KMV模型的信用风险度量研究
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摘要
自从信用风险出现以来,它就始终影响和困扰着经济活动中的各个主体。随着20世纪70年代开始的金融自由化和经济全球化,金融市场中的各类风险开始急剧凸显。进入20世纪90年代后,金融危机频繁爆发,危机给各国带来了巨大的影响和损失,信用风险管理受到了社会各界的高度关注。我国社会信用体系尚未完全建立,股票市场的投机行为和上市公司经营运作违规现象时有发生,我国上市公司的信用问题已成为我国资本市场进一步发展的严重阻碍,如何采取更有效的措施控制信用风险,是金融市场和金融机构持续面临的重大挑战。
本文结合理论介绍和实证研究,在KMV模型的基础上度量了甘肃省上市公司的信用风险水平。首先,本文的理论部分对信用风险的概念,信用风险度量的理论基石,上市公司信用风险度量方法的演变,以及信用风险度量方法的比较等进行了介绍。然后,对本文运用的KMV信用风险度量模型进行了构建及修正。最后,选文甘肃省23家上市公司,采用样本公司2012年12月股票市场和2012年第三季度季报中的数据,运用KMV模型计算出每一家上市公司的违约距离和预期违约概率,将它们与信用评级、资产负债比率进行比较,分析样本公司的信用风险状况。结果表明：甘肃省上市公司的违约风险较低,信用状况良好。以证券市场信息为基础的KMV模型,基本可以准确测度和反映样本上市公司信用风险质量的变化,在我国有较好的适用性。
Since its appearance, credit risk has always bothered the principle parties of economic activities. With the development of financial liberalization and economic globalization since1970s, various types of risks in the financial markets began to appear dramatically. Financial crisis outbreaks frequently since1990s, as a result, many countries suffer huge losses and much attention has been paid to the credit risk management. China is in a period of economic restructuring, social credit system is not yet fully established, speculative behaviors in the stock market and irregular operations of listed companies have occurred occasionally. The credit issue of China's listed companies has become a serious impediment to the further development of China's capital market. Therefore, how to take more effective measures to control credit risk is a significant challenge which financial markets and financial institutions have to continuously meet.
In this thesis, we measure the credit risk of listed companies in Gansu Province on the basis of the KMV model through theoretical introduction and empirical research. First, the concepts of credit risk, theoretical foundations of credit risk measurement, the evolution of credit risk measurement methods of listed companies, and the comparison and selection of credit risk measurement methods are introduced in the theoretical part. Then we construct and modify the KMV credit risk measurement model used in this paper. Finally, we selected23listed companies in Gansu Province and measured their credit risk by adopting the data from stock market in December,2012and the data from the third-quarter reports. We adopt the KMV model to calculate the distance to default and the expected probability of default of these listed companies, and compare them with credit rating and asset-liability ratio. The results show that the credit risk of listed companies in Gansu Province is low and these companies are in good credit standing. The KMV model based on the information from stock market can accurately measure and reflect the credit risk changes of listed companies; hence, it owns strong applicability in China.
本文结合理论介绍和实证研究,在KMV模型的基础上度量了甘肃省上市公司的信用风险水平。首先,本文的理论部分对信用风险的概念,信用风险度量的理论基石,上市公司信用风险度量方法的演变,以及信用风险度量方法的比较等进行了介绍。然后,对本文运用的KMV信用风险度量模型进行了构建及修正。最后,选文甘肃省23家上市公司,采用样本公司2012年12月股票市场和2012年第三季度季报中的数据,运用KMV模型计算出每一家上市公司的违约距离和预期违约概率,将它们与信用评级、资产负债比率进行比较,分析样本公司的信用风险状况。结果表明：甘肃省上市公司的违约风险较低,信用状况良好。以证券市场信息为基础的KMV模型,基本可以准确测度和反映样本上市公司信用风险质量的变化,在我国有较好的适用性。
Since its appearance, credit risk has always bothered the principle parties of economic activities. With the development of financial liberalization and economic globalization since1970s, various types of risks in the financial markets began to appear dramatically. Financial crisis outbreaks frequently since1990s, as a result, many countries suffer huge losses and much attention has been paid to the credit risk management. China is in a period of economic restructuring, social credit system is not yet fully established, speculative behaviors in the stock market and irregular operations of listed companies have occurred occasionally. The credit issue of China's listed companies has become a serious impediment to the further development of China's capital market. Therefore, how to take more effective measures to control credit risk is a significant challenge which financial markets and financial institutions have to continuously meet.
In this thesis, we measure the credit risk of listed companies in Gansu Province on the basis of the KMV model through theoretical introduction and empirical research. First, the concepts of credit risk, theoretical foundations of credit risk measurement, the evolution of credit risk measurement methods of listed companies, and the comparison and selection of credit risk measurement methods are introduced in the theoretical part. Then we construct and modify the KMV credit risk measurement model used in this paper. Finally, we selected23listed companies in Gansu Province and measured their credit risk by adopting the data from stock market in December,2012and the data from the third-quarter reports. We adopt the KMV model to calculate the distance to default and the expected probability of default of these listed companies, and compare them with credit rating and asset-liability ratio. The results show that the credit risk of listed companies in Gansu Province is low and these companies are in good credit standing. The KMV model based on the information from stock market can accurately measure and reflect the credit risk changes of listed companies; hence, it owns strong applicability in China.
引文
[1]Stephen Kealhofer. Portfolio Management of Default Risk[R]. KMV LLC.1997.
[2]Stefan Blochwitz,Thilo Liebig.Mikael Nyberg. Benchmarking Deutsche Bundesbank's Default Risk Model, the KMV(Private Firm Model)and Common Financial Ratios for German Corporations[R]. KMV LLC.2000.
[3]Mark Carey, Mark Hrycay. Parameterizing credit risk models with rating data[J]. Elsevier Science B.V.2001.
[4]Oldrich Alfons Vasicek. EDFTM Credit Measure and Corporate Bond Pricing[R]. KMV LLC. 2001.
[5]Peter Crosbie, Jeff Bohn. Modeling Default Risk [R]. PMoody's KMV Company.2003.
[6]Jin-Chuan Duan, Genevieve Gauthier,Jean-Guy Simonato. On the Equivalence of the KMV and Maximum Likelihood Methods for Structural Credit Risk Models[R]. the 15th Derivative Securities and Risk Management Conference.2005.
[7]Irina Korablev, Douglas Dwyer. Power and Level Validation of Moody's KMV EDFTM Credit Measures in North America, Europe, and Asia[R]. Moody's KMV Company.2007.
[8]Irina Korablev, Shisheng Qu. Validating the Public EDF Model Performance During the Credit Crisis[R]. Moody's KMV Company.2009.
[9]Ozge Gokbayrak,Lee Chua. Validating the Public EDFTM Model During the Credit Crisis in Asia and Europe[R]. Moody's KMV.2009.
[10]Christopher Crossen,Xu Zhang. Validating the Public EDF Model for Global Financial Firms[R]. Moody's Analytics Quantitative Research.2011.
[11]Zhao Sun, David Munves, David T. Hamilton. Public Firm Expected Default Frequency (EDFTM) Credit Measures:Methodology, Performance, and Model Extensions[R]. Moody's Analytics.2012.
[12]布莱甘吉林,约翰比拉尔代洛.公司信用分析基础[M].上海：上海财经大学出版社.2007.
[13]叶蜀君等.信用风险度量与管理[M].北京：首都经贸大学出版社.2008.
[14]干琼,陈金贤.信用风险定价方法与模型研究[J].现代财经.2002,22(4)：14-16.
[15]梁世栋,郭仌,李勇,方兆本.信用风险模型比较分析[J].中国管理科学.2002,10(1)：17-22.
[16]薛锋,关伟,乔卓.上市公司信用风险度量的一种新方法—KMV[J].西北工业大学学报(社会科学版).2003,23(3)：38-44.
[17]杨星,张义强.中国上市公司信用风险管理实证研究—EDF模型在信用评估中的应用[J].中国软科学.2004,(1)：43-47.
[18]陈东海,谢赤.信用风险管理中违约概率的估算方法[J].统计与决策.2005,(7)：18-19.
[19]曹道胜,何明升.商业银行信用风险模型的比较及其借鉴[J].金融研究.2006,(10)：90-97.
[20]周昭雄.基于我国上市公司的KMV模巧型研究[J].工业技术经济.2006,25(7)：142-148.
[21]夏红芳,马俊海.基于KMV模型的农业上市公司信用风险实证分析[J].农业经济问 题.2007,(10)：88-92.
[22]谢邦吕,朱世武,李璇,董春.我国上市公司信用风险度量模型的选择[J].经济学动态.2008,(5)：55-58.
[23]高扬敏,陈红伟,陈刚.上市公司信用风险的KMV模型分析[J].辽宁工程技术大学学报(社会科学版).2009,11(1)：20-22.
[24]周沅帆.基于KMV模型对我国上市保险公司的信用风险度量[J].保险研究.2009,(3)：77-81.
[25]陆珩琪,张佳慧.基于KMV模型的纺织业上市公司信用风险研究[J].价值工程.2010,(17)：24-26.
[26]万晏伶,杨俊.我国制造业上市公司信用风险研究—基于KMV模型[J].技术经济.2011,30(5)：119-123.
[27]彭伟.基于KMV模型的上市中小企业信贷风险研究[J].南方金融.2012,(3)：23-30.
[28]韩立岩,郑承利.基于模糊随机方法的公司违约风险预测研究[J].金融研究.2002,(8)：48-53.
[29]鲁炜,赵恒珩,方兆本,刘冀云.KMV模型在公司价值评估中的应用[J].管理科学.2003,16(3)：30-33.
[30]董颖颖,薛锋,关伟.KMV模型在我国证券市场的适用性分析及其改进[J].生产力研究.2004,(8)：116-117.
[31]张智梅,章仁俊.KMV模型的改进及对上市公司信用风险的度量[J].统计与决策.2006,(9)：157-160.
[32]陈东平,孙明.KMV模型的修正及应研究[J].经济研究导刊.2007,(1)：45-48.
[33]孙小琰,沈悦,罗璐琦.基于KMV模型的我国上市公司价值评估实证研究[J].管理工程学报.2008,(1)：102-108.
[34]闫海峰,华雯君.基于KMV模型的中国上市公司信用风险研究[J].产业经济研究.2009,(3)：14-22.
[35]张能福,张佳.改进的KMV模型在我国上市公司信用风险度量中的应用[J].预测.2010,(5)：48-52.
[36]彭大衡,张聪宁.市场分割条件下KMV模型的改进与实证分析[J].统计与信息论坛.2011,26(5)：33-38.
[37]李磊宁,杨凯.我国上市公司违约点选择问题研究—基于KMV模型[J].广西金融研究.2007,(10)：42-45.
[38]黄薇薇.KMV模型对中国上市公司信用风险评估的有效性验证[硕士论文].成都：西南财经大学.2012.
[39]樊晓.基于KMV模型的我国上市公司信用风险度量的动态化研究[硕士论文].青岛：中国海洋大学.2008.
[40]周程渝.基于KMV模型的中国上市公司信用风险度量研究[硕士论文].成都：西南财经大学.2009.
[41]赵吉红.基于KMV模型制造业上市公司信用风险研究[硕士论文].无锡：江南大学.2012.
[42]陈昕.商业银行信用风险管理的KMV模型及其修正[硕士论文].杭州：浙江工商大学.2010.
[43]何湘桂.KMV模型的实现与应用[硕士论文].武汉：华中科技大学.2008.
[2]Stefan Blochwitz,Thilo Liebig.Mikael Nyberg. Benchmarking Deutsche Bundesbank's Default Risk Model, the KMV(Private Firm Model)and Common Financial Ratios for German Corporations[R]. KMV LLC.2000.
[3]Mark Carey, Mark Hrycay. Parameterizing credit risk models with rating data[J]. Elsevier Science B.V.2001.
[4]Oldrich Alfons Vasicek. EDFTM Credit Measure and Corporate Bond Pricing[R]. KMV LLC. 2001.
[5]Peter Crosbie, Jeff Bohn. Modeling Default Risk [R]. PMoody's KMV Company.2003.
[6]Jin-Chuan Duan, Genevieve Gauthier,Jean-Guy Simonato. On the Equivalence of the KMV and Maximum Likelihood Methods for Structural Credit Risk Models[R]. the 15th Derivative Securities and Risk Management Conference.2005.
[7]Irina Korablev, Douglas Dwyer. Power and Level Validation of Moody's KMV EDFTM Credit Measures in North America, Europe, and Asia[R]. Moody's KMV Company.2007.
[8]Irina Korablev, Shisheng Qu. Validating the Public EDF Model Performance During the Credit Crisis[R]. Moody's KMV Company.2009.
[9]Ozge Gokbayrak,Lee Chua. Validating the Public EDFTM Model During the Credit Crisis in Asia and Europe[R]. Moody's KMV.2009.
[10]Christopher Crossen,Xu Zhang. Validating the Public EDF Model for Global Financial Firms[R]. Moody's Analytics Quantitative Research.2011.
[11]Zhao Sun, David Munves, David T. Hamilton. Public Firm Expected Default Frequency (EDFTM) Credit Measures:Methodology, Performance, and Model Extensions[R]. Moody's Analytics.2012.
[12]布莱甘吉林,约翰比拉尔代洛.公司信用分析基础[M].上海：上海财经大学出版社.2007.
[13]叶蜀君等.信用风险度量与管理[M].北京：首都经贸大学出版社.2008.
[14]干琼,陈金贤.信用风险定价方法与模型研究[J].现代财经.2002,22(4)：14-16.
[15]梁世栋,郭仌,李勇,方兆本.信用风险模型比较分析[J].中国管理科学.2002,10(1)：17-22.
[16]薛锋,关伟,乔卓.上市公司信用风险度量的一种新方法—KMV[J].西北工业大学学报(社会科学版).2003,23(3)：38-44.
[17]杨星,张义强.中国上市公司信用风险管理实证研究—EDF模型在信用评估中的应用[J].中国软科学.2004,(1)：43-47.
[18]陈东海,谢赤.信用风险管理中违约概率的估算方法[J].统计与决策.2005,(7)：18-19.
[19]曹道胜,何明升.商业银行信用风险模型的比较及其借鉴[J].金融研究.2006,(10)：90-97.
[20]周昭雄.基于我国上市公司的KMV模巧型研究[J].工业技术经济.2006,25(7)：142-148.
[21]夏红芳,马俊海.基于KMV模型的农业上市公司信用风险实证分析[J].农业经济问 题.2007,(10)：88-92.
[22]谢邦吕,朱世武,李璇,董春.我国上市公司信用风险度量模型的选择[J].经济学动态.2008,(5)：55-58.
[23]高扬敏,陈红伟,陈刚.上市公司信用风险的KMV模型分析[J].辽宁工程技术大学学报(社会科学版).2009,11(1)：20-22.
[24]周沅帆.基于KMV模型对我国上市保险公司的信用风险度量[J].保险研究.2009,(3)：77-81.
[25]陆珩琪,张佳慧.基于KMV模型的纺织业上市公司信用风险研究[J].价值工程.2010,(17)：24-26.
[26]万晏伶,杨俊.我国制造业上市公司信用风险研究—基于KMV模型[J].技术经济.2011,30(5)：119-123.
[27]彭伟.基于KMV模型的上市中小企业信贷风险研究[J].南方金融.2012,(3)：23-30.
[28]韩立岩,郑承利.基于模糊随机方法的公司违约风险预测研究[J].金融研究.2002,(8)：48-53.
[29]鲁炜,赵恒珩,方兆本,刘冀云.KMV模型在公司价值评估中的应用[J].管理科学.2003,16(3)：30-33.
[30]董颖颖,薛锋,关伟.KMV模型在我国证券市场的适用性分析及其改进[J].生产力研究.2004,(8)：116-117.
[31]张智梅,章仁俊.KMV模型的改进及对上市公司信用风险的度量[J].统计与决策.2006,(9)：157-160.
[32]陈东平,孙明.KMV模型的修正及应研究[J].经济研究导刊.2007,(1)：45-48.
[33]孙小琰,沈悦,罗璐琦.基于KMV模型的我国上市公司价值评估实证研究[J].管理工程学报.2008,(1)：102-108.
[34]闫海峰,华雯君.基于KMV模型的中国上市公司信用风险研究[J].产业经济研究.2009,(3)：14-22.
[35]张能福,张佳.改进的KMV模型在我国上市公司信用风险度量中的应用[J].预测.2010,(5)：48-52.
[36]彭大衡,张聪宁.市场分割条件下KMV模型的改进与实证分析[J].统计与信息论坛.2011,26(5)：33-38.
[37]李磊宁,杨凯.我国上市公司违约点选择问题研究—基于KMV模型[J].广西金融研究.2007,(10)：42-45.
[38]黄薇薇.KMV模型对中国上市公司信用风险评估的有效性验证[硕士论文].成都：西南财经大学.2012.
[39]樊晓.基于KMV模型的我国上市公司信用风险度量的动态化研究[硕士论文].青岛：中国海洋大学.2008.
[40]周程渝.基于KMV模型的中国上市公司信用风险度量研究[硕士论文].成都：西南财经大学.2009.
[41]赵吉红.基于KMV模型制造业上市公司信用风险研究[硕士论文].无锡：江南大学.2012.
[42]陈昕.商业银行信用风险管理的KMV模型及其修正[硕士论文].杭州：浙江工商大学.2010.
[43]何湘桂.KMV模型的实现与应用[硕士论文].武汉：华中科技大学.2008.