Incorporating behavioural and macroeconomic correlations for the prediction of bank capital for credit risk

被引:0
作者
Djeundje, Viani Biatat [1 ]
Crook, Jonathan [1 ]
机构
[1] Univ Edinburgh, Edinburgh, Scotland
关键词
OR in banking; credit risk analysis; value at risk; MODELS; COINTEGRATION; FRAILTY;
D O I
10.1080/01605682.2025.2467774
中图分类号
C93 [管理学];
学科分类号
12 ; 1201 ; 1202 ; 120202 ;
摘要
Large banks are required to stress test their credit portfolios annually under Basel III. Stress testing credit portfolios to macroeconomic shocks at account level involves parameterising a model predicting probability of default followed by hypothesising specific shocks or by simulation to derive a value at risk (VaR) or expected shortfall (ES), 12 months into the future. Plausible simulation requires that the simulated values of the macroeconomic variables retain their correlated relationships. But the probability of default is also correlated with time-varying behavioural variables, which in turn are correlated with the macroeconomy. Simulation studies have estimated the VaR when mutually consistent macroeconomic values have been simulated or when behavioural variables have been simulated but not when both are simulated. In this article, we present a method to simulate both behavioural and macroeconomic variables into the future whilst maintaining the correlation structure between them to derive a more comprehensive simulation methodology to stress test a credit portfolio.
引用
收藏
页数:15
相关论文
共 47 条
[1]   Exploring the sources of default clustering [J].
Azizpour, S. ;
Giesecke, K. ;
Schwenkler, G. .
JOURNAL OF FINANCIAL ECONOMICS, 2018, 129 (01) :154-183
[2]  
Bates D., 2011, COMPUTATIONAL METHOD
[3]   Retail credit stress testing using a discrete hazard model with macroeconomic factors [J].
Bellotti, Tony ;
Crook, Jonathan .
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY, 2014, 65 (03) :340-350
[4]   Forecasting and stress testing credit card default using dynamic models [J].
Bellotti, Tony ;
Crook, Jonathan .
INTERNATIONAL JOURNAL OF FORECASTING, 2013, 29 (04) :563-574
[5]   Support vector machines for credit scoring and discovery of significant features [J].
Bellotti, Tony ;
Crook, Jonathan .
EXPERT SYSTEMS WITH APPLICATIONS, 2009, 36 (02) :3302-3308
[6]  
Berkowitz J., 1999, A coherent framework for stress testing, DOI [10.2139/ssrn.181931, DOI 10.2139/SSRN.181931]
[7]   Demographic Variables and Credit Scores: An empirical study of a controversial selection tool [J].
Bernerth, Jeremy B. .
INTERNATIONAL JOURNAL OF SELECTION AND ASSESSMENT, 2012, 20 (02) :242-246
[8]   BAYESIAN VARS: SPECIFICATION CHOICES AND FORECAST ACCURACY [J].
Carriero, Andrea ;
Clark, Todd E. ;
Marcellino, Massimiliano .
JOURNAL OF APPLIED ECONOMETRICS, 2015, 30 (01) :46-73
[9]   Macro-Economic Factors in Credit Risk Calculations: Including Time-Varying Covariates in Mixture Cure Models [J].
Dirick, Lore ;
Bellotti, Tony ;
Claeskens, Gerda ;
Baesens, Bart .
JOURNAL OF BUSINESS & ECONOMIC STATISTICS, 2019, 37 (01) :40-53
[10]  
Djeundje V. A. B., 2025, A New Method to Predict Economic Capital for a Lending Portfolio, DOI [https://dx.doi.org/10.1080/01605682.2024.2437568, DOI 10.1080/01605682.2024.2437568]