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.

The objective of this project is to investigate and model the quantitative connection between market prices of credit default swaps and the market perceived probability and timing of default by the underlying borrower. We quantify the credit risk of a borrower in a two-way relationship: calculate the term structure of default probabilities from the market prices of traded CDSs and calculate prices of CDSs from the probability distribution of the time-to-default.

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.

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.

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.

En el Perú tenemos diversas maneras de analizar y controlar el otorgamiento del crédito, cada tipo de crédito presenta una regulación distinta. En el presente proyecto de investigación nos enfocaremos en buscar el mejor método que permita controlar la cartera vencida de una empresa comercial. En la actualidad existen diversos parámetros como en la que brinda la Superintendencia de Banca Seguros y AFP, cálculo de la Pérdida Esperada según la normativa de Basilea II y una tercera propuesta que es el cómo se decide en un comité autónomo de Créditos. Para los tres parámetros se analiza el proceso de Provisiones, las provisiones son recursos que usan las empresas como prevención para proteger sus activos, esta estrategia ayuda a la empresa en el control de su cartera crediticia y mitigar el riesgo crediticio para la toma de decisiones e implantar nuevas políticas de control y seguimiento de la misma. Por último, revisaremos el impacto en su Estado de Resultado a fin de analizar el mejor método que una empresa Comercial podría adoptar afectando en el menos posible su Utilidad Neta. Para esto analizaremos una empresa comercial que vende productos de consumo a nivel nacional y se usará una data de cinco años (2014-2018). / In Peru we have different ways of analyzing and controlling the granting of credit, each type of credit has a different regulation. In this research project we will focus on finding the best method to control the overdue portfolio of a commercial company. At present there are several parameters such as the one provided by the Superintendence of Insurance Banking and AFP, calculation of the Expected Loss according to Basel II regulations and a third proposal which is how to decide in an autonomous Credit Committee. For the three parameters, the Provisions process is analyzed, the provisions are resources that companies use as prevention to protect their assets, this strategy helps the company to control its credit portfolio and mitigate credit risk for decision making and implement new policies to control and monitor it. Finally, we will review the impact on its Income Statement in order to analyze the best method that a Commercial company could adopt affecting as little as possible its Net Profit. For this purpose, we will analyze a commercial company that sells consumer products nationwide and will use a five-year (2014-2018) data. / Tesis

In this dissertation, I consider a range of topics in bankruptcy, credit risk and asset pricing. The first chapter proposes a structural-equilibrium model to examine some economic implications arising from voluntary filing of Chapter 11. The results suggest that conflict of interests (between debtors and creditors) arising from the voluntary filing option causes countercyclical losses in firm value. The base calibration shows that these losses amount to approximately 5% of the ex-ante firm value and are twice those produced by a model without incorporating the business cycles. Furthermore, besides countercyclical liquidation costs as in Chen (2010) and Bhamra, Kuehn and Strebulaev (2010), countercyclical pre-liquidation distress costs and the conflict of interests help to generate reasonable credit spreads, levered equity premium and leverage ratios. The framework nests several important models and prices the firm's contingent claims in closed-form. The second chapter proposes a structural credit risk model with stochastic asset volatility for explaining the credit spread puzzle. The base calibration indicates that the model helps explain the credit spread puzzle with a reasonable volatility risk premium. The model fits well to the dynamics of CDS spreads and equity volatility in the data. The third chapter develops a consumption-based learning model to study the interactions among aggregate liquidity, asset prices and macroeconomic variables in the economy. The model generates reasonable risk-free rates, equity premium, real yield curve, and asset prices in equity and bond markets. The base calibration implies a long-term yield spread of around 185 basis points and a liquidity premium of around 55 basis points for an average firm in the economy. The calibrated yield spread and liquidity premium are consistent with the empirical estimates.

The purpose of this thesis is to analyze the drivers and dynamics of credit losses in Australasian banking over an extended period of time in order to improve the means by which financial institutions manage their credit risks and regulatory bodies safeguard the stability and integrity of the financial system. The analysis is based on a specially constructed data base of credit loss and provisioning data retrieved from original financial reports published by Australian and New Zealand banks. The observation period covers 1980 to 2005, starting at the time when such information was published for the first time in bank financial statements. It moreover covers the time of major crises which occurred in both Australia and New Zealand in the late 1980s and early 1990s. The heterogeneity of reporting the data both amongst banks and through time requires the development of a reporting typology which allows data extraction with equivalent informational content. As a thorough study of credit risks requires long data series often not available from third party data providers, the method developed here will provide value to a range of researchers. Based on an evaluation of many alternative proxies which track a bank's credit loss experience (CLE), the thesis proposes a preferred model for impaired assets expense (as % of loans) as dependent variable, mainly because of its timely nature and good data availability. Explanatory variables include aggregate macro variables of which changes in unemployment and the return in the share markets are found to have the most significant influence on a bank's credit losses. Bank-specific control variables include a pre-provision earnings proxy whose significance points to the use of provisions for the purpose of income smoothing by Australasian banks. The model also controls for size and nature of lending as smaller, retail-oriented housing lenders, on average, exhibit lower loan losses. Clear results are found with regard to the effect of rapid expansion which appears to be followed by a surge of bad debt provisions 2 to 3 years later. Moreover, inefficient banks tend to suffer greater credit losses. An important part of the thesis looks at the characteristics of alternative CLE proxies such as stock of provisions, impaired assets and write-offs which have been used by earlier literature. Estimating the preferred model with such alternative CLE parameters confirms their peculiarities such as the memory character of stock of provisions and the delayed nature of write-offs. These measures correlate rather poorly amongst themselves which calls for caution in the comparative interpretation of earlier studies that use differing CLE proxies.