Following the principles and concepts of prudent macro management, a macro stress testing method for banking credit risk is put forward based on industry correlation. By considering industry correlation and the distribution characters of risk factor t, the multiple risk factor model is extended. The macro stress testing scenario with multiple risk factor model is considered, the values of industrial cycle index obtained under the stress scenario are transformed into conditional distributions of industrial risk factors under the corresponding scenario. Based on examination of macroeconomic cycle, the stress scenarios settings employ a variety of statistical methods to deal with historical macroeconomic data of the entire cycle in order to eliminate the procyclicality of credit risk measurement. The statistical methods include exponential smoothing, regression modeling approach and historical scenario analysis method. The process organically links the economic capital management with the prevention of systemic risk in banking industry. This macro stress testing method can reflect default correlations between credit assets in different industries, identify the negative impact on credit assets of other industries caused by some certain industry downturn, which reflects the source and mechanism of systemic risk.
PENG Jian-gang, YI Hao, PAN Ling-yao
. Research of Macro Stress Testing for Banking Credit Risk Based on the Industry Correlation[J]. Chinese Journal of Management Science, 2015
, 23(4)
: 11
-19
.
DOI: 10.16381/j.cnki.issn1003-207x.2015.04.002
[1] 周小川. 金融政策对金融危机的响应-宏观审慎政策框架的形成背景、内在逻辑和主要内容[J]. 金融研究, 2011, (1):1-14.
[2] 张健华, 贾彦东.宏观审慎政策的理论与实践进展[J]. 金融研究, 2012, (1):20-35.
[3] 彭建刚, 易昊, 童磊.基于系统性风险防范的金融压力测试新理念[J]. 湖南大学学报(社会科学版), 2012, 26(5):37-43.
[4] DrehMann M. Stress tests:Objectives, challenges and modelling choices[J]. Riksbank Economic Review, 2008, 94(2), 60-92.
[5] Virolainen K. Macro stress-testing with a macroeconomic credit risk model for finland[R]. Discussion Papers, Bank of Finland, 2004.
[6] Van D E, Hoeberichts M, Tabbae M. Modelling scenario analysis, macro stress-testing[R]. Working Paper, De Nederlandsche Bank, 2006.
[7] Wong J, Choi K F, Fong P W.A framework for stress testing banks' credit risk[J]. The Journal of Risk Model Validation, 2008, 2(1):3-23.
[8] Havrlchyk O. A macroeconomic credit risk model for stress testing the South African banking Sector[R]. Working Paper, University Library of Munich, 2010.
[9] Ali A, Daly K. Macroeconomic determinants of credit risk:Recent evidence from a cross country study[J]. International Review of Financial Analysis, 2010, 19(2):165-171.
[10] Jimenez G, Mencia J. Modelling the distribution of credit losses with observable and latent factors[J]. Journal of Empirical Finance, 2009, 16(2):235-253.
[11] Filosa R. Stress testing of the stability of the Italian banking system:A VAR approach[J]. Heterogeneity and Monetary Policy, 2007, 703(1):1-46.
[12] Marcucci J, Quagliariello M. Is bank portfolio riskiness procyclical? Evidence from Italy using a Vector Autoregression[J]. Journal of International inancial Markets, Institutions and Money, 2008, 18 (1):46-63.
[13] 谭晓红, 樊纲治.我国商业银行宏观压力测试研究[J]. 投资研究, 2011, (12):3-16.
[14] Castren O, Fitzpatrick T, Sydow M. Assessing portfolio credit risk changes in a sample of EU large and complex banking groups in reaction to macroeconomic shocks[R]. working paper, ECB, 2009.
[15] Klaus D, Martin E. Crash testing German banks[J].International Journal of Central Banking, 2009, 5(3):139-175.
[16] 彭建刚, 吕志华. 基于行业特性的多元系统风险因子CreditRisk+模型[J].中国管理科学, 2009, 17(3):56-64.
[17] Huang Xin, Zhou Hao, Zhu Haibin. A framework for assessing the systemic risk of major financial institutions[J]. Journal of Banking and Finance, 2009, 33(11):2036-2049.
[18] Segoviano M A, Goodhart C. Banking stability measures[R]. Working Papers, IMF, 2009.
[19] Grass G. Using structural models for default prediction[R].Working Paper, University of Fennsylvania, 2009.
[20] Alessandri P, Gai P, Kapadia S, et al. Towards a framework for quantifying systemic stability[J]. International Journal of Central Banking, 2009, 5(3):47-81.
[21] Finger C. Conditional approaches for credit metrics portfolio distributions[J]. Credit Metrics Monitor, 1997, 7(1):14-33.
[22] 林宇, 卫贵武, 魏宇, 等.基于Skew-t-FIAPARCH的金融市场动态风险VaR测度研究[J].中国管理科学, 2009, 17(6):17-24.
[23] Buncic D, Melecky M. Macroprudential stress testing of credit risk-A practical approach for policy makers[J]. Journal of financial stability, 2013, 9(3):347-370.
[24] 徐国祥, 檀向球.我国A股市场系统性风险的实证研究[J].统计研究, 2002, (5):37-40.
[25] 董进.宏观经济波动周期的测度[J]. 经济研究, 2006, (7):41-48.
[26] 杜红军, 王宗军. 基于Copula-AL法的VaR和CVaR的度量与分配[J].中国管理科学, 2012, 20(3):1-9.
