>Magister Scientiae - MSc / The first two chapters give the background, history and overview of the dissertation, together with the necessary mathematical preliminaries. Thereafter, the next four chapters deal with credit risk and credit derivatives.The final part of the dissertation is devoted to the Basel II bank regulatory framework and the mathematical modeling of asset allocation in bank management, pertaining to credit risk.Credit risk models can be categorized into two groups known as structural models and reduced form models. These models are used in pricing and hedging credit risk. In this thesis we review a variety of credit risk instruments described by models of the said types. One of the strategies utilized by companies to mitigate credit risk is by using credit derivatives.In this thesis, five main types of risk derivatives have been considered: credit swaps, credit linked notes, credit spreads, total return swaps and collaterized debt obligations. Valuation models for the first three derivatives that are mentioned above, are also presented in this dissertation.The material presented include some of the most recent developments in the literature. Our methods range from single-period modeling to application of stochastic optimal control theory. We expand on the material presented from the literature by way of simplifying or clarifying proofs, and by adding illustrative examples in the form of calculations, tables and simulations.Also, the entire Chapter 6 is a new original contribution to the existing literature on mathematical modeling of credit risk. Key words: credit risk; default risk; structural approach; reduced form approach; incomplete information approach; investment strategy; Basel II regulatory framework

Credit scoring is very important process in banking industry during which each potential or current client is assigned credit score that in certain way expresses client's probability of default, i.e. failing to meet his or her obligations on time or in full amount. This is a cornerstone of credit risk management in banking industry. Traditionally, statistical models (such as logistic regression model) are used for credit scoring in practice. Despite many advantages of such approach, recent research shows many alternatives that are in some ways superior to those traditional models. This master thesis is focused on introducing ensemble learning models (in particular constructed by using bagging, boosting and stacking algorithms) with various base models (in particular logistic regression, random forest, support vector machines and artificial neural network) as possible alternatives and challengers to traditional statistical models used for credit scoring and compares their advantages and disadvantages. Accuracy and predictive power of those scoring models is examined using standard measures of accuracy and predictive power in credit scoring field (in particular GINI coefficient and LIFT coefficient) on a real world dataset and obtained results are presented. The main result of this comparative study is that...

In my thesis I have analyzed internal and external scoring model of financial organization. I have prepared comprehensive comparison and evaluation of both internal and external scoring systems. The aim of the thesis was creating a complete assessment of external scoring system with the simplified financial analysis and also with taking into the consideration appropriateness of this offer before approving purchase of external model.

The submitted diploma thesis deals with calculation of capital requirement according to New Basel Capital Accord and calculation of economical capital according to credit model CreditMetrics. The goal of the thesis is to submit hypothesis that level of capital requirement will be higher than economical capital. Analyses were undertaken on the bank loan portfolio made out of 5 corporate and another portfolio, which was gradually extended up to 1000 loans. 5 corporate loans were also examined by effects of correlation of assets and effects of recovery of assets.

The subject of this diploma thesis is managing credit risk in banks, as the most significant risk faced by banks. The aim of this work is to define the basic techniques, tools and methods that are used by banks to manage credit risk. The first part of this work focuses on defining these procedures and describes the entire process of credit risk management, from the definition of credit risk, describing credit strategy and policy, organizational structure, defining the most used credit risk mitigation tools to the regulatory requirements for credit risk management. The second part gives a more detailed view to credit risk measurement and evaluation and possibilities of credit risk hedging. Last part presents credit risk management in practise illustrated by the example of chosen bank.

The aim of this study is to quantify and investigate the effect of the Covid-19 pandemic on non-financial South African firms listed on the Johannesburg Stock Exchange. The study implemented the Merton (1974) model on the 59 largest non-financial firms and calculated the probability of default for each firm before the pandemic and during the pandemic as at each firm's financial year-end. The default probabilities are calculated predominantly from the value and volatility of firm equity. The results emphasize that the Covid-19 pandemic, on average, had a dramatic impact on the probability of default of publicly traded South African firms. The observed increase in default probability was found to be statistically significant at the 5% significance level.

Previous studies seem to agree that increased competition would cause riskier banking behavior. This dissertation shows that when competition intensifies, banks have greater incentives for screening loan applicants, and thus loan quality may improve. In addition, competition fosters banks to rely less on collateral requirements. Hence, banks may be less vulnerable to asset price shocks. The empirical chapter finds evidence of loan quality improvement after removing cross-border entry restrictions in the EU. There is also evidence that banks' behavior across EU countries has converged. / Ph. D.

Yes / In this paper, using China’s risk-free and corporate zero yields together with aggregate credit risk measures and various control variables from 2006 to 2013, we document a puzzle of counter-credit-risk corporate yield spreads. We interpret this puzzle as a symptom of the immaturity of China’s credit bond market, which reveals a distorted pricing mechanism latent in the fundamental of this market. We also find interesting results about relationships between corporate yield spreads and interest rates as well as risk premia and the stock index, and these results are somewhat attributed to this puzzle.

Magistro baigiamajame darbe nagrinėjamas Lietuvos bankinio sektoriaus kredito rizikos valdymas 2008-2011 m. laikotarpiu: teorinėje dalyje pateikiama bendroji kredito rizikos esmė, išskiriami galimi kreditų rizikos valdymo modeliai, metodai ir priemonės, lyginami skirtingų užsienio mokslininkų kredito rizikos valdymo empiriniai tyrimai. Empirinėje dalyje atliekama Lietuvos bankų sektoriaus suteiktų kreditų dinaminė analizė 2008-2011 m., sąryšiu su pagrindiniais kredito ir bankinės veiklos kokybės rodikliais, kreditų palūkanų normomis ir aptariama Lietuvos ūkinė situacija finansinės krizės metu. Konstruktyvioje dalyje pateikiamas galimas kredito rizikos valdymo modelis Lietuvos bankiniame sektoriuje. / In the final thesis of the Master‘s degree there are analyzed the credit risk management in Lithuanian banking sector in year 2008-2011: in the theoretical part there are described the general credit risk definition, highlighted models, approaches and tool for credit risk management, compared the interpretation aspects of credit risk management researches by different scientists. In the empirical part there are represented the dynamics of given credits in year 2008-2011, and its’ relationship with the main measures of the quality of credit portfolio and commercial banks‘ activity. In the last part there is represented the model for credit risk management in Lithuanian banking sector.

Recently, the financial world witnessed a series of major defaults by several institutions and investment banks. Therefore, it is not at all surprising that credit risk analysis have turned out to be one of the most important aspect among the finance community. As credit derivatives are long term instruments, it is affected by the changes in the market conditions. Thus, it is a appropriate to take into consideration the effects of the market economy. This thesis addresses some of the important issues in credit risk analysis in a regime switching market. The main contribution in this thesis are the followings: (1) We determine the price of default able bonds in a regime switching market for structural models with European type payoff. We use the method of quadratic hedging and minimal martingale measure to determine the defaultble bond prices. We also obtain hedging strategies and the corresponding residual risks in these models. The defaultable bond prices are obtained as solution to a system of PDEs (partial differential equations) with appropriate terminal and boundary conditions. We show the existence and uniqueness of the system of PDEs on an appropriate domain. (2) We carry out a similar analysis in a regime switching market for the reduced form models. We extend some of the existing models in the literature for correlated default timings. We price single-name and multi-name credit derivatives using our regime switching models. The prices are obtained as solution to a system of ODEs(ordinary differential equations) with appropriate terminal conditions. (3) The price of the credit derivatives in our regime switching models are obtained as solutions to a system of ODEs/PDEs subject to appropriate terminal and boundary conditions. We solve these ODEs/PDEs numerically and compare the relative behavior of the credit derivative prices with and without regime switching. We observe higher spread in our regime switching models. This resolves the low spread discrepancy that were prevalent in the classical structural models. We show further applications of our model by capturing important phenomena that arises frequently in the financial market. For instance, we model the business cycle, tight liquidity situations and the effects of firm restructuring. We indicate how our models may be extended to price various other credit derivatives.