scholarly journals Internal Models, Subordinated Debt, and Regulatory Capital Requirements for Bank Credit Risk

2002 ◽  
Vol 02 (157) ◽  
pp. 1
Author(s):  
Paul H. Kupiec ◽  
Risks ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 106
Author(s):  
Marco Locurcio ◽  
Francesco Tajani ◽  
Pierluigi Morano ◽  
Debora Anelli ◽  
Benedetto Manganelli

The economic crisis of 2008 has highlighted the ineffectiveness of the banks in their disbursement of mortgages which caused the spread of Non-Performing Loans (NPLs) with underlying real estate. With the methods stated by the Basel III agreements, aimed at improving the capital requirements of banks and determining an adequate regulatory capital, the banks without the skills required have difficulties in applying the rigid weighting coefficients structures. The aim of the work is to identify a synthetic risk index through the participatory process, in order to support the restructuring debt operations to benefit smaller banks and small and medium-sized enterprises (SME), by analyzing the real estate credit risk. The proposed synthetic risk index aims at overcoming the complexity of Basel III methodologies through the implementation of three different multi-criteria techniques. In particular, the integration of objective financial variables with subjective expert judgments into a participatory process is not that common in the reference literature and brings its benefits for reaching more approved and shared results in the debt restructuring operations procedure. Moreover, the main findings derived by the application to a real case study have demonstrated how important it is for the credit manager to have an adequate synthetic index that could lead to the avoidance of risky scenarios where several modalities to repair the credit debt occur.


2020 ◽  
Vol 13 (5) ◽  
pp. 89 ◽  
Author(s):  
Faridah Najuna Misman ◽  
M. Ishaq Bhatti

In less than a decade, the Islamic Banking (IB) industry has become an essential part of the global financial system. During the last ten years, the IB industry has witnessed changes in economic conditions and proved to be resilient during the periods of financial crisis. This paper aims to examine the important issues related to credit risk in selected Islamic banks in nine countries from Association of South East Asian Nations (ASEAN) and Gulf Cooperation Council (GCC) regions. It employs the generalized least squares panel data regression, to estimate the ratio of non-performance financing to total financing as dependent variables and bank specific variables (BSV) to determine the credit risk. It uses 12 years of unbalanced panel data from 40 different Islamic banks. The overall findings show that financing quality has a significant positive effect on credit risk. It is observed that the larger IBs owned more assets with lower credit risk compared to smaller banks. The bank’s age is also an important factor influencing the credit risk level. Moreover, regulatory capital significantly reduces the credit risk exposure adherence to the minimum regulatory capital requirements which help IBs to manage their credit risk exposures. It was also observed that IBs were not affected by the global financial crisis due to less credit risk compared to the conventional banks.


2021 ◽  
Vol 14 (5) ◽  
pp. 211
Author(s):  
Iryna Yanenkova ◽  
Yuliia Nehoda ◽  
Svetlana Drobyazko ◽  
Andrii Zavhorodnii ◽  
Lyudmyla Berezovska

This article deals with the issue of managing bank credit risk using a cost risk model. Modeling of bank credit risk management was proposed based on neural-cell technologies, which expand the possibilities of modeling complex objects and processes and provide high reliability of credit risk determination. The purpose of the article is to improve and develop methodical support and practical recommendations for reducing the level of risk based on the value-at-risk (VaR) methodology and its subsequent combination with methods of fuzzy programming and symbiotic methodical support. The model makes it possible to create decision support subsystems for nonperforming loan management based on the neuro-fuzzy approach. For this paper, economic and mathematical tools (based on the VaR methodology) were used, which made it possible to analyze and forecast the dynamics of overdue payment; assess the quality of the credit portfolio of the bank; determine possible trends in bank development. A scientific and practical approach is taken to assess and forecast the degree of credit problematicity by qualitative criteria using a mathematical model based on a fuzzy technology, which can forecast the increased risk of loan default at an early stage in the process of monitoring the loan portfolio and model forecasting changes in the degree of credit problematicity on change of indicators. A methodology is proposed for the analysis and forecasting of indicators of troubled loan debt, which should be implemented as software and included in the decision support system during the process of monitoring the risk of the bank’s credit portfolio.


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