The Basel Committee on Banking Supervision (BCBS) designed the Internal Ratings Based (IRB) approach, which is based on a single risk factor model. This IRB approach was de-signed to determine banks’ regulatory capital for credit risk. The asymptotic single risk factor (ASRF) model they used makes use of prescribed asset correlations, which banks must use for their credit risk regulatory capital, in order to abide by the BCBS’s rules. Banks need to abide by these rules to reach an international standard of banking that promotes the health of the specific bank. To evaluate whether these correlations are as conservative as the BCBS intended, i.e. not too onerous or too lenient, empirical asset correlations embedded in gross loss data, spanning different economic milieus, were backed out of the regulatory credit risk model. A technique to extract these asset correlations from a Vasicek distribution of empirical loan losses was proposed and tested in international markets. This technique was used to extract the empirical asset correlation, and then compare the prescribed correlations for developed (US) and developing (South Africa) economies over the total time period, as well as a rolling time period. For the first analysis, the BCBS’s asset correlation was conservative when com-pared to South Africa and the US for all loan types. Comparing the empirical asset correlation over a seven-year rolling time period for South Africa and the BCBS, the specified asset cor-relation was found to be as conservative as the BCBS intended. Comparing the US empirical asset correlation for the same rolling period to that of the BCBS, it was found that for all loans, the BCBS was conservative, up until 2012. In 2012 the empirical asset correlation sur-passed that of the BCBS, and thus the BCBS was not as conservative as they had originally intended. / MCom (Risk Management), North-West University, Potchefstroom Campus, 2015

Research report to SBL, Unisa, Midrand. / Credit risk has been identified as the main risk that can result in the failure of a bank due to ineffective credit decisions. It is, therefore, critical for the banks to conduct credit risk assessment on new applicants and existing customers in order to determine the level of affordability and mitigate credit risk. Consumer credit information plays a very important role in credit risk assessment because it can accurately detect and predict default. The aim of this study was to investigate the consequences of inaccurate credit information on bank’s secured lending division. The investigation was conducted using various methods to achieve the objectives of this research. This was done through the exploration of literature review relating to research of the management of consumers credit information in developed and developing countries, and secured lending and inaccurate credit data. A quantitative research methodology was adopted. It was observed that credit risk is seen as the key risk that banks are faced with. It was found that inaccurate consumer credit data can have a negative impact on bank’s operations in terms of consumer’s disputes, higher pricing and consumer overindebtedness. In addition, inaccurate consumer credit data impede access to credit by consumers. One of the general recommendations of this research is that banks should assist in training the consumers to improve their knowledge of credit report. Further studies in the area of corporate or business clients are also recommended as the focus of this research was on individual bank’s clients.

Before the global financial crisis around 2008, the priority of the credit margin was comparatively low and was not taken into consideration as much as today. Many actors believed that credit risk could be neglected at various valuations. Due to that a lot of parties went bankrupt because of the low priorities. Today, this is a natural component in the financial market due to the capital regulation CRR and the Capital requirement directives (CRD IV), which are directly related to Basel III. In this thesis the authors have created a Credit valuation adjustment model, or a CVA-model, on behalf of the consulting firm AGL who want to use it in negotiations of interest rate swap with financial institutions. Factors as expected exposure, loss given default and probability of default are estimated in order to estimate a fair value for CVA. As a final product, the authors have created a model in VBA that can price CVA for individual contracts. This model is then evaluated and a sensitivity analysis is performed to see what impact credit rating and maturity have on the result.

This thesis comprehensively explores the credit risk of Small and Medium Sized Enterprises (SMEs) using transactional characteristics, financial variables and market information. It contributes SMEs credit risk modelling by exploring a range of soft features, such as management capability, industrial sectors, entity type, etc. It is the first study of investigating the concept of management capability through quantitative transactional information. Firstly, models are proposed to assess the credit risk of SMEs by identifying the significant factors. To fulfill this, two studies are carried out. In the first study, logistic regression, survival analysis and ordinal regression are used to model the relationship between transformed financial variables and probability of default. Both the traditional AUROC measure and Hand Statistic are used to evaluate the performances of the models, and they both indicate that logistic regression on weights of evidence transformed data yields the best prediction. Survival model takes an extra element of the time dimension into consideration. Ordinal regression performs poorly possibly due to impact of sample sizes. The factors appeared with highest frequencies are ratios associated with liquidity and growth. The other study predicts the credit risk (‘good’ ‘bad’ and ‘indeterminate’) of the SMEs using transactional characteristics. 35000 SMEs are clustered by different clustering algorithms. It is notably found that most ‘indeterminate’ observations are clustered with ‘bad’ observation, which is different from industry habit of combining ‘indeterminate’ and ‘good’. Logistic regression performs better than ordinal regression according to AUROC measure. In addition, some key points raised in focus group interview with bank managers are seen in the modelling process as significant variables, such as sector belonging to, entity type, region/location, time associated with bank, and account conduct. Secondly, the informational bases of two major models, which are accounting based credit scoring models and Merton type models, are explored to figure out aspects which affect SMEs’ credit risk. 33 financial variables covering nine financial categories are considered. It employs other modelling frameworks rather than the often-used linear regression, which are linear regression with interactions and the Cox proportional hazard model. It is found that weak relationship exists between these two models. The two major models capture different aspects of corporate information, it is suggested that a hybrid model, which incorporate both sources of information, might be considered to predict SMEs financial health. Thirdly, management capability of SMEs is elicited by applying principal component analysis to their transactional characteristics. Management capability is a qualitative idea, and its manifestation in quantitative variables was not explored in previous research. This study indicates some success in determining management capability. It is found that financial measure (credit turnover and debit turnover) and the performance measure (number of days in excess of the account) could be considered as reflecting management capability. Good management can identify trends at a very early stage and take action to mitigate the issue.

Research report to SBL, Unisa, Midrand. / Credit risk has been identified as the main risk that can result in the failure of a bank due to ineffective credit decisions. It is, therefore, critical for the banks to conduct credit risk assessment on new applicants and existing customers in order to determine the level of affordability and mitigate credit risk. Consumer credit information plays a very important role in credit risk assessment because it can accurately detect and predict default. The aim of this study was to investigate the consequences of inaccurate credit information on bank’s secured lending division. The investigation was conducted using various methods to achieve the objectives of this research. This was done through the exploration of literature review relating to research of the management of consumers credit information in developed and developing countries, and secured lending and inaccurate credit data. A quantitative research methodology was adopted. It was observed that credit risk is seen as the key risk that banks are faced with. It was found that inaccurate consumer credit data can have a negative impact on bank’s operations in terms of consumer’s disputes, higher pricing and consumer overindebtedness. In addition, inaccurate consumer credit data impede access to credit by consumers. One of the general recommendations of this research is that banks should assist in training the consumers to improve their knowledge of credit report. Further studies in the area of corporate or business clients are also recommended as the focus of this research was on individual bank’s clients.

This thesis deals with credit risk and selected methods of its evalua- tion. It is focused on assumptions, calculation methods, results and specifics of the CreditMetrics and the CreditRisk+ models. The CreditRisk+ model analytically determines the portfolio credit losses distribution that is caused by defaults of counterparties. In the CreditMetrics model, the credit migration risk is addition- ally considered and the future portfolio value distribution is calculated using the Monte Carlo simulation. The third approach covered in this thesis is the Solvency II, the set of requirements proposed by the European Union for determination of regulatory capital for insurance companies. In the practical part the three ap- proaches are applied on a set of three portfolios of different credit quality. Their results, particularly the determined level of capital required to cover the risk of unexpected credit losses, are analyzed and compared.

This thesis deals with credit risk and selected methods of its evalua- tion. It is focused on assumptions, calculation methods, results and specifics of the CreditMetrics and the CreditRisk+ models. The CreditRisk+ model analytically determines the portfolio credit losses distribution that is caused by defaults of counterparties. In the CreditMetrics model, the credit migration risk is addition- ally considered and the future portfolio value distribution is calculated using the Monte Carlo simulation. The third approach covered in this thesis is the Solvency II, the set of requirements proposed by the European Union for determination of regulatory capital for insurance companies. In the practical part the three ap- proaches are applied on a set of three portfolios of different credit quality. Their results, particularly the determined level of capital required to cover the risk of unexpected credit losses, are analyzed and compared.

This study performs a stress testing exercise on the Italian banking system in view of the 2007 financial crisis which was triggered by the crash of subprime mortgages. At the base of the global financial crisis was a failure of finan- cial regulators to quantify the accumulation of endogenous risks. Following the crisis, stress testing has acquired particular emphasis in the field of risk measurement under the Basel II supervisory framework. An econometric rela- tionship between the probability of default and the macroeconomic indicators is modeled according to the Merton approach for structural analysis using data on the Italian banking system. A latent factor model is employed to under- stand the dependence of the credit risk on the changes in the macroeconomic environment. The resulting relationship is exploited to compute the capital requirement under stressed conditions in order to draw inference about the resilience of the Italian banking system. JEL Classification G0, G01, G17, G10, C50, C22 Keywords Financial crisis, macroeconomic stress testing, credit risk, latent-factor model Author's e-mail jacomessi@yahoo.it Supervisor's e-mail petr.gapko@seznam.cz Abstrakt Klasifikace JEL G0, G01, G17, G10, C50, C22 Klíčová slova Financial crisis, macroeconomic stress test- ing, credit risk,...

M.Com. (Financial Management) / It has been argued that inadequate credit risk management practices and high levels of credit risk was the cause of the 2007 to 2009 global financial crisis, as well as the banking crises over the two past decades, including the 1997 East Asian crisis. As a result, banks have increasingly prioritised credit risk management to ensure acceptable levels of profitability and to keep them from collapsing. However, research on the relationship between credit risk management and profitability in banks in South Africa remains limited. Therefore, this study addressed the question of whether credit risk management has an effect on profitability in South Africa’s four major banks. A quantitative approach was used to establish the relationship between profitability, represented by return on equity (ROE), and credit risk management, represented by two variables, namely capital adequacy ratio (CAR) and the non-performing loans ratio (NPLR). Secondary data for the years 2002 to 2013 was analysed using panel regression and the study concludes that not only does credit risk management have an effect on profitability in South African banks, but that bank size, operating expenses and economic growth also affect the profitability of South African banks. These findings would enable the enhancement of profitability in South Africa through constantly improving credit risk management practices and policies, and by addressing other factors that can negatively affect profitability.

The theme of the work is credit risk insurance. The main objective is to analyze this specific type of insurance, define its role in insurance system and for covering credit risk. Analyzed are both commercial insurance and insurance with state support. The important part of this work is also analysis of czech and world insurance markets and influence of global economic recession. At the end of the work there are presented alternative instruments for minimizing credit risk and their comparison with insurance products.