基于Stackelberg博弈的房地产企业授信额度研究
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摘要
近年来,我国房地产金融市场上银行对开发商的过度支持及开发商对银行的过度依赖使得房地产行业的特定风险正逐步向银行机构转移。落实授信管理、对风险和收益进行统筹规划是保证银行对房地产企业授信安全性和盈利性的重要手段,而授信额度的核定作为授信管理的核心,已成为当前银行所面临的重要决策问题。
本文以银行房地产企业授信额度为研究内容,采用理论结合实际的研究方法,探讨银行如何核定授信额度以实现风险和收益的优化。首先,通过对银行房地产企业授信现状的分析,讨论了当前银行对房地产企业采取的主要授信模式及面临的问题,并分析了现行授信额度核定方法在控制风险及提高效益方面的局限性。然后,在论述Stackelberg博弈基本内容的基础上,深入剖析了授信管理中银行与房地产开发商之间的博弈关系。在经济增加值(EVA)和风险调整的收益率(RAROC)框架下构建了银行授信的目标函数;以融资结构优化为依据构建了房地产企业的目标函数,最终建立二者的Stackelberg博弈模型,并给出了二者均衡解的求解方法。最后,对决定模型实用性的两个关键系数违约概率(PD)和违约损失率(LGD)的确定方法进行了探讨,在比较现有测度方法的技术需求及适用条件之后,分别讨论了运用人工神经网络估计法和清算数据贴现法确定PD和LGD的基本思路。
本文从理论上提出了银行核定房地产企业授信额度的新方法,为银行改进授信管理、控制房地产信贷风险提供了一定的理论支持,具有较强的现实意义。
In recent years, two problems in China’s real estate financial market are causing the gradual risk transfer from real estate industry to financial instructions, one is the excessive dependence of real estate developers on banks, while the other is the excessive financial support of banks to developers. Hence, implementing credit management to make coordinated planning of risk and return is an important approach to ensure banks’security and profitability, which makes line of credit, the core of credit management, an essential decision-making problem for every bank today.
This paper mainly researches on line of credit to real estate developers, using a method of theory combining with practice, it studies how banks should decide line of credit to optimize risk and return. First of all, basing on the analysis about present situation of banks’credit to real estate developers, it discusses some main problems every bank faces under today’s credit pattern and gives the summary of several methods widely used by banks to calculate line of credit, as well as their deficiency in risk control and benefit enhancement. Secondly, after the introduction of Stackelberg game theory, the paper makes a deep analysis on the Stackerlberg relationship between developers and banks during the credit service, after that an objective function of banks according to the structures of economic value added(EVA)and risk adjusted return on capital (RAROC) is designed, so as the target function of real estate developers according to best financial structure, thus the Stackelberg model is built and equilibrium solution is discussed in a simple way. Finally, it studies how to decide two important parameters in the model, probability of default(PD) and loss given default(LGD), both of which influence the practicality of the model seriously. After the comparison of technical requirements and applicable conditions of existing methods to assess the two parameters, it gives separately the basic processes to estimate PD using artificial neural network and LGD using workout value measurement.
This paper proposes a new method to decide line of credit to real estate developers theoretically, providing banks with certain theoretical support to improve credit management and control the risk of credit to real estate industry, so the research is quite meaningful in practice.
本文以银行房地产企业授信额度为研究内容,采用理论结合实际的研究方法,探讨银行如何核定授信额度以实现风险和收益的优化。首先,通过对银行房地产企业授信现状的分析,讨论了当前银行对房地产企业采取的主要授信模式及面临的问题,并分析了现行授信额度核定方法在控制风险及提高效益方面的局限性。然后,在论述Stackelberg博弈基本内容的基础上,深入剖析了授信管理中银行与房地产开发商之间的博弈关系。在经济增加值(EVA)和风险调整的收益率(RAROC)框架下构建了银行授信的目标函数;以融资结构优化为依据构建了房地产企业的目标函数,最终建立二者的Stackelberg博弈模型,并给出了二者均衡解的求解方法。最后,对决定模型实用性的两个关键系数违约概率(PD)和违约损失率(LGD)的确定方法进行了探讨,在比较现有测度方法的技术需求及适用条件之后,分别讨论了运用人工神经网络估计法和清算数据贴现法确定PD和LGD的基本思路。
本文从理论上提出了银行核定房地产企业授信额度的新方法,为银行改进授信管理、控制房地产信贷风险提供了一定的理论支持,具有较强的现实意义。
In recent years, two problems in China’s real estate financial market are causing the gradual risk transfer from real estate industry to financial instructions, one is the excessive dependence of real estate developers on banks, while the other is the excessive financial support of banks to developers. Hence, implementing credit management to make coordinated planning of risk and return is an important approach to ensure banks’security and profitability, which makes line of credit, the core of credit management, an essential decision-making problem for every bank today.
This paper mainly researches on line of credit to real estate developers, using a method of theory combining with practice, it studies how banks should decide line of credit to optimize risk and return. First of all, basing on the analysis about present situation of banks’credit to real estate developers, it discusses some main problems every bank faces under today’s credit pattern and gives the summary of several methods widely used by banks to calculate line of credit, as well as their deficiency in risk control and benefit enhancement. Secondly, after the introduction of Stackelberg game theory, the paper makes a deep analysis on the Stackerlberg relationship between developers and banks during the credit service, after that an objective function of banks according to the structures of economic value added(EVA)and risk adjusted return on capital (RAROC) is designed, so as the target function of real estate developers according to best financial structure, thus the Stackelberg model is built and equilibrium solution is discussed in a simple way. Finally, it studies how to decide two important parameters in the model, probability of default(PD) and loss given default(LGD), both of which influence the practicality of the model seriously. After the comparison of technical requirements and applicable conditions of existing methods to assess the two parameters, it gives separately the basic processes to estimate PD using artificial neural network and LGD using workout value measurement.
This paper proposes a new method to decide line of credit to real estate developers theoretically, providing banks with certain theoretical support to improve credit management and control the risk of credit to real estate industry, so the research is quite meaningful in practice.
引文
[1]王世豪.房地产信贷战略与实务[M].北京:中国金融出版社,2006:100-101.
[2]陈源.招商银行个人住房贷款业务风险与对策研究[D].四川:西南财经大学,2005:20-22.
[3] LU Chuiling.Return relationships between listed banks and real estate firms: evidence from seven Asian economics[J].The Journal of Real Estate Finance and Economics,2005,31(2):189-206.
[4] SHOCKLEY R L, THAKOR V.Bank loan commitment contracts:data,theory and tests [J].Journal of Money, Credit & Banking, 1997,(29):201-203.
[5] MELNIK A, PLAUT S.Loan commitment contracts,terms of lending and credit allocation[J].Journal of Finance,1986,(41):425-435.
[6] SUMIT Agarwal, SOUPHALA Chomsisengphet.Loan commitments and private firms[J].Finance and Economics Discussion Series,2004,(27):94-96.
[7] AMIR K.Bank lines of credit in corporate finance:an empirical analysis[R].USA:FDIC center for financial research,2005.
[8] BERGER A N, UDELL G..Relationship lending and lines of small firm finance[J].Journal of Business,1995,(68):351-382.
[9] DENNIS S, NANDY D.The determinants of contract terms in bank revolving credit agreement[J].Journal of Financial and Quantitative Analysis,2000,(35): 87-100.
[10] JEREMY Taylor.A unified approach to credit limit setting[J].RMA Journal,2002,84(10):56-61.
[11] LEIPPOLDY Markus.Optimal credit limit management under different information regimes[J].Journal of Banking & Finance,2006,30(2):463-487.
[12]牛锡明.我国商业银行贷款风险度管理的理论研究[J].经济研究,1998,(3):58-75.
[13]李敏玲.商业银行授信管理决策模型的研究[D].大连:大连理工大学,2002:19-22.
[14]李敬.农业银行最高综合授信额度核定模型的认识与思考[J].海南金融,2003,(8):18-20.
[15]卢信有.商业银行授信风险管理系统的量化模式初探[J].东北财经大学学报,2002,(2):45-48.
[16]程功,张维.基于信用评级的银行风险测算方法[J].管理科学,2006, 19(2): 36-42.
[17]何自力.授信额度核定的逻辑:一个共生理论的视角[J].金融论坛,2007,(1): 31-36.
[18]钟震宇.浅谈新巴塞尔协议下我国商业银行统一授信额度的测算[D].北京:清华大学,2005:28-39.
[19]王希颖.新巴塞尔协议对我国商业银行风险管理的借鉴意义[J].北京市经济干部管理学院学报,2007,22(1):44-48.
[20]屠世超.商业银行综合授信业务,北方经济,2005,(15):58-59.
[21]刘斌.国内股份制商业银行综合授信管理问题研究[J].国际金融研究,2005,(1):33-36.
[22]谢识予.经济博弈论[M].上海:复旦大学出版社,2002:8-10.
[23]王金炳.博弈论的发展历史和基本内容[J].时代经贸,2007,5(6):1-2.
[24]施锡铨.博弈论[M].上海:上海财经大学出版社,2000:6-10.
[25]陆晓明.银行风险管理的方向—全面风险管理[J].国际金融研究,1999,(8):53-55.
[26]王伟.巴塞尔协议的演进机理及发展趋势[J].安阳师范学院学报,2007,(1):34-35.
[27]张建刚,刘中文.商业银行全面风险管理体系构架与功能需求研究[J].2007,(14):32-34.
[28]邹志明.上市银行EVA与风险调整绩效指标模拟测算与分析[J].金融论坛,2007,(9):44-48.
[29]穆海权.现阶段我国商业银行房地产开发贷款问题分析[J].国际金融,2003,(5):43-45.
[30]蒋树宽.银企信用风险管理[M].广州:广东经济出版社,2001:66-69.
[31]李鹏雁,杨阳.商业银行房地产开发贷款风险分析[J].燕山大学学报,2007,31(2):26-28.
[32]贾将贤.房地产开发贷款风险管理[J].科技情报开发与经济,2007,17(29):182-183.
[33]杨帆,李宏谨.泡沫经济理论与中国房地产市场[J].管理科学,2005,(6):45-48.
[34]孔炜.浅谈房地产行业信贷风险及对策[J].河北金融,2005,(5):19-20.
[35]夏筱英.2001年以来我国房地产政策评述[J].银行家,2005,(7):90-93.
[36]李敬.农业银行最高综合授信额度核定模型的认识与思考海南金融[J].2003,(8):18-20.
[37]邓晓霞.中外资银行授信风险管理的比较及启示[J].2005,(1):54-56.
[38]何明.浅析客户最高综合授信额度的核定[J].安徽农村金融,2005,(11):26-27.
[39]郑应平.多人决策与博弈论[J].信息与控制,1987,(4):41-43.
[40]张建坤,张璞.基于层次分析法确定房地产融资结构的研究[J].基建优化,2004,(4):15-19.
[41]魏源.银行EVA评价体系的建立与应用[J].经济管理,2004,(13):22-25.
[42]陈四清.追求资本、风险、收益的动态平衡—我国商业银行风险管理机制改革的四大关系[J].银行家,2007,(1):46-47.
[43]王鑫,贾晓东.基于RAROC的银行经济资本配置研究[J].合作经济与技术,2007,(2):38-39.
[44]周兰珍.额度授信的实质及选择权设置[J].浙江金融,2003,(5):20-21.
[45]王春峰,万海晖.基于神经网络技术的商业银行信用风险评估[J].系统工程理论与实践,1999,(9):24-32.
[46] KANAK P.An empirical estimation of default risk of the UK real estate companies[J].Journal of Real Estate Finance and Economics,2006,32(1):21-40.
[47] FRYE J.Collateral damage detected[R].Chicago:Federal Reserve Bank of Chicago,2000.
[48]袁敏.债务违约损失率研究综述[J].外国经济与管理,2005,27(8):55-64.
[49]武剑.内部评级法中的违约损失率(LGD)模型——新资本协议核心技术研究[J].国际金融研究,2005,(2):15-22.
[50]陈忠阳.违约损失率(LGD)研究[J].国际金融研究,2004,(5):53-54.
[51]吴晓桃.从法律角度分析当前房地产开发贷款的风险与防范[J].福建金融,2006,(5):25-27.
[2]陈源.招商银行个人住房贷款业务风险与对策研究[D].四川:西南财经大学,2005:20-22.
[3] LU Chuiling.Return relationships between listed banks and real estate firms: evidence from seven Asian economics[J].The Journal of Real Estate Finance and Economics,2005,31(2):189-206.
[4] SHOCKLEY R L, THAKOR V.Bank loan commitment contracts:data,theory and tests [J].Journal of Money, Credit & Banking, 1997,(29):201-203.
[5] MELNIK A, PLAUT S.Loan commitment contracts,terms of lending and credit allocation[J].Journal of Finance,1986,(41):425-435.
[6] SUMIT Agarwal, SOUPHALA Chomsisengphet.Loan commitments and private firms[J].Finance and Economics Discussion Series,2004,(27):94-96.
[7] AMIR K.Bank lines of credit in corporate finance:an empirical analysis[R].USA:FDIC center for financial research,2005.
[8] BERGER A N, UDELL G..Relationship lending and lines of small firm finance[J].Journal of Business,1995,(68):351-382.
[9] DENNIS S, NANDY D.The determinants of contract terms in bank revolving credit agreement[J].Journal of Financial and Quantitative Analysis,2000,(35): 87-100.
[10] JEREMY Taylor.A unified approach to credit limit setting[J].RMA Journal,2002,84(10):56-61.
[11] LEIPPOLDY Markus.Optimal credit limit management under different information regimes[J].Journal of Banking & Finance,2006,30(2):463-487.
[12]牛锡明.我国商业银行贷款风险度管理的理论研究[J].经济研究,1998,(3):58-75.
[13]李敏玲.商业银行授信管理决策模型的研究[D].大连:大连理工大学,2002:19-22.
[14]李敬.农业银行最高综合授信额度核定模型的认识与思考[J].海南金融,2003,(8):18-20.
[15]卢信有.商业银行授信风险管理系统的量化模式初探[J].东北财经大学学报,2002,(2):45-48.
[16]程功,张维.基于信用评级的银行风险测算方法[J].管理科学,2006, 19(2): 36-42.
[17]何自力.授信额度核定的逻辑:一个共生理论的视角[J].金融论坛,2007,(1): 31-36.
[18]钟震宇.浅谈新巴塞尔协议下我国商业银行统一授信额度的测算[D].北京:清华大学,2005:28-39.
[19]王希颖.新巴塞尔协议对我国商业银行风险管理的借鉴意义[J].北京市经济干部管理学院学报,2007,22(1):44-48.
[20]屠世超.商业银行综合授信业务,北方经济,2005,(15):58-59.
[21]刘斌.国内股份制商业银行综合授信管理问题研究[J].国际金融研究,2005,(1):33-36.
[22]谢识予.经济博弈论[M].上海:复旦大学出版社,2002:8-10.
[23]王金炳.博弈论的发展历史和基本内容[J].时代经贸,2007,5(6):1-2.
[24]施锡铨.博弈论[M].上海:上海财经大学出版社,2000:6-10.
[25]陆晓明.银行风险管理的方向—全面风险管理[J].国际金融研究,1999,(8):53-55.
[26]王伟.巴塞尔协议的演进机理及发展趋势[J].安阳师范学院学报,2007,(1):34-35.
[27]张建刚,刘中文.商业银行全面风险管理体系构架与功能需求研究[J].2007,(14):32-34.
[28]邹志明.上市银行EVA与风险调整绩效指标模拟测算与分析[J].金融论坛,2007,(9):44-48.
[29]穆海权.现阶段我国商业银行房地产开发贷款问题分析[J].国际金融,2003,(5):43-45.
[30]蒋树宽.银企信用风险管理[M].广州:广东经济出版社,2001:66-69.
[31]李鹏雁,杨阳.商业银行房地产开发贷款风险分析[J].燕山大学学报,2007,31(2):26-28.
[32]贾将贤.房地产开发贷款风险管理[J].科技情报开发与经济,2007,17(29):182-183.
[33]杨帆,李宏谨.泡沫经济理论与中国房地产市场[J].管理科学,2005,(6):45-48.
[34]孔炜.浅谈房地产行业信贷风险及对策[J].河北金融,2005,(5):19-20.
[35]夏筱英.2001年以来我国房地产政策评述[J].银行家,2005,(7):90-93.
[36]李敬.农业银行最高综合授信额度核定模型的认识与思考海南金融[J].2003,(8):18-20.
[37]邓晓霞.中外资银行授信风险管理的比较及启示[J].2005,(1):54-56.
[38]何明.浅析客户最高综合授信额度的核定[J].安徽农村金融,2005,(11):26-27.
[39]郑应平.多人决策与博弈论[J].信息与控制,1987,(4):41-43.
[40]张建坤,张璞.基于层次分析法确定房地产融资结构的研究[J].基建优化,2004,(4):15-19.
[41]魏源.银行EVA评价体系的建立与应用[J].经济管理,2004,(13):22-25.
[42]陈四清.追求资本、风险、收益的动态平衡—我国商业银行风险管理机制改革的四大关系[J].银行家,2007,(1):46-47.
[43]王鑫,贾晓东.基于RAROC的银行经济资本配置研究[J].合作经济与技术,2007,(2):38-39.
[44]周兰珍.额度授信的实质及选择权设置[J].浙江金融,2003,(5):20-21.
[45]王春峰,万海晖.基于神经网络技术的商业银行信用风险评估[J].系统工程理论与实践,1999,(9):24-32.
[46] KANAK P.An empirical estimation of default risk of the UK real estate companies[J].Journal of Real Estate Finance and Economics,2006,32(1):21-40.
[47] FRYE J.Collateral damage detected[R].Chicago:Federal Reserve Bank of Chicago,2000.
[48]袁敏.债务违约损失率研究综述[J].外国经济与管理,2005,27(8):55-64.
[49]武剑.内部评级法中的违约损失率(LGD)模型——新资本协议核心技术研究[J].国际金融研究,2005,(2):15-22.
[50]陈忠阳.违约损失率(LGD)研究[J].国际金融研究,2004,(5):53-54.
[51]吴晓桃.从法律角度分析当前房地产开发贷款的风险与防范[J].福建金融,2006,(5):25-27.