scholarly journals Credit risk stress testing in a cluster of Russian commercial banks

2019 ◽  
Vol 13 (3) ◽  
pp. 35-51
Author(s):  
Davit Bidzhoyan ◽  
Tatiana Bogdanova ◽  
Dmitry Neklyudov
2018 ◽  
Vol 14 (2) ◽  
pp. 1
Author(s):  
Oanh T. K. Vu ◽  
Yen H. Vu ◽  
Trang T. T. Nguyen ◽  
Trung H. Bui

In this paper, we assess the capacity of Vietnamese commercial banks to withstand the effects of an increase in credit risk as a result of macroeconomic shocks. Firstly, VAR model is used to estimate the relationship among macro variables (real GDP, real exchange rate, lending interest rate and inflation rate) and from that, macroeconomic scenarios are set up. Next, we employ a GMM model to estimate the relationship between the non-performing loan ratio (credit risk) and macro variables involved in first step. Finally, the new capital requirement ratio (CAR) is recalculated, which is based on the increase in loan provision followed by the rise in non-performing loan. The results show that credit risk which the commercial banks have to face is relatively limited when their risk weighted assets are unchanged. If these numbers, however, increase as banks broaden their lending, all banks’ CAR will reduce remarkably and four large banks will be lack of capital seriously and cannot meet the requirement of Central Bank.


2018 ◽  
Vol 1 (1) ◽  
pp. 10
Author(s):  
Amel Ben Youssef

<p><em>Stress tests of credit risk is greatly affected by data constraints in Tunisian banking system. Aiming to improve the assessment of credit risk in such conditions, we propose a model to conduct a macro stress test of credit risk for a sample of ten Tunisian commercial banks based on scenario analysis.</em></p><p><em>The approach consists first in explaining the credit risk for each bank in terms of macroeconomic and bank-specific variables through a static fixed effects model, second in a stress-testing exercise using the Monte Carlo Simulation for generating credit risk losses distributions in case of different scenarios and for determining unexpected losses for each bank. </em></p><p><em>The panel analysis applied suggests a robust negative relationship between the credit risk of bank loans and real GDP growth, with a lag response of four periods. In addition, return on assets ratio and bank size show significant negative effect on credit quality, while the net loans to total asset ratio is positively associated with it. </em></p><p><em>The credit risk stress testing results indicate that an adverse scenario of economic downturn produces increase of the frequency of the higher credit loss comparatively to the lower ones for all banks of the sample and that the estimated unexpected losses that would take place in a stress situation can be covered by available capital of these banks.</em></p>


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