商业银行信用风险压力测试的应用研究
摘要
近些年来,信用风险是银行经营过程中面临的最主要风险。20世纪90年代以后,金融工具的不断创新、信贷规模的持续扩张等因素,使信用风险与银行所面临的其他风险之间的关联性越来越强,由信用风险所造成的银行损失越来越大。尤其是2008年美国爆发的次贷危机,其根本原因是由于美国部分银行信用风险的管理不善造成的,此次危机愈演愈烈,最终演变成了一场席卷全球的金融海啸。传统的信用风险管理方法和度量模型已不能满足当前商业银行的发展需求,如何全面、有效地对银行信用风险进行管理,是所有国家银行机构最值得研究的重要课题之一。
本文以现代商业银行信用风险管理理论为基础,全面、系统地对信用风险的发展过程和主要特征进行了分析,总结了目前具有代表性的4类信用风险度量模型,确定CPV模型为适合我国银行机构信用风险度量的主要适用模型。在此基础上,采用压力测试技术分别从银行监管机构和银行分支机构这2个层面对我国商业银行信用风险进行了实证分析。首先,建立了关于国民生产总值增长率、居民消费价格指数、流通中现金同比增长率和美国国债三年期利率这4个因素的宏观信贷模型,进行了压力测试；其次,在对银行分支机构进行压力测试时,从企业的角度出发,以KMV模型为基础,把违约距离作为企业信用风险的度量指标,得出实证结论：国民生产总值增长率、净资产收益率、行业产出量对企业的违约距离具有较明显的影响；最后,在实证分析基础上,提出了加强压力测试技术在金融领域推广工作的政策性建议,加强金融风险量化的管理工作,提高我国银行机构的整体风险管理水平。
In recent years, credit risk has been the primary risk in the process of bank operation. After 19 century, on the account of continuous innovation、continued expansion of credit risk on financial tools, the relevance between credit risk and other bank risks become stronger and more hazardous. Especially the American subprime crisis which outbroke in 2008, the basic reason was that the credit risk on mismanagement of some banks in the U.S which finally evolved into a global financial tsunami. The traditional credit risk management methods and measure models could not meet the current commercial bank's end development needs. How to comprehensively and effectively manage the bank credit risk are the primary work and the most valuable research on the whole countries banks.
The paper is based on modern commercial bank credit risk management theory comprehensively and systematically analyzes the development of credit risk process and main characteristics, summing up 4 representative models and determining the CPV model are the most suitable one to measure the credit risk on Chinese bank institutions. On that basis, the paper uses the stress testing techniques to make an empirical analysis on the credit risk of commercial banks which is based on two aspects "bank regulators" and "bank branches" First of all, the paper establishes 4 macro-credit model which are GDP growth, consumer price index, cash in circulation growth and the three-year U.S. treasury rate, then carried out stress tests analysis; secondly, for bank branches in stress test, the paper from the angel of business and based on the KMV model, set the default distance as a credit risk metric, then by doing experimental simulation it can be concluded that the GDP growth, the ROE and the output of enterprises have obvious influences on the default distance; finally, on the basis of empirical analysis, the paper proposes the suggestions on strengthening the stress testing technology which is applied in the financial sector promotion in order to strengthen the management on financial risk quantification and improve the whole Chinese banking institutions on risk management.
本文以现代商业银行信用风险管理理论为基础,全面、系统地对信用风险的发展过程和主要特征进行了分析,总结了目前具有代表性的4类信用风险度量模型,确定CPV模型为适合我国银行机构信用风险度量的主要适用模型。在此基础上,采用压力测试技术分别从银行监管机构和银行分支机构这2个层面对我国商业银行信用风险进行了实证分析。首先,建立了关于国民生产总值增长率、居民消费价格指数、流通中现金同比增长率和美国国债三年期利率这4个因素的宏观信贷模型,进行了压力测试；其次,在对银行分支机构进行压力测试时,从企业的角度出发,以KMV模型为基础,把违约距离作为企业信用风险的度量指标,得出实证结论：国民生产总值增长率、净资产收益率、行业产出量对企业的违约距离具有较明显的影响；最后,在实证分析基础上,提出了加强压力测试技术在金融领域推广工作的政策性建议,加强金融风险量化的管理工作,提高我国银行机构的整体风险管理水平。
In recent years, credit risk has been the primary risk in the process of bank operation. After 19 century, on the account of continuous innovation、continued expansion of credit risk on financial tools, the relevance between credit risk and other bank risks become stronger and more hazardous. Especially the American subprime crisis which outbroke in 2008, the basic reason was that the credit risk on mismanagement of some banks in the U.S which finally evolved into a global financial tsunami. The traditional credit risk management methods and measure models could not meet the current commercial bank's end development needs. How to comprehensively and effectively manage the bank credit risk are the primary work and the most valuable research on the whole countries banks.
The paper is based on modern commercial bank credit risk management theory comprehensively and systematically analyzes the development of credit risk process and main characteristics, summing up 4 representative models and determining the CPV model are the most suitable one to measure the credit risk on Chinese bank institutions. On that basis, the paper uses the stress testing techniques to make an empirical analysis on the credit risk of commercial banks which is based on two aspects "bank regulators" and "bank branches" First of all, the paper establishes 4 macro-credit model which are GDP growth, consumer price index, cash in circulation growth and the three-year U.S. treasury rate, then carried out stress tests analysis; secondly, for bank branches in stress test, the paper from the angel of business and based on the KMV model, set the default distance as a credit risk metric, then by doing experimental simulation it can be concluded that the GDP growth, the ROE and the output of enterprises have obvious influences on the default distance; finally, on the basis of empirical analysis, the paper proposes the suggestions on strengthening the stress testing technology which is applied in the financial sector promotion in order to strengthen the management on financial risk quantification and improve the whole Chinese banking institutions on risk management.
引文
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[2]Wilson TC.Portfolio credit risk I [J].Risk,1997,9 (10):111-170.
[3]W ilson T C. Portfolio credit risk II[J]. Risk,1997,10 (10):56-61.
[4]Vlieghe G. Indicators of fragility in the UK corporate sector [J]. Bank of England Working Paper:on.146,2001.
[5]Bunn P, Cunningham A, Drehmann M. Stress testing as a tool for assessing systemic risk[J]. Financial Stability Review:Bank of England,2005 (6):116-260.
[6]Hoggarth G, Whitley J. Assessing the strength of UK banks through macroeconomic Stress Tests[J]. Financial Stability Review:Bank of England,2003 (7):91-103.
[7]Drehmann M, Hoggarth G, Logan A, et a.l Macrostress testing UK banks[R]. Paper presented at the Workshop on Financial Stability in Frankfurt:Bank of England,2004 (6):16-17.
[8]Bernhardsen T. The relationship between interest rate differentials and macroeconomic variables:a panel data study for European countries [J]. Journal of International Money and Finance,2000,18 (2):289-30.
[9]Bernhardsen T. Real-time data for Norway:Challenges for monetary policy [J]. The North American Journal of Economics and Finance,2005,19 (3):333-349.
[10]Froyland E, Larsen K. How vulnerable are financial in stitutions to macroeconomic changes an analysis basedon StressTesting [J]. Economic Bulletin,2002,3 (11) 127-169.
[11]Erlenmaier U. Correlations models in Credit Risk Management [D]. Norway:University of Heidelberg,2004.
[12]Erlenmaier U, Gersbach H. Default probabilities and default correlations [D]. Norway: University of Heidelberg,2005.
[13]Sorge M, Virolainen K. A comparative analysis of macro stress-testing methodologies with application to Finland [J]. Journal of Financial Stability,2006,17 (2):113-122
[14]Wong J, Choi K F, Fong T. A frame work for macrostress-testing the credit risk of banks in Hong Kong[J]. Hong Kong Monetary Authority Quarterly Bulletin,2006 (10) 1-38.
[15]Kalirai H, Scheicher M. Macroeconomic stress testing:preliminary evidence for Austria [R]. Financial Stability Report:Austrian National Bank,2002 (3):77-98.
[16]Hanschel E, Monnin P. Measuring and forecasting stress in the banking sector:evidence from Switzerland [J]. Workpaper of Swiss National Bank:on.118,2003.
[17]Jones M T, Hilbers P, Slack G. Stress testing financial systems:what to do when the governor calls[J]. IMF Working paper:no.46,2004:191-211.
[18]Jones M T, Saunders A. Have US financial institutionsreal estate investments exhibited trend chasing behaviour [J]. The Review of Economics and Statistics,2004(11):248-258.
[19]Worrell D. Quantitative assessment of the financial sector:an integrate dapproach[J]. IMF Working paper:on.144,2004:1-57.
[20]Worrell D, Cherebin D, Polius-Mounsey T. Financial system soundness in the Caribbean:an initial assessment[J]. IMF Working Paper:on.123,2001 (9):1-57.
[21]Gray D, Merton R C, Bodie Z. A new frame work foran-alyzing and managing macro financial risks[C]. Conference on Finance and the Macro economy:NYU,2004.
[22]Derviz A, Kladlcakova N. Business cycle, credit risk and economic capital determination by commercial banks [J]. Czech National Bank,2003 (11):57-62
[23]汪寿阳,张静,王振全.日元贬值对中国出口的影响：压力测试分析[J].科技导报,2002(7)
[24]杨鹏.压力测试及其在金融监管中的应用[J].上海金融,2005(1)
[25]孙连友.金融体系压力测试-概念与方法[J].济南金融,2006(2)：43-50.
[26]高同裕,陈元富.宏观压力测试及其在我国应用面临的问题[J].南方金融,2006.
[27]熊波.中国银行体系稳定性评估-指标分析与压力测试[D].西南财经大学硕士论文,2006.
[28]徐明东,刘晓星.金融系统稳定性评估：基于宏观压力测试方法的国际比较[J].国际金融研究,2008(2)
[29]陶峥.金融危机对银行业流动性管理的影响[J].金融经济,2009(4)：14-15.
[30]安国俊,安国勇,王峰娟.金融危机对银行业流动性风险管理的影响[N].证券市场导报,2008(12)：38-43.
[31]廖岷.从全球金融危机看商业银行流动性风险管理的重要性[J].西部金融,2009(1)：33-35.
[32]殷俊,刘爽.银行宏观审慎监管框架下的压力测试应用研究[J].财经理论与实践,2011(1)：13-18.
[33]方舟.房价下跌对我国商业银行个人住房信贷带来的冲击[J].金融理论与时间,2011(1)：86-89.
[34]香港金融监管当局.监管政策手册《IC-5压力测试》,2003.
[35]霍兵,李颖.四大国有商业银行信用风险的数据分析[J].财经论丛,2005(5)：75-76.
[36]班赛尔(V,K).用VaR度量市场风险[M].北京：机械工业出版社,2001：45-66.
[37]张玲,张佳林.信用风险评估方法发展趋势[J].预测,2000(7)：72-73.
[38]潘爱香.财务报表分析[M].北京：经济科学出版社,1999.
[39]刘芸芸,李丹.浅析现代信用风险度量模型[J].中国西部科技,2006(6)：90-91.
[40]银监会.《商业银行压力测试指引》,2007.
[41]鲜玫,黄秋丽.金融稳定护卫：宏观压力测试[J].商场现代化,2006(11)：367-368.
[42]李娜,吴全平.论新巴塞尔协议和在其框架下的压力测试[J].思想战线,2010(10)：63-65.
[43]徐航航,何昌海,陆华兵.基于货币市场基金的压力测试研究[J].财政金融,2009(12) ： 17-19.
[44]12家上市银行去年涉房贷款超5万亿,2010年4月22日http://finance.ifeng.com/stock/hybg/20100422/2086595.shtml
[45]刘晓星.压力测试与金融系统稳定性评估报告[J].财经问题研究,2009(9)：57-65.
[46]李亚范,王磐.关于我国商业银行的压力测试工作的探讨[J].消费导刊,2009(11)：44-46.
[47]和讯网,中国银行年报,2008-2009.
[48]贾海涛,邱长溶.宏观因素对贷款企业违约率影响的实证分析[J].现代管理科学,2009(2)：67-70.
[49]施华强.国有商业银行账面不良贷款、调整因素和严重程度：1994-2004[J].金融研究,2005(12)：28-30
[50]罗俊.基于KMV模型的中国上市公司信用风险实证研究[D].西南财经大学硕士论文,2008.
[2]Wilson TC.Portfolio credit risk I [J].Risk,1997,9 (10):111-170.
[3]W ilson T C. Portfolio credit risk II[J]. Risk,1997,10 (10):56-61.
[4]Vlieghe G. Indicators of fragility in the UK corporate sector [J]. Bank of England Working Paper:on.146,2001.
[5]Bunn P, Cunningham A, Drehmann M. Stress testing as a tool for assessing systemic risk[J]. Financial Stability Review:Bank of England,2005 (6):116-260.
[6]Hoggarth G, Whitley J. Assessing the strength of UK banks through macroeconomic Stress Tests[J]. Financial Stability Review:Bank of England,2003 (7):91-103.
[7]Drehmann M, Hoggarth G, Logan A, et a.l Macrostress testing UK banks[R]. Paper presented at the Workshop on Financial Stability in Frankfurt:Bank of England,2004 (6):16-17.
[8]Bernhardsen T. The relationship between interest rate differentials and macroeconomic variables:a panel data study for European countries [J]. Journal of International Money and Finance,2000,18 (2):289-30.
[9]Bernhardsen T. Real-time data for Norway:Challenges for monetary policy [J]. The North American Journal of Economics and Finance,2005,19 (3):333-349.
[10]Froyland E, Larsen K. How vulnerable are financial in stitutions to macroeconomic changes an analysis basedon StressTesting [J]. Economic Bulletin,2002,3 (11) 127-169.
[11]Erlenmaier U. Correlations models in Credit Risk Management [D]. Norway:University of Heidelberg,2004.
[12]Erlenmaier U, Gersbach H. Default probabilities and default correlations [D]. Norway: University of Heidelberg,2005.
[13]Sorge M, Virolainen K. A comparative analysis of macro stress-testing methodologies with application to Finland [J]. Journal of Financial Stability,2006,17 (2):113-122
[14]Wong J, Choi K F, Fong T. A frame work for macrostress-testing the credit risk of banks in Hong Kong[J]. Hong Kong Monetary Authority Quarterly Bulletin,2006 (10) 1-38.
[15]Kalirai H, Scheicher M. Macroeconomic stress testing:preliminary evidence for Austria [R]. Financial Stability Report:Austrian National Bank,2002 (3):77-98.
[16]Hanschel E, Monnin P. Measuring and forecasting stress in the banking sector:evidence from Switzerland [J]. Workpaper of Swiss National Bank:on.118,2003.
[17]Jones M T, Hilbers P, Slack G. Stress testing financial systems:what to do when the governor calls[J]. IMF Working paper:no.46,2004:191-211.
[18]Jones M T, Saunders A. Have US financial institutionsreal estate investments exhibited trend chasing behaviour [J]. The Review of Economics and Statistics,2004(11):248-258.
[19]Worrell D. Quantitative assessment of the financial sector:an integrate dapproach[J]. IMF Working paper:on.144,2004:1-57.
[20]Worrell D, Cherebin D, Polius-Mounsey T. Financial system soundness in the Caribbean:an initial assessment[J]. IMF Working Paper:on.123,2001 (9):1-57.
[21]Gray D, Merton R C, Bodie Z. A new frame work foran-alyzing and managing macro financial risks[C]. Conference on Finance and the Macro economy:NYU,2004.
[22]Derviz A, Kladlcakova N. Business cycle, credit risk and economic capital determination by commercial banks [J]. Czech National Bank,2003 (11):57-62
[23]汪寿阳,张静,王振全.日元贬值对中国出口的影响：压力测试分析[J].科技导报,2002(7)
[24]杨鹏.压力测试及其在金融监管中的应用[J].上海金融,2005(1)
[25]孙连友.金融体系压力测试-概念与方法[J].济南金融,2006(2)：43-50.
[26]高同裕,陈元富.宏观压力测试及其在我国应用面临的问题[J].南方金融,2006.
[27]熊波.中国银行体系稳定性评估-指标分析与压力测试[D].西南财经大学硕士论文,2006.
[28]徐明东,刘晓星.金融系统稳定性评估：基于宏观压力测试方法的国际比较[J].国际金融研究,2008(2)
[29]陶峥.金融危机对银行业流动性管理的影响[J].金融经济,2009(4)：14-15.
[30]安国俊,安国勇,王峰娟.金融危机对银行业流动性风险管理的影响[N].证券市场导报,2008(12)：38-43.
[31]廖岷.从全球金融危机看商业银行流动性风险管理的重要性[J].西部金融,2009(1)：33-35.
[32]殷俊,刘爽.银行宏观审慎监管框架下的压力测试应用研究[J].财经理论与实践,2011(1)：13-18.
[33]方舟.房价下跌对我国商业银行个人住房信贷带来的冲击[J].金融理论与时间,2011(1)：86-89.
[34]香港金融监管当局.监管政策手册《IC-5压力测试》,2003.
[35]霍兵,李颖.四大国有商业银行信用风险的数据分析[J].财经论丛,2005(5)：75-76.
[36]班赛尔(V,K).用VaR度量市场风险[M].北京：机械工业出版社,2001：45-66.
[37]张玲,张佳林.信用风险评估方法发展趋势[J].预测,2000(7)：72-73.
[38]潘爱香.财务报表分析[M].北京：经济科学出版社,1999.
[39]刘芸芸,李丹.浅析现代信用风险度量模型[J].中国西部科技,2006(6)：90-91.
[40]银监会.《商业银行压力测试指引》,2007.
[41]鲜玫,黄秋丽.金融稳定护卫：宏观压力测试[J].商场现代化,2006(11)：367-368.
[42]李娜,吴全平.论新巴塞尔协议和在其框架下的压力测试[J].思想战线,2010(10)：63-65.
[43]徐航航,何昌海,陆华兵.基于货币市场基金的压力测试研究[J].财政金融,2009(12) ： 17-19.
[44]12家上市银行去年涉房贷款超5万亿,2010年4月22日http://finance.ifeng.com/stock/hybg/20100422/2086595.shtml
[45]刘晓星.压力测试与金融系统稳定性评估报告[J].财经问题研究,2009(9)：57-65.
[46]李亚范,王磐.关于我国商业银行的压力测试工作的探讨[J].消费导刊,2009(11)：44-46.
[47]和讯网,中国银行年报,2008-2009.
[48]贾海涛,邱长溶.宏观因素对贷款企业违约率影响的实证分析[J].现代管理科学,2009(2)：67-70.
[49]施华强.国有商业银行账面不良贷款、调整因素和严重程度：1994-2004[J].金融研究,2005(12)：28-30
[50]罗俊.基于KMV模型的中国上市公司信用风险实证研究[D].西南财经大学硕士论文,2008.