability is linked to economy closely. When the economy worsens, default increases; when the economy becomes stronger, default decreases.
引文
[1]Altman,E.I.,Saunder,A.1998,Credit risk measurement:development over the last 20 years,J.Bank&Finance.
[2]Hellmam T F, Murdock KC, Stiglitz J E.Liberalization, Moral Hazard in Banking, and Prudential Regulation: Are Capital Requirements Enough [J]. American Economic Review, 2000, (1):147-165.
[3]Dume,Darrell&KennethJ Singleton.ModelingTemrStruetuersofDeafultableBondS[J],Revie Wof Financial Studies,1999,(12):687一720
[4]Chun sheng zhou.AJump一diuffsion Approaeh to Modeling Credit Risk and Valuing Deafultable Securities,Wokring Paper,Federal Resevre Board.1997
[5]王东卫,现代银行全面风险管理[M],北京:中国经济出版社,2001.1
[6]张玲,张佳林,信用风险评估方法发展趋势[J],预测,2000(4):14-23
[7]Carty, LeaV, Dana Lieberman, 1996, Default Bank Loan Recoveries, Moodys Investors Service, Global Credit Research, Special Report, November
[8]Merton, R, C, On the Pricing of Corporate Debt: The Risk Structure of Interest Rates [J], Journal of Finance
[9]Serbigny, de, A, Renault, Measuring and Managing the Credit Risk [M], McGraw-Hill, 2004
[10]Lore,M,Borodosky,L,The Professional’s Handbook of Financial Risk Management[M],Reed Educational and Professional Publishing,2000
[11]Nelken, I, Implementing Credit Derivatives [M], Mc Graw-Hill, 1999
[12]Michael K.Ong, Credit Rating Methodologies, Rational and Default Risk [M], Risk Books, 2002
[13]Saunders, A, Credit Risk Management [M], John Wiley&Sons, 1999
[14]Golin, The Bank CreditAnalysis Handbook: A Guide forAnalysts, Bankers and Investors [M], John Wiley&Sons (Asia) Pre.Ltd, 2001
[15]Stiglitz, Weiss. A, Credit Rationing with Imperfect Information [J], American Economic Review
[16]Maria Stepanova, Lyn Thomas, Survival Analysis Methods for Personal Loan Data, Operations Research, 2002 INFORMS, Vol50, No.2, March–April 2002, pp.277–289.
[17]Bart Baesens,Neural Network Survival Analysis for Personal Loan Data,Faculteit Economien Bedrijfskunde,working paper,November 2004.
[18]John C.Wiginton,A Note on the Comparison of Logit and Discriminant Models of Consumer Credit Behavior. The Journal of Financial and Quantitative Analysing.Vol.15, No.3 (Sep.1980), pp: 757-770.
[19]J.S.Cramer, Scoring Bank Loans That May Go Wrong:a Case Study,University of Amsterdam and Tinbergen Institute,Keizersgracht 482,1017 EG Amsterdam,October 20,2000.
[20]Y Liu and M Schumann, Data mining feature selection for credit scoring models, Journal of the Operational Research Society (2005).1–10.
[21]Thomas Parnitzke,,Credit scoring and the sample selection bias,Institute of Insurance Economics University of St.Gallen Kirchlistrasse 2,9010 St.Gallen,Switzerland,May 31,2005.
[22]彭书杰、詹远瑞,国内外两种信用风险模型的比较与剖析[J],甘肃科学学报,2002,(3)
[23]沈沛龙、任若恩,现代信用风险管理模型和方法的比较研究[J],经济科学,2002,(3)
[24]王琼、陈金贤,信用风险定价方法与模型研究[J],现代财经,2002,(4)
[25]彭彰,商业银行信用风险管理-——兼论巴塞尔新资本协议[M].北京:中国人民大学出版社,2002
[26]易丹辉、吴建民,上市公司信用风险计量研究[J].统计与信息论坛,2004(11)
[27]惠晓峰、孙嘉鹏,商业银行信用风险识别:信用矩阵的实证应用研究[J],国际金融研究,2004,
[28]朱小宗、张宗益、耿丹华,现代信用风险度量模型剖析与综合比较分析[J],财经研究,2004
[29]霍学喜、张永娟,信用组合风险模型的构建及其应用[J],西北农林科技大学学报,2004
[30]杨文翰,商业银行信用风险度量模型与管理研究[D],南京航空航天大学博士学位论文,2006
[31]符国伟,个人住房抵押贷款模型的实证研究[D],湖南大学硕士学位论文,2005
[32]陈则明、吴光伟,住房抵押贷款中业主违约现象的分析[J],中国房地产,2001.11
[33]刘萍,个人住房抵押贷款风险探析[J],金融研究,2002.8
[34]徐建斌,银行房地产信贷形成的微观机制及其防范措施[J],投资研究,2003.8
[35]王妙英,商业银行房地产贷款风险研究[D],哈尔滨工业大学硕士学位论文,2001
[36]韩继云,房地产信贷风险剖析及防范措施[J],宏观经济管理,2003.8
[37]刘梓,房地产信贷风险与防范措施[J],中国金融,2004.7
[38]刘春红,我国房地产抵押贷款风险及其对策研究和实证分析[D],上海交通大学博士学位论文,2000
[39]杨星,麦元勋,个人住房贷款信用风险管理实证研究[J],南方金融,2003.3
[40]王福林,个人住房抵押违约贷款风险影响因素实证研究[D],浙江大学博士学位论文,2004
[41]朱勇,房地产金融风险研究[D],中国人民大学博士学位论文,1999
[42]邓源,国有商业银行房地产贷款风险分析与防范,重庆大学工程硕士论文,2005
[43]王仁祥,喻平,《金融风险管理》武汉理工大学出版社武汉2004,P:252-261
[44]廖昌开,商业银行房地产信贷.北京:中国金融出版社,1999
[45]Basel Committee on Banking Supervision, International Convergence of Capital, Measurement and Capital Standards[R] Basle, July, 1988
[46]杨军,银行信用风险——理论、模型和实证分析[M],北京:中国财政经济出版社,2004
[47]Caouette.J.B; E.J.Altman and P.Narayanan, Managing Credit Risk.John Wiley & Sons, New York, 1998, p:87-90
[48]孙立坚,金融经济学[M].北京:高等教育出版社,2004:447-448
[49]陆超,信用风险度量研究[D],北京:北京交通大学,2004
[50]KMV Corporation.Credit Monitor Overview [M],San Francisco California.1993
[51]Altman EI, SaundersA·Credit riskmeasurement: Developments over the last 20 years [J]Journal of Banking&Finance 21:1721-1742, 1997
[52]Michel Crouhy,,Dan Galai and Robert Mark.Risk Management[M].New York: McGraw-Hil
[53]陈德胜,冯宗宪,国际主流信用风险度量技术的比较[J],经济论坛, 2004, (22):77-79
[54]国际合作与发展组织官方网站www.oecd.org
[55]宁宣熙,刘思峰,管理预测与决策方法[M],北京:科学出版社,2003:82
[56]崔滨洲,新巴塞尔协议与中国银行业的全面风险管理[J].武汉大学学报(哲学社会科学版),2004,(3):364-369
[2]Hellmam T F, Murdock KC, Stiglitz J E.Liberalization, Moral Hazard in Banking, and Prudential Regulation: Are Capital Requirements Enough [J]. American Economic Review, 2000, (1):147-165.
[3]Dume,Darrell&KennethJ Singleton.ModelingTemrStruetuersofDeafultableBondS[J],Revie Wof Financial Studies,1999,(12):687一720
[4]Chun sheng zhou.AJump一diuffsion Approaeh to Modeling Credit Risk and Valuing Deafultable Securities,Wokring Paper,Federal Resevre Board.1997
[5]王东卫,现代银行全面风险管理[M],北京:中国经济出版社,2001.1
[6]张玲,张佳林,信用风险评估方法发展趋势[J],预测,2000(4):14-23
[7]Carty, LeaV, Dana Lieberman, 1996, Default Bank Loan Recoveries, Moodys Investors Service, Global Credit Research, Special Report, November
[8]Merton, R, C, On the Pricing of Corporate Debt: The Risk Structure of Interest Rates [J], Journal of Finance
[9]Serbigny, de, A, Renault, Measuring and Managing the Credit Risk [M], McGraw-Hill, 2004
[10]Lore,M,Borodosky,L,The Professional’s Handbook of Financial Risk Management[M],Reed Educational and Professional Publishing,2000
[11]Nelken, I, Implementing Credit Derivatives [M], Mc Graw-Hill, 1999
[12]Michael K.Ong, Credit Rating Methodologies, Rational and Default Risk [M], Risk Books, 2002
[13]Saunders, A, Credit Risk Management [M], John Wiley&Sons, 1999
[14]Golin, The Bank CreditAnalysis Handbook: A Guide forAnalysts, Bankers and Investors [M], John Wiley&Sons (Asia) Pre.Ltd, 2001
[15]Stiglitz, Weiss. A, Credit Rationing with Imperfect Information [J], American Economic Review
[16]Maria Stepanova, Lyn Thomas, Survival Analysis Methods for Personal Loan Data, Operations Research, 2002 INFORMS, Vol50, No.2, March–April 2002, pp.277–289.
[17]Bart Baesens,Neural Network Survival Analysis for Personal Loan Data,Faculteit Economien Bedrijfskunde,working paper,November 2004.
[18]John C.Wiginton,A Note on the Comparison of Logit and Discriminant Models of Consumer Credit Behavior. The Journal of Financial and Quantitative Analysing.Vol.15, No.3 (Sep.1980), pp: 757-770.
[19]J.S.Cramer, Scoring Bank Loans That May Go Wrong:a Case Study,University of Amsterdam and Tinbergen Institute,Keizersgracht 482,1017 EG Amsterdam,October 20,2000.
[20]Y Liu and M Schumann, Data mining feature selection for credit scoring models, Journal of the Operational Research Society (2005).1–10.
[21]Thomas Parnitzke,,Credit scoring and the sample selection bias,Institute of Insurance Economics University of St.Gallen Kirchlistrasse 2,9010 St.Gallen,Switzerland,May 31,2005.
[22]彭书杰、詹远瑞,国内外两种信用风险模型的比较与剖析[J],甘肃科学学报,2002,(3)
[23]沈沛龙、任若恩,现代信用风险管理模型和方法的比较研究[J],经济科学,2002,(3)
[24]王琼、陈金贤,信用风险定价方法与模型研究[J],现代财经,2002,(4)
[25]彭彰,商业银行信用风险管理-——兼论巴塞尔新资本协议[M].北京:中国人民大学出版社,2002
[26]易丹辉、吴建民,上市公司信用风险计量研究[J].统计与信息论坛,2004(11)
[27]惠晓峰、孙嘉鹏,商业银行信用风险识别:信用矩阵的实证应用研究[J],国际金融研究,2004,
[28]朱小宗、张宗益、耿丹华,现代信用风险度量模型剖析与综合比较分析[J],财经研究,2004
[29]霍学喜、张永娟,信用组合风险模型的构建及其应用[J],西北农林科技大学学报,2004
[30]杨文翰,商业银行信用风险度量模型与管理研究[D],南京航空航天大学博士学位论文,2006
[31]符国伟,个人住房抵押贷款模型的实证研究[D],湖南大学硕士学位论文,2005
[32]陈则明、吴光伟,住房抵押贷款中业主违约现象的分析[J],中国房地产,2001.11
[33]刘萍,个人住房抵押贷款风险探析[J],金融研究,2002.8
[34]徐建斌,银行房地产信贷形成的微观机制及其防范措施[J],投资研究,2003.8
[35]王妙英,商业银行房地产贷款风险研究[D],哈尔滨工业大学硕士学位论文,2001
[36]韩继云,房地产信贷风险剖析及防范措施[J],宏观经济管理,2003.8
[37]刘梓,房地产信贷风险与防范措施[J],中国金融,2004.7
[38]刘春红,我国房地产抵押贷款风险及其对策研究和实证分析[D],上海交通大学博士学位论文,2000
[39]杨星,麦元勋,个人住房贷款信用风险管理实证研究[J],南方金融,2003.3
[40]王福林,个人住房抵押违约贷款风险影响因素实证研究[D],浙江大学博士学位论文,2004
[41]朱勇,房地产金融风险研究[D],中国人民大学博士学位论文,1999
[42]邓源,国有商业银行房地产贷款风险分析与防范,重庆大学工程硕士论文,2005
[43]王仁祥,喻平,《金融风险管理》武汉理工大学出版社武汉2004,P:252-261
[44]廖昌开,商业银行房地产信贷.北京:中国金融出版社,1999
[45]Basel Committee on Banking Supervision, International Convergence of Capital, Measurement and Capital Standards[R] Basle, July, 1988
[46]杨军,银行信用风险——理论、模型和实证分析[M],北京:中国财政经济出版社,2004
[47]Caouette.J.B; E.J.Altman and P.Narayanan, Managing Credit Risk.John Wiley & Sons, New York, 1998, p:87-90
[48]孙立坚,金融经济学[M].北京:高等教育出版社,2004:447-448
[49]陆超,信用风险度量研究[D],北京:北京交通大学,2004
[50]KMV Corporation.Credit Monitor Overview [M],San Francisco California.1993
[51]Altman EI, SaundersA·Credit riskmeasurement: Developments over the last 20 years [J]Journal of Banking&Finance 21:1721-1742, 1997
[52]Michel Crouhy,,Dan Galai and Robert Mark.Risk Management[M].New York: McGraw-Hil
[53]陈德胜,冯宗宪,国际主流信用风险度量技术的比较[J],经济论坛, 2004, (22):77-79
[54]国际合作与发展组织官方网站www.oecd.org
[55]宁宣熙,刘思峰,管理预测与决策方法[M],北京:科学出版社,2003:82
[56]崔滨洲,新巴塞尔协议与中国银行业的全面风险管理[J].武汉大学学报(哲学社会科学版),2004,(3):364-369