Dissertations / Theses on the topic 'Credit risk credit risk management'

Credit risk management is becoming more and more important in recent years. Credit risk refers to the risk that an obligor fails to make payments on any type of debt at the time of maturity. Credit risk models are statistical tools to infer the future default probabilities and loss distribution of values of a portfolio of debts. This doctoral thesis focus on the application of credit risk management in different areas. To better understand the credit risk management, in the first chapter, we introduce the basic ideas in credit risk management and review the models developed in the last decades. To empirical test the performance of models reviewed in the first chapter, in the second chapter, we compare the reduce-form model with the structural model based on the China’s stock market. It turns out that both models contribute to explaining the default risk of listed firms, however, reduce-form model outperformances the structural model. The empirical results from the second chapter suggests that reduce-form model can better predict the firm’s default risk, but the correlated default risk between firms has not been answered yet. So therefore in the third chapter, we investigate the correlated default risk using copula theory which has been introduced in the first chapter. Based on the insurances firms and other financial firms in the US market, both short-term and long-term default dynamic correlations are found. Another interesting finding from the third chapter is that insurance firms which were considered to be stable actually have higher default risk. This motive us to further explore the determinants of default risk of insurance firms in the fourth chapter and new risk factors (macroeconomic and insurance-specific variables) are found.

In this thesis, efforts are devoted to the stochastic modeling, measurement and evaluation of credit risks, the development of mathematical and statistical tools to estimate and predict these risks, and methods for solving the significant computational problems arising in this context. The reduced-form intensity based credit risk models are studied. A new type of reduced-form intensity-based model is introduced, which can incorporate the impacts of both observable trigger events and economic environment on corporate defaults. The key idea of the model is to augment a Cox process with trigger events. In addition, this thesis focuses on the relationship between structural firm value model and reduced-form intensity based model. A continuous time structural asset value model for the asset value of two correlated firms with a two-dimensional Brownian motion is studied. With the incomplete information introduced, the information set available to the market participants includes the default time of each firm and the periodic asset value reports. The original structural model is first transformed into a reduced-form model. Then the conditional distribution of the default time as well as the asset value of each name are derived. The existence of the intensity processes of default times is proven and explicit form of intensity processes is given in this thesis. Discrete-time Markovian models in credit crisis are considered. Markovian models are proposed to capture the default correlation in a multi-sector economy. The main idea is to describe the infection (defaults) in various sectors by using an epidemic model. Green’s model, an epidemic model, is applied to characterize the infectious effect in each sector and dependence structures among various sectors are also proposed. The models are then applied to the computation of Crisis Value-at-Risk (CVaR) and Crisis Expected Shortfall (CES). The relationship between correlated defaults of different industrial sectors and business cycles as well as the impacts of business cycles on modeling and predicting correlated defaults is investigated using the Probabilistic Boolean Network (PBN). The idea is to model the credit default process by a PBN and the network structure can be inferred by using Markov chain theory and real-world data. A reduced-form model for economic and recorded default times is proposed and the probability distributions of these two default times are derived. The numerical study on the difference between these two shows that our proposed model can both capture the features and fit the empirical data. A simple and efficient method, based on the ordered default rate, is derived to compute the ordered default time distributions in both the homogeneous case and the two-group heterogeneous case under the interacting intensity default contagion model. Analytical expressions for the ordered default time distributions with recursive formulas for the coefficients are given, which makes the calculation fast and efficient in finding rates of basket CDSs.
published_or_final_version
Mathematics
Doctoral
Doctor of Philosophy

Banking is topic, practice, business or profession almost as old as the very existence of man, but literarily it can be rooted deep back the days of the Renaissance (by the Florentine Bankers). It has sprouted from the very primitive Stone-age banking, through the Victorian-age to the technology-driven Google-age banking, encompassing automatic teller machines (ATMs), credit and debit cards, correspondent and internet banking. Credit risk has always been a vicinity of concern not only to bankers but to all in the business world because the risks of a trading partner not fulfilling his obligations in full on due date can seriously jeopardize the affaires of the other partner.

The axle of this study is to have a clearer picture of how banks manage their credit risk. In this light, the study in its first section gives a background to the study and the second part is a detailed literature review on banking and credit risk management tools and assessment models. The third part of this study is on hypothesis testing and use is made of a simple regression model. This leads us to conclude in the last section that banks with good credit risk management policies have a lower loan default rate and relatively higher interest income.

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 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.

I explore whether inter-firm linkages affect firms' credit risk. After controlling for the endogeneity between a firm's credit risk and its dependence on customers and suppliers, I find that supply-chain relationships affect firms' credit risk. My results indicate firms with exposure to major customers have lower ratings, and the level of firm dependence on major customers is negatively associated with firms' credit ratings. Further, I show when a firm's customers also depend on it, this mitigates the negative effect of dependence on credit risk. Finally, I document a negative association between a customer's reliance on its dependent suppliers and the customer's credit rating. Overall, my results provide insights regarding how inter-firm relationships between corporate customers and suppliers affect credit risk.

This thesis is concerned with the assessment, perception and insurance of credit risk. The thesis aims to make contributions both within these areas, and at specific points of interface between them. No attempt is made to develop a single unifying thesis. Rather, a series of partial models are developed, both theoretical and empirical, that develop and connect particular facets of financial economics. The first model demonstrates how movements in market risk produce movements in lender risk-assessment effort. It is demonstrated that deleterious movements in market-wide risk can actually produce a fall in assessment effort. The capricious nature of risk assessment causes changes in the lender's perception of the weights placed on determinants. This has important implications for borrowers' attempts to minimize risk premiums. Time-variability of signal-weights is tested using structural break tests on ordinary least squares and fixed effects panel models. Results suggest a fluid relationship between risk and determinants. Central to empirical investigation is the measurement of perceived risk. A critique of potential measures rejects the use of interest rate spreads - the most commonly used measure - on the basis that they do not take into account the possibility of credit rationing. A model is then constructed to reproduce the standard explanation of credit rationing - Adverse Selection induced Credit Rationing Equilibrium (ASCRE). This model is then extended to include classificatory risk assessment. Assessment is found to reduce the scope for ASCRE, and to cause favourable selection. Credit insurance is then included, and it is found that insurance cover makes risk assessment less of an imperative to lenders, and reduces the utility losses from raising interest rates. The parallel implication is that credit insurance weakens ASCRE, to the extent that full insurance with flat-rate premiums removes the possibility of ASCRE altogether. If the terms of insurance are made contingent on the terms of the loan, a new form of credit rationing emerges: Contingent Insurance induced Credit Rationing Equilibrium (CICRE). CICRE is separate, but not mutually exclusive, to ASCRE. A theoretical model of the demand for loan insurance is developed, and empirically estimated, in the context of the UK mortgage market. Inter alia, the model examines the role of auto-perception of risk determining credit insurance demand. Results reveal the take-up of credit insurance to be relatively insensitive to the borrower's perception ofhis/her own risk.

The thesis investigates the exogenous maturity vintage model (EMV) as a framework for achieving unification in consumer credit risk analysis. We explore how the EMV model can be used in origination modelling, impairment analysis, capital analysis, stress-testing and in the assessment of economic value. The thesis is segmented into five themes. The first theme addresses some of the theoretical challenges of the standard EMV model – namely, the identifiability problem and the forecasting of the components of the model in predictive applications. We extend the model beyond the three time dimensions by introducing a behavioural dimension. This allows the model to produce loan-specific estimates of default risk. By replacing the vintage component with either an application risk or a behavioural risk dimension, the model resolves the identifiability problem inherent in the standard model. We show that the same model can be used interchangeably to produce a point-in-time probability forecast, by fitting a time series regression for the exogenous component, and a through-the-cycle probability forecast, by omitting the exogenous component. We investigate the use of the model for regulatory capital and stress-testing under Basel III, as well as impairment provisioning under IFRS 9. We show that when a Gaussian link function is used the portfolio loss follows a Vašíček distribution. Furthermore, the asset correlation coefficient (as defined under Basel III) is shown to be a function of the level of systemic risk (which is measured by the variance of the exogenous component) and the extent to which the systemic risk can be modelled (which is measured by the coefficient of determination of the regression model for the exogenous component). The second theme addresses the problem of deriving a portfolio loss distribution from a loan-level model for loss. In most models (including the Basel-Vašíček regimes), this is done by assuming that the portfolio is infinitely large – resulting in a loss distribution that ignores diversifiable risk. We thus show that, holding all risk parameters constant, this assumption leads to an understatement of the level of risk within a portfolio – particularly for small portfolios. To overcome this weakness, we derive formulae that can be used to partition the portfolio risk into risk that is diversifiable and risk that is systemic. Using these formulae, we derive a loss distribution that better-represents losses under portfolios of all sizes. The third theme is concerned with two separate issues: (a) the problem of model selection in credit risk and (b) the problem of how to accurately measure probability of insolvency in a credit portfolio. To address the first problem, we use the EMV model to study the theoretical properties of the Gini statistic for default risk in a portfolio of loans and derive a formula that estimates the Gini statistic directly from the model parameters. We then show that the formulae derived to estimate the Gini statistic can be used to study the probability of insolvency. To do this, we first show that when capital requirements are determined to target a specific probability of solvency on a through-the-cycle basis, the point-in-time probability of insolvency can be considerably different from the through-the-cycle probability of insolvency – thus posing a challenge from a risk management perspective. We show that the extent of this challenge will be greater for more cyclical loan portfolios. We then show that the formula derived for the Gini statistic can be used to measure the extent of the point-in-time insolvency risk posed by using a through-the-cycle capital regime. The fourth theme considers the problem of survival modelling with time varying covariates. We propose an extension to the Cox regression model, allowing the inclusion of time-varying macroeconomic variables as covariates. The model is specifically applied to estimate the probability of default in a loan portfolio, where the experience is decomposed the experience into three dimensions: (a) a survival time dimension; (b) a behavioural risk dimension; and (c) calendar time dimension. In this regard, the model can also be viewed as an extension of the EMV model – adding a survival time dimension. A model is built for each dimension: (a) the survival time dimension is modelled by a baseline hazard curve; (b) the behavioural risk dimension is modelled by a behavioural risk index; and (c) the calendar time dimension is modelled by a macroeconomic risk index. The model lends itself to application in modelling probability of default under the IFRS 9 regime, where it can produce estimates of probability of default over variable time horizons, while accounting for time-varying macroeconomic variables. However, the model also has a broader scope of application beyond the domains of credit risk and banking. In the fifth and final theme, we introduce the concept of embedded value to a banking context. In longterm insurance, embedded value relates to the expected economic value (to shareholders) of a book of insurance contracts and is used for appraising insurance companies and measuring management's performance. We derive formulae for estimating the embedded value of a portfolio of loans, which we show to be a function of: (a) the spread between the rate charged to the borrower and the cost of funding; (b) the tenure of the loan; and (c) the level of credit risk inherent in the loan. We also show how economic value can be attributed between profits from maturity transformation and profits from credit and liquidity margin. We derive formulae that can be used to analyse the change in embedded value throughout the life of a loan. By modelling the credit loss component of embedded value, we derive a distribution for the economic value of a book of business. The literary contributions made by the thesis are of practical significance. The thesis offers a way for banks and regulators to accurately estimate the value of the asset correlation coefficient in a manner that controls for portfolio size and intertemporal heterogeneity. This will lead to improved precision in determining capital adequacy – particularly for institutions operating in uncertain environments and those operating small credit portfolios – ultimately enhancing the integrity of the financial system. The thesis also offers tools to help bank management appraise the financial performance of their businesses and measure the value created for shareholders.

Graphical User Interface, which is known as GUI, is a way for a person to communicate and interact with a system through icons or other visual indicators. A well designed and intuitive user interface is critical to the success of a system since it encourages a natural interaction between a user and a system, thus conveying information more clearly and efficiently to the user.The aim of this study is to design and develop a user interface that is used in a financial technology company in their credit risk assessment process. The current user interface contains a visualization of an individual credit assessment flow together with a lot of data that is generated in the process. Some of the data is not properly visualized, which leads to confusion among end users.In order to optimize the user experience, a user-centered design approach was used combined with a heuristic evaluation. A new user interface was designed and implemented and according to the heuristic evaluation result, the usability was greatly improved. The new interface is able to help the company to visualize their credit risk assessment process in a better way and facilitate credit officers to make credit decisions. The result could also provide insights to other companies or organizations in presenting their data more clearly and effectively.
Grafiskt användargränssnitt, som även kallas GUI, är ett sätt för en person att kommunicera och interagera med ett system genom ikoner eller andra visuella indikatorer. Ett väl utformat och intuitivt användargränssnitt är avgörande för framgången för ett system, eftersom det uppmuntrar till en naturlig interaktion mellan en användare och ett system och därmed förmedlar information tydligare och effektivare till användaren.Syftet med denna studie är att designa och utveckla ett användargränssnitt som används i ett finansiellt teknikföretag i deras kreditriskbedömningsprocess. Det nuvarande användargränssnittet innehåller en visualisering av ett individuellt kreditbedömningsflöde tillsammans med mycket data som genereras i processen. En del av data är inte korrekt visualiserade, vilket leder till förvirring bland slutanvändare.För att optimera användarupplevelsen användes en användarcentrerad designmetod i kombination med en heuristisk utvärdering. Ett nytt användargränssnitt designades och implementerades och enligt det heuristiska utvärderingsresultatet förbättrades användbarheten kraftigt. Det nya gränssnittet kan hjälpa företaget att visualisera sin kreditriskbedömningsprocess på ett bättre sätt och underlätta kreditansvariga att fatta kreditbeslut. Resultatet kan också ge andra företag eller organisationer insikter om att presentera sina uppgifter tydligare och mer effektivt.

This report is for MA575: Market and Credit Risk Models and Management, given by Professor Marcel Blais. In this project, three different methods for estimating Value at Risk (VaR) and Expected Shortfall (ES) are used, examined, and compared to gain insightful information about the strength and weakness of each method. In the first part of this project, a portfolio of underlying assets and vanilla options were formed in an Interactive Broker paper trading account. Value at Risk was calculated and updated weekly to measure the risk of the entire portfolio. In the second part of this project, Value at Risk was calculated using semi-parametric model. Then the weekly losses of the stock portfolio and the daily losses of the entire portfolio were both fitted into ARMA(1,1)-GARCH(1,1), and the estimated parameters were used to find their conditional value at risks (CVaR) and the conditional expected shortfalls (CES).

Frail credit risk management practices have dragged financial intermediaries into financial crisis or bankruptcy if not well managed. The study seeks to appraise the intent to which Nigerian banks have meritoriously managed credit risk after the 2005 bank recapitalization exercise. It also seeks to establish other factors on why some banks to fail the 2009 stress test conducted by Central Bank of Nigeria. The study found that the failure to effectively manage credit risk as a result of increase capital inflow into the banking system and excessive lending contributed immensely to the 2009 banking crisis. The research also identified lax credit risk management practices as a major factor that caused the crisis. Furthermore, banks to develop and implement their credit I scoring models for assessing, monitoring and reviewing of credit portfolios and other credit granted.

Credit risk is one of the most general risks that exist in the financial market and a major risk faced by financial institutions. Credit risk management (CRM) is to identify, measure, monitor, and control risk arising from the possibility of default in loan repayments. The primary objective of CRM of rural commercial banks (RCBs) is to maintain risk within acceptable parameters and satisfy the regulatory requirements. CRM has long been the focus of governments, regulatory authorities and financial institutions. This thesis examines the importance of CRM for RCBs, which has been overlooked in the literature, and attempts to develop a CRM framework for RCBs. It has four specific research objectives: 1) to discuss the differences between RCBs and city based-commercial banks; 2) to examine the importance of CRM for RCBs and identify the approaches available for banks to manage credit risks; 3) to identify the key factors that have influenced the credit evaluation and assessment, as well as credit risk control in the context of China's RCBs; and 4) to propose a practicable CRM framework that suits the characteristics of Chinese RCBs. This study adopts qualitative analysis and case study approaches to identify key factors contributing to the failure of RCBs' customers, resulting in loan defaults and banks' credit risk. The quantitative-based CRM tools available for large financial institutions do not meet the requirements of RCBs because the main customers of RCBs are small and medium-sized enterprises (SMEs) and farming households and there is a lack of financial data and credit rating relating to these customers. In addition to normal risks faced by financial institutions, RCBs in China are also exposed to risks specifically to rural commercial banking business and in particular, farming-related loans and services. This study proposes a CRM framework for RCBs in China. The framework is based on the identification of business failures of RCBs' customers and factors contributing to the failures of SMEs and farming households. The framework is divided into five steps. The first step is to distinguish business failure and closure. The second step is to identify factors contributing to the failure of customers, which should be considered from environmental, operational, financial and guanxi aspects. The third step is to use PCA to identify principal factors. The fourth step is to design a credit risk analysis model with an analysis of these principal factors. The final step is to use the credit risk analysis model to manage credit risks of their portfolios and individual loans provided to SMEs and farming households. The CRM framework has been confirmed by practitioners through interviews conducted in the case bank. Interviews raise a number of issues relating to the development of a CRM model and assessment of credit risk of SMEs in China. The case study through an analysis of documents of the case bank reveals the importance of CRM and organisational structure in risk management and CRM. The case study presents evidence of lacking of practical methods in managing credit risk by RCBs in China. The proposed framework expects to address the problem. This study has made several contributions to the literature that studies CRM in financial institutions in general and RCBs in particular. This study critically identifies the current lack of studies specifically addressing the RCBs' CRM, and proposes a CRM framework for RCBs. The framework considers financial and non-financial variables to analyse SMEs and farming household for which financial information is very limited. Using nonfinancial variables along with financial variables as predictors of business failure significantly improves credit analysis quality and accuracy. Also, this study recognises guanxi as risk potentials affecting the business of SMEs and farming households and includes guanxi risks in the framework. The consideration of guanxi in credit risk analysis fits well with China's business environment.

This dissertation comprises three essays. In the first essay, we provide results for the valuation of European style contingent claims for a large class of specifications of the underlying asset returns. Our valuation results obtain in a discrete time, an infinite state-space setup using the no-arbitrage principle. Our approach allows for general forms of heteroskedasticity in returns. It also allows for conditional non-normal return inno- vations, which is critically important because heteroskedasticity alone does not su¢ ce to capture the option smirk. The resulting risk-neutral return dynamics are from the same family of distributions as the physical return dynamics. Our framework nests the valuation results obtained by Duan (1995), and Heston and Nandi (2000) by allowing for a time-varying price of risk and non-normal innovations. In the second essay, we develop a methodology to study the linkages between equity and corporate bond risk premia and apply it to a large panel of corporate bond transaction data. We find that a significant part of the time variation in bond default risk premia can be explained by equity-implied bond risk premium estimates. We compute these estimates using a recent structural credit risk model. In addition, we show by means of linear regressions that augmenting the set of variables predicted by typical structural models with equity-implied bond default risk premia significantly increases explanatory power. This, in turn, suggests that time-varying risk premia are a desirable feature for future structural models. In the third essay, we first document empirically that embedded put option values are related to proxies for term structure risk, default risk and illiquidity. In a second step, we develop a valuation model that simultaneously captures default and interest rate risk. We use this model to disentangle the reduction in yield spread enjoyed by putable bonds that can be attributed to each risk. Perhaps surprisingly, the most imp
Cette thèse comprend trois essais. Dans le premier essai nous avons développé des résultats pour l'évaluation des actifs contingents de type Européen pour une vaste classe de spécification du rendement de l'actif sous-adjacent. Notre méthode est obtenue dans une économie à temps discret et espace infini en utilisant seulement la condition de non arbitrage dans le marché. Notre approche permet une forme générale d'heteroskedasticité pour les rendements. Les résultats pour les cas d'homoskedasticité sont retrouvés comme des cas spéciaux. Notre approche permet d'accommoder les cas où l'innovation dans la dynamique du rendement est conditionnellement non normale. Cette flexibilité est extrêmement importante car l'heteroskedasticité seulement n'est pas su¢ sant pour cap- turer le phénomène du "smirk" dans les prix des options. Nos résultats emboîtent ceux obtenue dans Duan (1995) et Heston et Nandi (2000). Dans le deuxième essai nous avons développé une méthodologie pour étudier le lien entre la prime de risque dans les obligations corporatives et celle de l'actif risqué de la firme. Nous avons appliqué notre méthode sur une large base de données des transactions des obligations corporatives. Nous avons trouvé qu'une importante partie de la variation temporelle du risque de défaut dans ces obligations peut être expliquer par des estimées de la prime de risque du défaut reconstruite à partir de l'actif risqué de la firme seulement. En plus, nous avons démontré à l'aide des régressions linéaires qu'augmentant la série des variables prédites par le modèle structurel par notre estimé de la prime du risque de défaut ajoute une explication significative. Dans le troisième essai nous avons montré empiriquement que la valeur des obligations corporatives du type" puttable" est reliée aux risques de défaut, de liquidité et celui dû aux taux d'intérêts. Dans la deuxième étape de ce projet nous avons développé un mo

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.

El risc de crèdit s'associa a l'eventual incompliment de les obligacions de pagament per part dels creditors. En aquest cas, l'interès principal de les institucions financeres és mesurar i gestionar amb precisió aquest risc des del punt de vista quantitatiu. Com a resposta a l'interès esmentat, aquesta tesi doctoral, titulada "Structural Credit Risk Models: Estimation and Applications", se centra en l'ús pràctic dels anomenats "models estructurals de risc de crèdit". Aquests models es caracteritzen perquè estableixen una relació explícita entre el risc de crèdit i diverses variables fonamentals, la qual cosa permet un ventall ampli d'aplicacions. Concretament, la tesi analitza el contingut informatiu tant del mercat d'accions com del mercat de CDS sobre la base dels models estructurals esmentats.

El primer capítol, estudia la velocitat distinta amb què el mercat d'accions i el mercat de CDS incorporen nova informació sobre el risc de crèdit. L'anàlisi se centra a respondre dues preguntes clau: quin d'aquests mercats genera una informació més precisa sobre el risc de crèdit i quins factors determinen el diferent contingut informatiu dels indicadors respectius de risc, és a dir, les primes de crèdit implícites en el mercat d'accions enfront del de CDS. La base de dades utilitzada inclou 94 empreses (40 d'europees, 32 de nordamericanes i 22 de japoneses) durant el període 2002-2004. Entre les conclusions principals destaquen la naturalesa dinàmica del procés de price discovery, una interconnexió més gran entre ambdós mercats i un major domini informatiu del mercat d'accions, associat a uns nivells més elevats del risc de crèdit, i, finalment, una probabilitat més gran de lideratge informatiu del mercat de CDS en els períodes d'estrès creditici.

El segon capítol se centra en el problema de l'estimació de les variables latents en els models estructurals. Es proposa una nova metodologia, que consisteix en un algoritme iteratiu aplicat a la funció de versemblança per a la sèrie temporal del preu de les accions. El mètode genera estimadors de pseudomàxima versemblança per al valor, la volatilitat i el retorn que s'espera obtenir dels actius de l'empresa. Es demostra empíricament que aquest nou mètode produeix, en tots els casos, valors raonables del punt de fallida. A més, aquest mètode és contrastat d'acord amb les primes de CDS generades. S'observa que, en comparació amb altres alternatives per fixar el punt de fallida (màxima versemblança estàndard, barrera endògena, punt d'impagament de KMV i nominal del deute), l'estimació per pseudomàxima versemblança proporciona menys divergències.

El tercer i darrer capítol de la tesi tracta la qüestió relativa a components distints del risc de crèdit a la prima dels CDS. Més concretament, estudia l'efecte del desequilibri entre l'oferta i la demanda, un aspecte important en un mercat on el nombre de compradors (de protecció) supera habitualment el de venedors. La base de dades cobreix, en aquest cas, 163 empreses en total (92 d'europees i 71 de nord-americanes) per al període 2002- 2008. Es demostra que el desequilibri entre l'oferta i la demanda té, efectivament, un paper important a l'hora d'explicar els moviments a curt termini en els CDS. La influència d'aquest desequilibri es detecta després de controlar l'efecte de variables fonamentals vinculades al risc de crèdit, i és més gran durant els períodes d'estrès creditici. Aquests resultats il·lustren que les primes dels CDS reflecteixen no tan sols el cost de la protecció, sinó també el cost anticipat per part dels venedors d'aquesta protecció per tancar la posició adquirida.
El riesgo de crédito se asocia al potencial incumplimiento por parte de los acreedores respecto de sus obligaciones de pago. En este sentido, el principal interés de las instituciones financieras es medir y gestionar con precisión dicho riesgo desde un punto de vista cuantitativo. Con objeto de responder a este interés, la presente tesis doctoral titulada "Structural Credit Risk Models: Estimation and Applications", se centra en el uso práctico de los denominados "Modelos Estructurales de Riesgo de Crédito". Estos modelos se caracterizan por establecer una conexión explícita entre el riesgo de crédito y diversas variables fundamentales, permitiendo de este modo un amplio abanico de aplicaciones. Para ser más explícitos, la presente tesis explora el contenido informativo tanto del mercado de acciones como del mercado de CDS sobre la base de los mencionados modelos estructurales.

El primer capítulo de la tesis estudia la distinta velocidad con la que el mercado de acciones y el mercado de CDS incorporan nueva información sobre el riesgo de crédito. El análisis se centra en contestar dos preguntas clave: cuál de estos mercados genera información más precisa sobre el riesgo de crédito, y qué factores determinan en distinto contenido informativo de los respectivos indicadores de riesgo, esto es, primas de crédito implícitas en el mercado de acciones frente a CDS. La base de datos utilizada engloba a 94 compañías (40 europeas, 32 Norteamericanas y 22 japonesas) durante el periodo 2002-2004. Entre las principales conclusiones destacan la naturaleza dinámica del proceso de price discovery, la mayor interconexión entre ambos mercados y el mayor dominio informativo del mercado de acciones asociados a mayores niveles del riesgo de crédito, y finalmente la mayor probabilidad de liderazgo informativo del mercado de CDS en los periodos de estrés crediticio.

El segundo capítulo se centra en el problema de estimación de variables latentes en modelos estructurales. Se propone una nueva metodología consistente en un algoritmo iterativo aplicado a la función de verosimilitud para la serie temporal del precio de las acciones. El método genera estimadores pseudo máximo verosímiles para el valor, volatilidad y retorno esperado de los activos de la compañía. Se demuestra empíricamente que este nuevo método produce en todos los casos valores razonables del punto de quiebra. El método es además contrastado en base a las primas de CDS generadas. Se observa que, en comparación con otras alternativas para fijar el punto de quiebra (máxima verosimilitud estándar, barrera endógena, punto de impago de KMV, y nominal de la deuda), la estimación por pseudo máxima verosimilitud da lugar a las menores divergencias.

El tercer y último capítulo de la tesis aborda la cuestión relativa a componentes distintos al riesgo de crédito en la prima de los CDS. Se estudia más concretamente el efecto del desequilibrio entre oferta y demanda, un aspecto importante en un mercado donde el número de compradores (de protección) supera habitualmente al de vendedores. La base de datos cubre en este caso un total de 163 compañías (92 europeas y 71 norteamericanas) para el periodo 2002-2008. Se demuestra que el desequilibrio entre oferta y demanda tiene efectivamente un papel importante a la hora de explicar los movimientos de corto plazo en los CDS. La influencia de este desequilibrio se detecta una vez controlado el efecto de variables fundamentales ligadas al riesgo de crédito, y es mayor durante los periodos de estrés crediticio. Estos resultados ilustran que las primas de los CDS reflejan no sólo el coste de la protección, sino el coste anticipado por parte de los vendedores de tal protección de cerrar la posición adquirida.
Credit risk is associated with potential failure of borrowers to fulfill their obligations. In that sense, the main interest of financial institutions becomes to accurately measure and manage credit risk on a quantitative basis. With the intention to respond to this task this doctoral thesis, entitled "Structural Credit Risk Models: Estimation and Applications", focuses on practical usefulness of structural credit risk models that are characterized with explicit link with economic fundamentals and consequently allow for a broad range of application possibilities. To be more specific, in essence, the thesis project explores the information on credit risk embodied in the stock market and market for credit derivatives (CDS market) on the basis of structural credit risk models. The issue addressed in the first chapter refers to relative informational content of stock and CDS market in terms of credit risk. The overall analysis is focused on answering two crucial questions: which of these markets provides more timely information regarding credit risk, and what are the factors that influence informational content of credit risk indicators (i.e. stock market implied credit spreads and CDS spreads). Data set encompasses international set of 94 companies (40 European, 32 US and 22 Japanese) during the period 2002-2004. The main conclusions uncover time-varying behaviour of credit risk discovery, stronger cross market relationship and stock market leadership at higher levels of credit risk, as well as positive relationship between the frequency of severe credit deterioration shocks and the probability of the CDS market leadership.

Second chapter concentrates on the problem of estimation of latent parameters of structural models. It proposes a new, maximum likelihood based iterative algorithm which, on the basis of the log-likelihood function for the time series of equity prices, provides pseudo maximum likelihood estimates of the default barrier and of the value, volatility, and expected return on the firm's assets. The procedure allows for credit risk estimation based only on the readily available information from stock market and is empirically tested in terms of CDS spread estimation. It is demonstrated empirically that, contrary to the standard ML approach, the proposed method ensures that the default barrier always falls within reasonable bounds. Moreover, theoretical credit spreads based on pseudo ML estimates offer the lowest credit default swap pricing errors when compared to the other options that are usually considered when determining the default barrier: standard ML estimate, endogenous value, KMV's default point, and principal value of debt.

Final, third chapter of the thesis, provides further evidence of the performance of the proposed pseudo maximum likelihood procedure and addresses the issue of the presence of non-default component in CDS spreads. Specifically, the effect of demand-supply imbalance, an important aspect of liquidity in the market where the number of buyers frequently outstrips the number of sellers, is analyzed. The data set is largely extended covering 163 non-financial companies (92 European and 71 North American) and period 2002-2008. In a nutshell, after controlling for the fundamentals reflected through theoretical, stock market implied credit spreads, demand-supply imbalance factors turn out to be important in explaining short-run CDS movements, especially during structural breaks. Results illustrate that CDS spreads reflect not only the price of credit protection, but also a premium for the anticipated cost of unwinding the position of protection sellers.

Arguably, the credit risk models reported in the literature for the retail lending sector have so far been less developed than those for the corporate sector, mainly due to the lack of publicly available data. Having been given access to a dataset on defaulted mortgages kindly provided by a major UK bank, this work first investigates the Loss Given Default (LGD) of mortgage loans with the development of two separate component models, the Probability of Repossession (given default) Model and the Haircut (given repossession) Model. They are then combined into an expected loss percentage. Performance-wise, this two-stage LGD model is shown to do better than a single-stage LGD model (which directly models LGD from loan and collateral characteristics), as it achieves a better Rsquare value, and it more accurately matches the distribution of observed LGD. We next investigate the possibility of including macroeconomic variables into either or both component models to improve LGD prediction. Indicators relating to net lending, gross domestic product, national default rates and interest rates are considered and the interest rate is found to be most beneficial to both component models. Finally, we develop a competing risk survival analysis model to predict the time taken for a defaulted mortgage loan to reach some outcome (i.e. repossession or non-repossession). This allows for a more accurate prediction of (discounted) loss as these periods could vary from months to years depending on the health of the economy. Besides loan- or collateral-related characteristics, we incorporate a time-dependent macroeconomic variable based on the house price index (HPI) to investigate its impact on repossession risk. We find that observations of different loan-to-value ratios at default and different security type are affected differently by the economy. This model is then used for stress test purposes by applying a Monte Carlo simulation, and by varying the HPI forecast, to get different loss distributions for different economic outlooks.

Credit risk management is a significant fragment in financial institutions' security precautions against the downside of their investments. A major quandary within the subject of credit risk is the modeling of simultaneous defaults. Globalization causes economises to be affected by innumerous external factors and companies to become interdependent, which in turn enlarges the complexity of establishing reliable mathematical models. The precarious situation is exacerbated by the fact that managers often suffer from the lack of data. The default correlations are most often calibrated by either using financial and/or market information. However, there exists circumstances where these types of data are inaccessible or unreliable. The problem of scarce data also induces diculties in the estimation of default probabilities. The frequency of insolvencies and changes in credit ratings are usually updated on an annual basis and historical information covers 20-25 years at best. From a mathematical perspective, this is considered as a small sample and standard statistical models are inferior in such situations. The first part of this thesis specifies the so-called entropy model which estimates the impact of macroeconomic fluctuations on the probability of defaults, and aims to outperform standard statistical models for small samples. The second part specifies the CIMDO, a framework for modeling correlated defaults without financial and market data. The last part submits a risk analysis framework for calculating the uncertainty in the simulated losses. It is shown that the entropy model will reduce the variance of the regression coefficients but increase its bias compared to the OLS and Maximum Likelihood. Furthermore there is a significant difference between the Student's t CIMDO and the t-Copula. The former appear to reduce the model uncertainty, however not to such extent that evident conclusions were carried out.
Kreditriskhantering är den enskilt viktigaste delen i banker och finansiella instituts säkerhetsåtgärder mot nedsidor i deras investeringar. En påtaglig svårighet inom ämnet är modelleringen av simultana konkurser. Globalisering ökar antalet parametrar som påverkar samhällsekonomin, vilket i sin tur försvårar etablering av tillförlitliga matematiska modeller. Den prekära situationen förvärras av det faktum att analytiker genomgående saknar tillräcklig data. Konkurskorrelation är allt som oftast kalibrerad med hjälp av information från årsrapporter eller marknaden. Dessvärre existerar det omständigheter där sådana typer av data är otillgängliga eller otillförlitliga. Samma problematik skapar även svårigheter i skattningen av sannolikheten till konkurs. Uppgifter såsom frekvensen av insolventa företag eller förändringar i kreditbetyg uppdateras i regel årligen, och historisk data täcker i bästa fall 20-25 år. Syftet med detta examensarbete är att ge ett övergripande ramverk för kreditriskhantering i avsaknad av finansiell information och marknadsdata. Detta innefattar att estimera vilken påverkan fluktueringar i makroekonomin har på sannolikheten för konkurs, modellera korrelerade konkurser samt sammanfatta ett ramverk för beräkning av osäkerheten i den estimerade förlustdistributionen. Den första delen av examensarbetet specificerar den så kallade entropy modellen. Denna skattar påverkan av makroekonomin på sannolikheterna för konkurs och ämnar att överträffa statistiska standardmodeller vid små datamängder. Den andra delen specificerar CIMDO, ett ramverk för beräkning av konkurskorrelation när marknads- och företagsdata saknas. Den sista delen framlägger ett ramverk för riskanalys av förlustdistributionen. Det visas att entropy modellen reducerar variansen i regressionskoefficienter men till kostnad av att försämra dess bias. Vidare är det en signifikant skillnad mellan student’s t CIMDO och t-Copula. Det förefaller som om den förstnämnda reducerar osäkerheten i beräkningarna, men inte till den grad att uppenbara slutsatser kan dras.

Small, Medium and Micro Enterprises (SMMEs) make a significant contribution to the South African Economy. Regardless of size, these businesses have the ability to create employment, make a generous contribution to tax collections, uplift communities and serve as a beacon of hope for those trapped in the cycle of poverty and unemployment. However, SMMEs lack access to much-needed financial resources that are critical for their growth. Development Finance Institutions (DFIs) aim to bridge the gap between the SMME’s financial needs and the development of the respective SMME businesses, by providing funding to entrepreneurs with potentially viable businesses and ideas. Debt funding to these SMMEs are based on sound commercial lending principles that take various non-quantitative variables into account. The sustainability of SMMEs is a primary concern to all participants in the economy, as it is known that SMME failure rates are high Therefore, the primary objective of this study was to investigate the impact that the credit risk management practices of DFIs have on the sustainability of SMMEs, by examining a case study of a typical DFI. An electronic questionnaire survey was considered as an appropriate measurement method for this study. The targeted population of the study included SMMEs in the Eastern Cape that are Trust for Urban Housing (TUHF) clients and 23 SMMEs were identified as part of the study sampling frame. A total number of 14 questionnaires were returned out of the 23 targeted SMMEs - giving a response rate of 61%. The quantitative data was processed using the STATISTICA program, leading to appropriate descriptive statistical analyses. In order to better understand the impact of credit risk management practices on the sustainability of SMMEs, a hypothesis was formulated and linear regression analysis was used to establish the statistical significance of certain credit risk principles and sustainability characteristics. The results of the empirical study revealed that credit risk management practises do impact on the sustainability of SMMEs. Further, by testing the hypothesis, it was also revealed that certain sustainability variables are regarded as more important than others.

Into the Thesis, the author will try to give the basis of risk management and asset pricing. Both of them are fundamental elements to understand how the financial models work; this topic is judged important in the perspective of successive studies in financial math: having clear the starting point makes things easier. From the title it is clear that modern and more complex models will be only touched upon. We decide to divide the dissertation in two different parts because, in our opinion, it is more evident that two different ways to approach at credit risk exist: on one side we try to quantify the risk deriving from giving credit, on the other we will establish a strategy that allows us to invest money with the aim to pay the other part of the agreement. Everything became more clear chapter by chapter. Financial institutions like banks are exposed at both of this type of risk. Chapters 1 and 5 are the center of this thesis: they represent the zero point from which the modern models were originated.

The question of whether ratings agencies convey new information to financial markets when they assign new ratings or change previous ratings has been debated for at least 40 years. In this study I first examine equity market, bond market and CDS market reactions to long and short term rating changes from S&P, Fitch and Moody's. I find that not all the credit rating changes affect the market but only those classified as unanticipated. Subsequently, I study whether the regulatory setting, in which the Credit Ratings Agencies work, can possibly affect the financial markets reactions. Lastly I show that the probability of a future rating change is severely affected by different factors proportional hazard rate models.

In this thesis, we study random bridges of a certain class of Lévy processes and their applications to credit risky asset pricing. In the first part, we construct the compound random bridges(CLRBs) and analyze some tools and properties that make them suitable models for information processes. We focus on the Markov property, dynamic consistency, measure changes and increment distributions. Thereafter, we consider applications in credit risky asset pricing. We generalize the information based credit risky asset pricing framework to incorporate prematurity default possibilities. Lastly we derive closed-form expressions for default trends and intensities for credit risky bonds with CLRB as the background partial information process. We obtain analytical expressions for specific CLRBs. The second part looks at application of stochastic filtering in the current information based asset pricing framework. First, we formulate credit risky asset pricing in the information-based framework as a filtering problem under incomplete information. We derive the Kalman-Bucy filter in one dimension for bridges of Lévy processes with a given finite variance.

Thesis: S.M. in Management Studies, Massachusetts Institute of Technology, Sloan School of Management, 2019
Cataloged from PDF version of thesis.
Includes bibliographical references (page 40).
Credit risk is often a critical risk in the financial sector. Therefore, how a financial institution manages its credit risk is an important determinant of profitability and solvency. In this regard, the identification and measurement of credit risk is the first component of efficient risk management. Correct and timely credit ratings are important for risk management systems, and for informing regulators about financial system risks. Credit risk is the main risk faced by the Paraguayan financial sector. Effectively managing it requires banking supervision and regulation in line with international best practices. As a step in that direction, this research assesses the Paraguayan banking regulation of credit risk and compares it to the principles and the best practices about credit risk management issued by the Basel Committee. I propose principles to guide the implementation of statistical models for better measurement of credit risk in Paraguayan financial institutions.
by Bruno Gomez.
S.M. in Management Studies
S.M.inManagementStudies Massachusetts Institute of Technology, Sloan School of Management

LBO transactions were structured increasingly aggressive in the years prior to the outbreak of the financial crisis (2007), as reflected in rising debt proportions. This was followed by many LBO credits experiencing difficulties to adhere to their loan documentations. Bank credit functions played a role in this, giving rise to an investigation into whether their work could be more effective. To identify areas for improvement in their work of evaluating LBO credits and – if such areas can be identified – deduce some potential measures how to address them was the aim of this research. The research is timely as evaluations of LBO credits continue to be required heavily. Many of the credits structured around 2006/2007 will soon require refinancing and in parallel new transactions come to the market. A literature review on the risk factors and cycles relating to LBOs, the simple techniques of portfolio management and LBO credit management practices found that all the ingredients required for effective LBO credit management are available. This is in conflict with the observation of increasing credit risk inherent in these transactions in the years just prior to the outbreak of the financial crisis of 2007. Based on this, 18 experts were interviewed. The results were analysed quantitatively and qualitatively. To enhance robustness, results were discussed with four senior credit executives as well as a focus group discussion of the credit function of one bank. The exploratory results of this research suggest that there is strong awareness of the risk factors in LBOs. The systematic risk in LBOs in form of an LBO cycle however is not considered to a significant degree in credit analysis/credit monitoring. Some important risk factors also receive relatively little attention in credit analysis/credit monitoring and aspects of portfolio management are not used strongly at the level of credit functions. Finally, an area of improvement that had been identified was the utilization of results; i.e. the need to draw consequences from observations made with view to risk factors. Due to the limited scope of the study, updating the results with more recent data as well validation and triangulation of results remain recommended.

Tito Mboweni (2011) said one of South Africa’s biggest tests is the overwhelming the skills shortage. He was echoing the views of Higher Education Minister Blade Nzimande who himself said “South Africa could not afford to have an economy "constrained by a severe lack of skills". There are numerous initiatives that having been undertaken by government in an attempt to solve the skills shortage problem. However, these initiatives are not aimed at the tertiary education system. The tertiary education system is the focus of this study as the author investigates how the NMMU Business School can play a significant role in addressing the skills shortage in the credit risk management sector. Following a literature review, surveys were completed by the NMMU Business School MBA students (ninety of them completed it) and personal interviews were conducted with three Provincial HR managers from South Africa’s “four big banks” in Nelson Mandela Bay (Nedbank, Standard Bank and ABSA). The study found that the skills shortage is indeed a problem. The study found that reasons including the legacy left by apartheid and students pursuing the wrong degrees were highlighted as some of the reason for this skills shortage. An opportunity for the NMMU Business School was identified to support the banking industry in addressing credit risk management skills shortage. The benefits include financial reward and more importantly an opportunity to differentiate the Business School and the courses offered at the school from the rest. Some of the recommendations included sourcing of the best practices from institutions like the Millpark Business School on effective partnering with the banking industry as well as a proactive approach to be adopted by the banking industry in terms of lobbying support from other potential role players for example but not limited to, student bodies, BankSeta and the smaller banks in the industry.

Organisations or individuals that lend money (banks and micro lenders) or that sell goods on credit (retailers) are classified as credit providers. The debtor enters into a contractual agreement with a credit provider, or creditor, with the obligation to repay the loan amount, fees and interest according to a predetermined schedule. The contractual agreement, also known as a credit agreement, is as a general rule very complex. Legislation protecting debtors in various ways is an international phenomenon. In South Africa, the National Credit Act, Act 34 of 2005 (NCA) was enacted in 2005. The NCA changed the playing field for credit providers participating in the South African consumer credit market to a great extent. Consumer lending is the sleeping giant of the financial sector. The key to successfully unlock this enormous market is the credit provider's ability to accurately assess the creditworthiness of a potential customer during the customer acquisition phase. The creditworthiness of the customer is related to the risk of default, i.e. a debtor's non-payment of debt in terms of the credit agreement. The risk of default is also known as credit risk. Real People Investment Holdings (Pty) Ltd (RPIH) classifies credit risk as the single largest risk the Group is exposed to. They recognise that the intelligent and responsible management of credit risk makes it the Group's largest profit driver. Credit risk scorecards are excellent decision aids during the customer acquisition phase. The characteristics and behaviour of merchants submitting credit applications to RPIH for assessment have a definite impact on the credit risk of the Group. The merchant plays a pivotal role in the debtor-creditor-supplier business model. The merchant influences the customer's sales experience and subsequent level of satisfaction with the transaction. A satisfied customer constitutes a lower level of credit risk for the creditor, in this case RPIH. The research is conducted with a positivistic paradigm. The cross-sectional study approach is used. The merchant is the unit of analysis. A sample of 77 merchants is selected from the population of 244 merchants who submitted credit applications to RPIH during the observation period. Questionnaires are used as the data collection method in this research project. The predictive ability of fourteen merchant related characteristics are demonstrated through this empirical study.

This thesis discusses three topics in the area of quantitative finance in relation to risk and credit portfolio management. Chapter 2 investigates the issue of estimating and testing the goodness-of-fit of a model for a dependence break. The dependence is modelled by copulas and an unknown break of dependence structure is allowed for by including a dummy variable in the copula. The model is selected by minimizing the Akaike Information Criterion (AIC) of each candidate breaking point. The candidate models are estimated by a well-established two-step Maximum Likelihood (ML) approach, namely "Inference Function for Margin" (IFM). Moreover, we examine 5 single-factor copulas and compare them to each other by AIC criteria. A parametric bootstrap goodness-of-fit test is also proposed. Empirically, the dependence structures of stock indices between the US-UK and US-Japan markets during the Subprime crisis are examined. We found breaks in both dependence structures. In Chapter 3, a new general approach is developed for optimizing a credit portfolio by minimizing the default risk of a whole credit portfolio subject to a certain target premium. The approach is rooted in concepts from Modem Portfolio Theory. The default risk is measured by a quadratic form of weights and a matrix containing information about default correlations between any two single-names and default intensities of each single-name. The default correlation and the default intensities are modelled by a new binomial intensity model. A Genetic Algorithm (GA) approach is also introduced to optimize a credit portfolio with the purpose of overcoming limitations of the analytical method and the traditional numerical method based on the first order condition. Empirically, the approach is applied to optimize Credit Default Swap (CDS) portfolios consisting of members of iTraxx and CDX indices. In Chapter 4, we focus on modelling counterparty risks of two important financial instruments: the Interest Rate Swap (IRS) and the CDS. Analytical solutions are derived for the theoretical fair prices of the IRS and the CDS under various assumptions of defaults of counterparties. Also a Monte Carlo approach is proposed as a numerical solution for the fair prices. Numerical experiments are designed to study the effects of various factors on the fair price. Empirically, we examine the counterparty risk of a CDS portfolio, composed of randomly selected single-names from iTraxx series 10.

In recent years, most developed countries have suffered a severe recession due to a financial crisis starting in the US with mortgages loans. The lack of credit risk management has been pointed out as one of the causes of this bank panics. To avoid a similar situation, the credit card companies need to have proper risk management tools. This thesis presents a credit scoring system which aims at setting credit lines and thus, controlling credit risk. It includes three types of models: application scorecards, early detection scorecards and behavioral scorecards. They have been built on real and recent data coming from a German credit card company. The models have been built with a training sample and validated accordingly, using logistic regression. Information value and validation charts have been used for comparing the models. In the scoring process described, the scorecards are used in a sequential order. The author shows that minimizing losses might not be optimal in order to maximize profit. Finally, the author presents possible extensions to the research. The author hopes that the microeconomic analysis of the mechanics of a particular lender’s credit allocation process described in this thesis can play some part in preventing future financial crisis.

This study aims to investigate the effectiveness of using asset securitisation as risk management technique in banks. This study examines the direct impacts of asset securitisation on the riskiness of banks’ loan portfolios as well as the indirect impacts on the subsequent financial stability. This study also tests the changes in banks’ equity capital and liquidity as a result of using asset securitisation in order to understand their potential contributions to the examined bank risk behaviour. Furthermore, this study tests the impacts of adopting the Basel capital requirements on banks’ exposure to asset securitisation and the related bank risk behaviour. The study is informed by stakeholder theory. The use of stakeholder theory in the current study helps in addressing the causal connections between banks’ risk management practices and the achievement of banks’ performance objectives. Using stakeholder theory also helps understand the role of external regulatory structures in supporting risk management practices in banks. The empirical study is conducted by using a sample of 44 bank holding companies selected from 13 European countries during the period 2004-2014. The choice of the sample banks is based on the availability of securitisation data as well as the condition that all European banks should have placed at least one securitisation transaction during the period of the study. Moreover, seven linear regression models were developed to examine the study relationships and were estimated by using Fixed Effects panel data analysis. The use of panel data analysis in this study aims to capture the dynamics of bank risk behaviour and other bank-specific conditions that are associated with asset securitisation during the period of the study. The results found in the empirical analysis confirm that incorporating the use of asset securitisation with higher capital requirements is more likely to reduce originators’ credit risk-taking that arise from their lending activities. The findings reported in this study, however, do not support the regulatory capital arbitrage hypothesis of the securitisation products. Furthermore, this study confirms that European securitising banks continued to view asset securitisation as cost-efficient funding source, despite the decreasing number of transactions since the crisis. The findings in this study also show that European securitising banks did not effectively operate their securitisation proceeds in profitable investments during the period of the study. Based on the results found in the current study, we can suggest that introducing more risk-sensitive capital requirements is a key factor in the future development of the asset securitisation markets. This study contributes to the existing literature by emphasising the direct connections between asset securitisation and the riskiness of banks’ loan portfolios. This study also is one of the first studies to test asset securitisation effects on the absolute level of bank capital in order to provide a better understanding of the regulatory capital arbitrage hypothesis. The current study further extends the existing literature to test the role of the Basel capital requirements in controlling the use of asset securitisation in banks, taking into account the former regulatory frameworks and the full implementation years of the Basel (II) framework. Unlike previous studies, the employment of stakeholder theory in the current study has helped in expanding the perception of risk management in banks, from purely controlling device to a broad approach that aims to support bank’s existence and prosperity. Furthermore, this study is one of the first studies that had a broader look at the European securitisation market, during the years before and after the crisis and compared the empirical results of both sub-samples to validate the robustness of the study findings in terms of the financial crisis.

Kredito rizikos ir valdymo magistro baigiamojo darbo tema yra aktuali, todėl kad bankai – vieni iš svarbiausių ekonominės veiklos dalyvių, kadangi jie kaupia lėšas priimdami indėlius ir skolina pinigus juridiniams ir fiziniams asmenims. Bankų pajamas užtikrina paskolos, kituose bankuose laikomi indėliai, vertybiniai popieriai ir kitas turtas. Paskolos sudaro apie 60 % bankų turto, todėl pati reikšmingiausia komerciniams bankams yra kreditinė rizika, nes paskolų portfelis paprastai sudaro pačią didžiausią banko aktyvų dalį. Kredito rizika reiškia, kad klientas neįvykdys savo įsipareigojimų bankui. Todėl ir kredito rizikos valdymo aktualumas yra akivaizdus. Deja, Lietuvos mokslinėje literatūroje kreditinei rizikai skiriama santykinai mažai dėmesio. Daugelis autorių aptaria arba visas rizikos rūšis, jų valdymą, tačiau nepakankamai išsamiai, arba didesnį dėmesį skiria kitoms rizikos rūšims, tačiau ne kredito rizikai. Todėl neabejotina tiek šio darbo praktinė nauda, tiek ir naujumas. Šio darbo objektas – kreditinė rizika Lietuvos komerciniuose bankuose. Pagrindinis šio darbo tikslas - išanalizuoti kreditinės rizikos valdymo praktiką, problemas ir pateikti siūlymus toms problemoms spręsti. Darbo hipotezė – kreditinės rizikos valdymas nėra pakankamai išvystytas Lietuvos komerciniuose bankuose. Siekiant keliamo tikslo buvo sprendžiami tokie uždaviniai: apibūdinti kreditinę riziką ir jos rūšis, apibrėžti, kokiomis priemonėmis valdoma rizika ir apžvelgti kredito rizikos valdymo sistemą... [toliau žr. visą tekstą]
The theme of the master’s thesis of credit risk is actuals because banks are one of the most important participants in the economical activities, because they accumulate funds accepting deposits and borrow money to legal and natural people. The income of banks is assured by loans, deposits, kept at the other banks, securities and other assets. Loans form approximately 60 % of the bank assets, therefore credit risk is mostly important for commercial banks, because loans portfolio form the biggest part of the banks actives. Credit risk means that a client will not fulfil his obligations to the bank. Therefore, the actuality of credit risk management is evident. However, relatively low attention is aid at the credit risk in Lithuanian non-fiction literature. Many authors discuss either all the types of risk, their management, however in insufficient detail, o a higher attention is paid at the other types of risk, but not the credit risk. Therefore, this thesis is valuable not only because of its practice, but also by its originality. Therefore, practical use as well as originality of this thesis is obvious. The subject of this thesis is credit risk at Lithuanian commercial banks. The key objective of this thesis is to analyze credit risk management practice, problems and provide with the offers to solve these problems. The hypothesis of this thesis is that credit risk management has not been developed sufficiently at Lithuanian commercial banks. To seek a raised aim the... [to full text]

Гендич, І. С. Управління кредитним ризиком комерційних банків України : магістерська робота : 072 Фінанси, банківська справа та страхування / І. С. Гендич ; керівник роботи Ніколаєнко Ю. В. ; Національний університет «Чернігівська політехніка», кафедра фінансів, банківської справи та страхування. – Чернігів, 2020. – 95 с.
Предметом дослідження кваліфікаційної роботи є сукупність теоретичних, методичних, організаційних та практичних аспектів управління кредитним ризиком комерційного банку. Об’єктом дослідження є процес управління кредитним ризиком в АТ КБ «ПРИВАТБАНК». Мета кваліфікаційної роботи полягає у формуванні та обгрунтуванні нових теоретичних поглядів щодо управління кредитним ризиком комерційних банків України. За результатами дослідження сформульовані пропозиції щодо вдосконалення управління кредитним ризиком в комерційних банках України. Одержані результати можуть бути використані для удосконалення управління кредитними ризиками комерційним банком в сучасних економічних умовах.
The subject of research qualification work is a set of theoretical, methodological, organizational and practical aspects of credit risk management of a commercial bank. The object of the study is the process of credit risk management in JSC CB "PRIVATBANK". The purpose of the qualification work is to form and substantiate new theoretical views on credit risk management of commercial banks of Ukraine. According to the results of the study, proposals for improving credit risk management in commercial banks of Ukraine have been formulated. The obtained results can be used to improve credit risk management by a commercial bank in modern economic conditions.

Автором розглянуті передумови та можливості використання окремих інструментів управління кредитним ризиком в банку.
The author reviewed the prerequisites and possibilities of the use of certain instruments of credit risk management in bank.
State Institution of Higher Education “National Mining University"

碩士
國立中興大學
高階經理人碩士在職專班
100
This paper examines the effect of the idiosyncratic risk of a firm on its credit risk and the relationship between the credit risk and accrual or real earnings management under considering idiosyncratic risk. We further investigate the effects of composition of real earnings management on the credit risk of a firm. In sensitivity analysis, we examine the effects of the credit risk and the idiosyncratic risk of a firm on managerial behavior of income smoothing. The findings indicate that net cash flows of external financing, debt financing and equity financing activities are negatively related to the credit risk of a firm whether the idiosyncratic risk are measured by market model or Fama-French three factors model, implying that the higher financed funds, the higher credit rating of a firm is. It is because the firm has more investment opportunities and is in growth stage. The idiosyncratic risk of a firm is positively related to the credit risk. Next, both accrual and real earnings managements are positively related the credit risk under considering the idiosyncratic risk, and the idiosyncratic risk is also positively related to credit risk. As for the compositions of real earnings management, abnormal cash flows are negatively related to the credit risk but both abnormal production cost and abnormal discretionary expense are unrelated to credit risk. In this case, we also find the idiosyncratic risk of a firm is positively correlated with the credit risk. Moreover, in sensitivity analysis, the evidence indicates that the idiosyncratic risk is negatively related to earnings smoothing and the firm with higher credit risk prefers to income smoothing strategy.

碩士
國立臺灣大學
商學研究所
86
The main configuration of this research is to use a portfolio credit risk approach of CreditMetrics quantifies credit risks that arise due to increa sed exposure to an obligor or a group of correlated obligors. In this research, the credit risk includes not just default or insol vency risk but also changes in credit spreads and thereby market values, cha nges in credit ratings, and generic changes in credit quality. And foll ow this concept, this research has some issues as follows： 1、Use certain model to measure credit risk of any bonds、lo ans、receivables and derivatives whose value exposures are affected by the credit risk. 2、Use Black & Scholes and Vasicek model to extend the a sset pricing under the credit risk, which includes bonds pricing、loans pric ing、receivables pricing and derivatives pricing. 3、Introduce how to use this credit risk model to help obligees or investors to manage their assets'' credit exposures. 4、Introduce credit derivatives and develop new credit derivatives to help obligees or investors to hedge、 diversify a and gain access to credit exposures. 5、Use the result of the credit risk model and asset pricing to develop the pricing model of credit derivatives.

Counterparty credit risk (CCR) is the risk that a counterparty in a deal will not be able to meet their contractual obligations in the future. While CCR is an important task for any risk desk, it has often been underestimated due to the miss-conception that some counterparties were deemed to be either too big to fail or too big to be allowed to default. This was highlighted by the 2008 nancial crisis that saw respected banks, such as Lehman Brothers, and nancial service providers, such as AIG, default on their obligations. Since then there has been renewed interest in CCR, with the focus being on actively pricing and hedging it. In this work CCR is invistigated including its intersection with other forms of risk. CCR mitigation techniques are explored, followed by the formal quanti cation of CCR in the form of credit value adjustments (CVA). The analysis of CCR is then applied to interest rate derivatives, more speci cally forward rate agreements (FRAs) and interest rate swaps (IRSs). The e ect of correlation on unilateral and bilateral CVA between counterparties, including risk factors such as the interest rate, is investigated. This is invistigated under two credit risk modelling frameworks, the structural and intensity based frameworks. It is shown that correlation has a none-negligible e ect on both unilateral and bilateral CVA for FRAs and IRSs. Correlation structures, namely the Gaussian and the Student-t copula, are used to induce dependency in order to understand their e ect on both unilateral and bilateral CVA. It is shown that the choice of copula does not have signi cant e ect on either unilateral or bilateral CVA.