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

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.

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,...

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.

Thesis Portfolio Credit Risk Modeling focuses on state-of-the-art credit models largely implemented by banks into their banking risk-assessment and complementary valuation system frameworks. Reader is provided in general with both theoretical and applied (practical) approaches that are giving a clear notion how selected portfolio models perform in real-world environment. Our study comprises CreditMetrics, CreditRisk+ and KMV model. In the first part of the thesis, our intention is to clarify theoretically main features, modeling principles and moreover we also suggest hypotheses about strengths/drawbacks of every scrutinized model. Subsequently, in the applied part we test the models in a lab-environment but with real-world market data. Noticeable stress is also put on model calibration. This enables us to con firm/reject the assumptions we made in the theoretical part. In the very end there follows a straightforward general overview of all outputs and a conclusion.

Credit Default Swaps (CDS) instruments - as an indicator of credit risk - were one of the most prominent innovations in financial engineering. Very limited literature existed on the drivers of CDS spreads before the financial crisis due to the opacity of this market and its lack of transparency. First, this thesis investigates the drivers of CDS spread in the UK banking sector, by considering the role of the housing market, over the period of 2004-2011. I find that, in the long-run, house price dynamics were the main factor contributing to wider CDS spreads. In addition, I show that a rise in stock prices lead to higher availability of capital and therefore increased bank borrowing activities, which led to lower credit risk. Furthermore, findings show that with higher aggregate bank liquidity, banks tend to grant more loans to low-income consumers, thus increasing bank credit risk. In addition, in the short-run, I employ the Structural VAR by imposing short-run restrictions to identify the five shocks arising from the CDS spread, the house price index, the yield spread, the TED spread, and the FTSE100. The SVAR findings indicate that a positive shock to house prices significantly increases the CDS spread in the medium-term, in the UK banking sector. In addition, apart from its own shock, the house price shock explains a big part of the variance (nearly 20%) in CDS spread. These results remained robust even after changing the ordering of the variables in the Structural VAR. Second, considering the bank-level factors across 30 countries and 115 banks, I find most significant bank-level drivers of the CDS spread were asset quality, liquidity and the operations income ratio. As such, banks with better asset quality, high levels of liquidity and operations income ratio were subject to lower CDS spreads and credit risk. Furthermore, larger banks were found to be more risky than smaller banks. We have conducted the U-test and our results indicate the presence of a U-shape relationship between bank size and bank CDS spread. It should be noted that in order to ensure that our results are robust, we used several estimation frameworks, including the FE, RE and alternative Generalized Method of Moments (GMM) approaches, which all prove the existence of a U-shape relationship between the CDS spread and bank size. In addition, we find a threshold level of bank size, which shows that banks growing beyond this point are subject to wider CDS spreads. Finally, I consider the difference in financial systems at country-level and regulatory structures at bank-level, in a panel setting, over the period of 2004-2011. At country-level, my findings directly link financial deepening to higher credit risk, reflecting a sign of credit bubble. Besides, at bank-level, I confirm my previous findings whereby asset quality, liquidity and operations income remain significant drivers of the CDS spread.

Total valuation adjustment (XVA) is a new technique which takes multiple material financial factors into consideration when pricing derivatives. This paper explores how funding costs and counterparty credit risk affect pricing the American option based on no-arbitrage analysis. We review previous studies of European option pricing with different funding costs. The conclusions help to compute the no- arbitrage price of the American option in the model with different borrowing and lending rates. Another model with counterparty credit risk is set up, and this pricing approach is referred to as credit valuation adjustment (CVA). A defaultable bond issued by the counterparty is used to hedge the loss from the option's default. We incorporate these two models to assess the XVA of an American option. The collateral, which protects the option investors from default, is considered in our benchmark model. To illustrate our results, numerical experiments are designed to demonstrate the relationship between XVA and parameters, which include the funding rates, bond's rate of return, and number of periods.

Práce pojednává o řízení rizik v leasingové společnosti. Popisuje proces hodnocení bonity klienta a faktory ovlivňující schvalování obchodních případů. Charakterizuje ratingový a scoringový model v konkrétní leasingové společnosti, hodnotí jejich nedostatky a navrhuje změny na jejich vylepšení. Obsahuje i praktický příklad komplexního hodnocení obchodního případu včetně posouzení bonity klienta prostřednictvím ratingového modelu a nástrojů finanční analýzy.

Cette thèse traite de plusieurs sujets en mathématiques financières: risque de crédit, optimisation de portefeuille et modélisation des taux d’intérêts. Le chapitre 1 consiste en trois études dans le domaine du risque de crédit. La plus innovante est la première dans laquel nous construisons un modèle tel que la propriété d’immersion n’est vérifiée sous aucune mesure martingale équivalente. Le chapitre 2 étudie le problème de maximisation de la somme d’une utilité de la richesse terminale et d’une utilité de la consommation. Le chapitre 3 étudie l’évaluation des produits dérivés de taux d’intérêt dans un cadre multicourbe, qui prend en compte la différence entre une courbe de taux sans risque et des courbes de taux Libor de différents tenors. / This thesis deals with several topics in mathematical finance: credit risk, portfolio optimization and interest rate modeling. Chapter 1 consists of three studies in the field of credit risk. The most innovative is the first one, where we construct a model such that the immersion property does not hold under any equivalent martingale measure. Chapter 2 studies the problem of maximization of the sum of the utility of the terminal wealth and the utility of the consumption, in a case where a sudden jump in the risk-free interest rate induces market incompleteness. Chapter 3 studies the valuation of Libor interest rate derivatives in a multiple-curve setup, which accounts for the spreads between a risk-free discount curve and Libor curves of different tenors.