This thesis studies the Chinese banks’ credit risk assessment using the Post Keynesian approach. We argue that bank loans are the major financial sources in emerging economies and it is uncertainty, an unquantifiable risk, rather than asymmetric information about quantifiable risk, as held by the mainstream approach, which is most important for the risk attached to credit loans, and this uncertainty is particularly important in China. With the universal existence of uncertainty, borrowers and lenders have to make decisions based on convention and experience. With regard to the nature of decision-making, this implies the importance of qualitative methods rather than quantitative methods. The current striking problem in Chinese banking is the large amount of Non-Performing Loans (NPLs) and this research aims to address the NPLs through improving credit risk management. Rather than the previous literature where Western models are introduced into China directly or with minor modification, this work advocates building on China’s conventional domestic methods to deal with uncertainty. We briefly review the background of the Chinese banking history with an evolutionary view and examine Chinese conventions in the development of the credit market. Based on an overview of this history, it is argued that Soft Budget Constraints (SBC) and the underdeveloped risk-assessing mechanism contributed to the accumulation of NPLs. Informed by Western models and experience, we have made several suggestions about rebuilding the Chinese convention of credit risk assessment, based on an analysis of publications and interviews with Chinese bankers. We also suggest some further development of the Asset Management Companies (AMCs) which are used to dispose of the NPLs.

Our thesis aims to help the market participants to understand the source of the risk in green bonds market. We estimate the liquidity risk effects in green bonds' yield spreads as well as controlling for credit risk, bond-specific chracteristics and macroeconomic variables. Both of our liquidity measures suggest that green bonds are more liquid than investment grade US corporate bonds. We find that liquidity effect in green bonds' yield spreads is pronounced, and the result is robust after controlling for potential endogeneity bias. The power of green bonds' liquidity premium is about 10 to 100 times as strong as speculative grade German bonds and investment grade US corporate bonds respectively. In addition to the lack of clear risk profile in green bonds market, our three-stage least squares regression shows that credit risk influences the liquidity risk of green bonds, this indicates that credit risk is a potential source of private information that affects the high liquidity of green bonds. This result has an implication for policy as the credit risk and liquidity risk could be the pitfalls in green bonds market.

Understanding the determinants of aggregated default probabilities (PDs) has attracted substantial research over the past decades. This study addresses two major difficulties in understanding the determinants of aggregate PDs: Model uncertainty and multicollinearity among the regressors. We present Bayesian Model Averaging (BMA) as a powerful tool that overcomes model uncertainty. Furthermore, we supplement BMA with ridge regression to mitigate multicollinearity. We apply our approach to an Austrian dataset. Our findings suggest that factor prices like short term interest rates and energy prices constitute major drivers of default rates, while firms' profits reduce the expected number of failures. Finally, we show that the results of our baseline model are fairly robust to the choice of the prior model size. / Series: Research Report Series / Department of Statistics and Mathematics

Stress testing is a macro-prudential analytical method of assessing the financial system's resilience to adverse events. This thesis describes the methodology of the stress tests and illustrates the stress testing for credit and market risks on the real bank-by-bank data in the two Balkan countries: Croatia and Serbia. Credit risk is captured by the macroeconomic credit risk models that estimate the default rates of the corporate and the household sectors. Setting-up the framework for the countries that were not much covered in former studies and that face the limited availability of data has been the main challenge of the thesis. The outcome can help to reveal possible risks to financial stability. The methods described in the thesis can be further developed and applied to the emerging markets that suffer from the similar data limitations. JEL Classification: E37, G21, G28 Keywords: banking, credit risk, default rate, macro stress testing, market risk

The aim of this thesis is to investigate possible improvement of scoring models prediction power in retail credit segment by using structural models estimating the future development of behavioral score. These models contain the informa- tion about past development of the behavioral score by parameters which take into account the sensitivity of clients' probability of default on individual market and life changes. These parameters are estimated by Markov Chain Monte Carlo methods based on score history. Eight different types of structural models were applied to real data. The diversification measure of individual models is compared using the Gini coefficient. These structural models were compared with each other and also with the existing scoring model of the credit institution which provided the underlying data. 1

The aim of this thesis is to address the implications of Basel III regulation on counterparty credit risk. We analysed the development of OTC market, we addressed systemic risk and the way how central counterparties could mitigate or spread the contagion among banks. We used simulated data to develop a stress test model to find out the impact of counterparty credit risk on banks' capital requirements, in case the interest rate increased extensively. Six pos- sible scenarios of interest rate levels were developed with ascending order of the IR level. From these scenarios we computed the exposure levels and credit valuation adjustment (CVA) as the market value of counterparty credit risk. We came to the following conclusions: (1) Czech banks have enough capital to withstand any interest rate increase in any scenario. (2) Banks with high expo- sure to derivatives like Bank of America, Citibank and JP Morgan would face severe problems if the interest rate increased. (3) There is no direct correlation between credit valuation adjustment and interest rate, the CVA increases faster with the increase of the interest rate.

Master Thesis - Simona Tothova Abstract This thesis empirically investigates impact of countries' bailouts on probability of SME segment distress. The impact is examined by multi-period logit model where dependent variable is distress rate and explanatory variables includes self-constructed bailout variable, several binary predictors and firm-specific and macroeconomic control variables. The hypotheses are tested on dataset for period from 2005 to 2013 including observations from seven European countries which received financial assistance program (bailout) from Troika. Every bailout from Troika comes with the requirement for austerity measures and our results suggest that impact of bailouts on SMEs probability of distress are depended on the success of application in individual countries and the impacts are more positive in non euro-zone countries. Keywords Bailout, Financial crisis, Credit risk, SME segment, Distress rate Author's e-mail tothova.simona@gmail.com Supervisor's e-mail rado.parrak@gmail.com

Counterparty credit risk is an important type of financial risk. The importance of proper counterparty risk management became most apparent in the wake of the 2008 series of failures of several large banks. Correlation of market factors is an important issue in the calculation of CVA. A notable case of correlation is wrong-way risk which occurs whenever the probability of default of the counterparty is positively correlated with exposure. The basic formulas for CVA and basic counterparty credit risk models do not account for wrong-way risk because its modeling is nontrivial. This thesis aims to answer how well can the impact of wrong-way risk on CVA be approximated with an add-on which only depends on correlation between the price of the underlying asset and the credit spread of the counterparty. The thesis is supplemented by a fully documented implementation of the model in the Mathematica software.

The thesis is focused on the options of quantifying credit risk by using the concept of the KMV model. The introduction outlines the basic approaches to measuring credit risk. In the following chapters is specified the nature of KMV model with the focus on its application in the Czech capital market. Self-calibration of the KMV model is made in this part. The analytical part related to the quantification of credit risk using the KMV model is implemented on selected companies which are traded on the Prague Stock Exchange. The results obtained are consequently confronted with the official rating degrees of agency Moody's.

The thesis examines the relation of the low-interest rate environment to the banks' selected credit risk measures with a panel dataset on banks in Eurozone, Denmark, Japan, Sweden, and Switzerland covering the period 2011-2017. It employs a system GMM framework and a combination of bank-related and macroeconomic variables. This study builds on recent literature on effects of low-interest rates on banks' profitability and estimates the following three hypotheses: The potential effects of the low-interest rate on non-performing loans (NPL) ratio, risk-weighted assets (RWA) to total assets ratio, and changes in Tier 1 capital ratio. There are three main results: Firstly, the results suggest that a prolonged period of negative monetary interest rate can affect the NPL ratio and reveal a possible relationship between the 3M-interbank interest rate and NPL ratio. Thus, the thesis does not reject the first hypotheses. However, it rejects these hypotheses in case of the other two ratios. Secondly, the study finds a bank heterogeneity to be a significant determinant of the credit risk. Finally, using recent data, this thesis contributes to the literature focusing on the drivers of the NPL ratio, RWA to total assets ratio and Tier 1 capital ratio, where in case of the latter two the existing research is...