This dissertation includes three essays, which investigate contingent claims pricing subject to credit risk based on the structural approach and analyze associated issues of corporate finance. The first essay develops and examines a partial equilibrium model to investigate the effects of macroeconomic condition and firm-level productivity shocks on the determination of optimal debt ratio. The model extends the contingent-claims models of the firm's capital structure by incorporating both the industry demand and firm-level supply factors into the firm's earnings and unlevered asset value. Our model predicts that the optimal debt ratio is negatively correlated to the macroeconomic conditions and the firm-level productivity. Furthermore, the theoretical implications are totally supported by the pooled feasible generalized least squares estimation with 311 Taiwanese listed manufacturing firms' quarterly data over the period from 1994 to 2003. The differences between the high-tech electronics and other manufacturing firms are also investigated, and particularly the high-tech firms are not tied up with the macroeconomic conditions while the others are. The second essay presents a contingent claim valuation of a callable convertible bond with the issuer's credit risk. The optimal call, voluntary conversion and bankruptcy strategies are jointly determined by shareholders and bondholders to maximize the equity value and the bond value, respectively. Our model not only incorporates tax benefits, bankruptcy costs, refunding costs and a call notice period, but also takes account of the issuer's debt size and structure. The numerical results show that the predicted optimal call policies are generally consistent with recent empirical findings; therefore calling convertible bonds too late or too early can be rational. The third essay provides a closed-form valuation formula for the Black-Scholes options subject to interest rate risk and credit risk. Not only does our model allow for the possible default of the option issuer prior to the option's maturity, but also considers the correlations among the option issuer's total asset, the underlying stock, and the default-free zero coupon bond. We further tailor-make a specific credit-linked option for hedging the default risk of the option issuer. The numerical results show that the default risk of the option issuer significantly reduces the option values, and the vulnerable option values may be remarkably overestimated in the case where the default can occur only at the maturity of the option.

Paskolų teikimas yra viena pagrindinių kredito unijų veiklos krypčių, o pagrindinė ir svarbiausia rizika su kuria jos susiduria yra būtent kredito rizika, todėl būtina ją detaliai analizuoti ir turėti patikimą šių aktyvų valdymo mechanizmą. Taip pat valdant, šią riziką, būtina atsižvelgti į socialinį kredito unijų aspektą. Darbo objektas – kredito rizikos valdymas. Darbo tikslas - išnagrinėti kredito unijų, kaip specifinių finansinių institucijų, veiklos ir kredito rizikos valdymo ypatumus. Pirmoje darbo dalyje nagrinėjami kredito unijų kredito rizikos valdymo ypatumai: analizuojama kooperatyvinių finansinių institucijų kreditavimo specifika bei kredito rizikos ypatybės būdingos kredito unijoms. Taip pat apžvelgiami kredito rizikos vertinimo principai ir pateikiami kredito rizikos valdymo metodai bei jų veikimo ypatybės. Antroje darbo dalyje analizuojama ir apibendrinama kredito rizikos valdymo kredito unijose praktika ir empiriniai tyrimai, apibūdinamos Lietuvos kredito unijų sektoriaus veiklos sąlygos ir formuojama kredito rizikos valdymo Lietuvos kredito unijose tyrimo metodika. Trečioje dalyje tiriami ir vertinami dviejų kredito unijų paskolų portfelio kokybės rodikliai, analizuojama paskolų portfelio koncentracija, remiantis atlikta analize formuojami efektyvūs Lietuvos kredito unijų investiciniai portfeliai, kaip kredito rizikos valdymo instrumentai. Pateikiami siūlymai geresniam kredito rizikos valdymo vystymui Lietuvos kredito unijose. Darbe prieita prie tokių... [toliau žr. visą tekstą] / Lending is one of the main credit union activities, and the most important risks which they face to is credit risk, it is necessary to analyze it in detail and have a sound mechanism of these asset management. As well as the management, this risk is necessary to consider the social aspect of the credit union. The work item - credit risk management. The aim - to examine the operational and credit risk management features of credit union as a specific financial institutions. The first part of the study looked at the credit union theoretical credit risk management features: characterized by cooperative financial institutions lending and credit risk perception of the concept of inherent credit unions. It also reviews the credit risk assessment principles and provides credit risk management techniques and performance characteristics. Secound segment of the paper characterizes situation in Lithuanian credit unions sector and describes researches based on credit risk management in credit unions practice. There also is given research method of credit risk management in Lithuanian credit unions. The work led to the following main conclusions. Credit union is a lending institution which distinguished higher degree of confidence and a wider access to financial services. Profitability is not as much emphasis to be exposed to liberal lending policies, fulfilling the mission and goals. Credit risk assessment before issuing a loan is the most important step in credit risk management process... [to full text]

Contingent claims models have recently been applied to the field of corporate insolvency prediction in an attempt to provide the art with a theoretical methodology that has been lacking in the past. Limited studies have been carried out in order to empirically compare the performance of these “market” models with that of their accounting number-based counterparts. This thesis contributes to the literature in several ways: The thesis traces the evolution of the art of corporate insolvency prediction from its inception through to the present day, combining key developments and methodologies into a single document of reference. I use receiver operating characteristic curves and tests of economic value to assess the efficacy of sixteen models, carefully selected to represent key moments in the evolution of the art, and tested upon, for the first time, post-IFRS UK data. The variability of model efficacy is also measured for the first time, using Monte Carlo simulation upon 10,000 randomly generated training and validation samples from a dataset consisting of over 12,000 firmyear observations. The results provide insights into the distribution of model accuracy as a result of sample selection, which is something which has not appeared in the literature prior to this study. I find overall that the efficacy of the models is generally less than that reported in the prior literature; but that the theoretically driven, market-based models outperform models which use accounting numbers; the latter showing a relatively larger efficacy distribution. Furthermore, I obtain the counter-intuitive finding that predictions based on a single ratio can be as efficient as those which are based on models which are far more complicated – in terms of variable variety and mathematical construction. Finally, I develop and test a naïve version of the down-and-out-call barrier option model for insolvency prediction and find that, despite its simple formulation, it performs favourably compared alongside other market-based models.

We assume pairs of random variables (X_i, Y_i), whereby the real variable X_i measures the creditworthiness of individual i with i = 1, . . . , n. The Bernoulli variable Y_i represents the default indicator of individual i. Our main target is a division of the creditworthiness into a given number of groups with a homogeneous default risk, i.e. to estimate rating classes. The framework of change point analysis provides a nonparametric method to estimate the breakpoints between the rating classes under quite weak assumptions. Up to now, the theory of breakpoint estimation is developed under the assumption of exactly one breakpoint. The contribution at hand, basically implements this theory, but extends it into a multi-stage heuristic. That means, we sequentially apply the theory for only one breakpoint as a multi-stage procedure. With this article we transfer the interesting theoretical issue of breakpoint estimation into an applicable form. Thereby, all the results are checked and obtained by simulation. The main results are as follows. Applying a sequential breakpoint estimation basically works and leads to outcomes of practical purpose. Thereby, the multistage heuristic reveals some weakness esp. in the case of quite huge differences between default probabilities that can be resolved by some interventions.

The thesis evaluates relationship between probability of default of non-financial corporations and households and evolution of macroeconomic environment. This work contributes to the literature of credit risk proving importance of macroeconomic variables in determining the PDs both on aggregate level and for sector of non-financial corporations and sector of households in the Czech Republic. Evaluation of an impact of the recent financial crisis on the PDs are done by employing latent factor model and FAVAR model on monthly data of non-performing loans and other macroeconomic variables covering the period 01/2002-06/2013. Finally, an ability to forecast and fit the data of FAVAR model and one factor latent model are compared. The comparison indicates that latent factor model should be more appropriate than FAVAR model.

The determination of lending (credit) risk is one of the most important fields of bank activities. This thesis discusses the IRB approach under Basel II. This approach includes the LGD, EAD and PD parameters. All parameters are individually modelled by the bank using regulator approved models. Parameter PD is the most focused one in this thesis. Theory for this parameter is of interest in many papers. However, at present the need for modelling of PD parameter over more years is appearing. Parameter LGD is also discussed in this thesis. The parameter EAD is only briefly presented. The thesis begins with the IRB approach, regression models and evaluation indicators, and then it focuses on the above parameters.

This diploma thesis consists of two parts. The first part deals with theory and practice of credit risk management in non-financial enterprises in the Czech Repulic. The other part focuses on collection agencies - it defines their services, describes legislation that regulates their business, state their advantages and disadvantages in comparison to other ways of collecting debts and provides an analysis of the debt collection agencies market in the Czech Republic based on a questionnaire survey.

The main goal of thesis is to give explanation of credit insurance process. Theoretical unit describes potential risks in the international and domestic trade and instruments by which these risks can be eliminated or reduced. The practical part is focus on the service of credit insurance as an effective tool to ensure against the risk. On the case study is calculated by using Net Present Value dependence on the size of discount rate, size of insured loss and date of insurance claim. In the final part of the thesis author take a think of what extent are credit insurance companies responsible for the deepening of economic recession.

[pt] Mensurar o risco de default justo para uma empresa sempre foi uma tarefa crucial para uma instituição financeira na hora de emprestar, principalmente, hoje em dia, com o aumento da competitividade e a redução dos spreads. Por outro lado, as empresas também precisam ser críticas e devem saber determinar o seu grau de risco com a mesma exatidão das instituições financeiras.Todos os agentes de mercado devem possuir as melhores ferramentas para mensurar o risco de crédito. Com esse intuito será apresentado nesta dissertação uma metodologia de análise de risco de crédito que está sendo muito discutida no momento. O foco será no modelo teórico de equilíbrio de Merton, 1974, que foi amplamente difundido pela KMV Corporation, que desenvolveu um modelo baseado nas premissas de Merton para fazer previsão de default. A dissertação começará com uma abordagem sobre o cenário que levou ao desenvolvimento de novos modelos para quantificar o risco de crédito. Em seguida, será feita uma revisão da modelagem KMV e da modelagem DLI (baseada na teoria de Merton, 1974). Após, será estimado o valor dos ativos a partir do valor do equity e calculada a probabilidade de default de empresas brasileiras, negociadas em bolsa, e que realmente entraram em default. Serão discutidas as vantagens e desvantagens apresentadas por estes dois modelos e as diferenças que existem entre a modelagem da KMV e a DLI. / [en] Measuring the fair default risk for a company, has always been a crucial task for a financial institution when it comes to granting loans, especially nowadays, with the rise in competitiveness and the reduction of the spreads. On the other hand, companies need to be analytical and must know how to determine their level of risk with the same accuracy as the financial institutions. Every market agent must possess the best tools to measure the credit risk, and with this purpose, the most discussed subject of the moment will be presented in this dissertation. The focus will be on the theoretical model of equilibrium by Merton, 1974, which was widely spread by KMV Corporation, who developed a model based on Merton`s premises in order to be able to predict default. The dissertation will start with an approach over the scenario that led to the development of new models to quantify the credit risk. Next, a review over the KMV model and the DLI model (based on Merton, 1974) will be done. After that, we will estimate the asset value starting from the equity value, and calculate the probability of default of Brazilian companies that are negotiated on the stock exchange, and who`ve really gone into default. We will discuss the advantages and disadvantages presented by these two models and the existing difference between the KMV and the DLI models.

Modelos de Credit Scoring são utilizados para estimar a probabilidade de um cliente proponente ao crédito se tornar inadimplente, em determinado período, baseadas em suas informações pessoais e financeiras. Neste trabalho, a técnica proposta em Credit Scoring é Redes Bayesianas (RB) e seus resultados foram comparados aos da Regressão Logística. As RB avaliadas foram as Bayesian Network Classifiers, conhecidas como Classificadores Bayesianos, com seguintes tipos de estrutura: Naive Bayes, Tree Augmented Naive Bayes (TAN) e General Bayesian Network (GBN). As estruturas das RB foram obtidas por Aprendizado de Estrutura a partir de uma base de dados real. Os desempenhos dos modelos foram avaliados e comparados através das taxas de acerto obtidas da Matriz de Confusão, da estatística Kolmogorov-Smirnov e coeficiente Gini. As amostras de desenvolvimento e de validação foram obtidas por Cross-Validation com 10 partições. A análise dos modelos ajustados mostrou que as RB e a Regressão Logística apresentaram desempenho similar, em relação a estatística Kolmogorov- Smirnov e ao coeficiente Gini. O Classificador TAN foi escolhido como o melhor modelo, pois apresentou o melhor desempenho nas previsões dos clientes maus pagadores e permitiu uma análise dos efeitos de interação entre variáveis. / Credit Scoring Models are used to estimate the insolvency probability of a customer, in a period, based on their personal and financial information. In this text, the proposed model for Credit Scoring is Bayesian Networks (BN) and its results were compared to Logistic Regression. The BN evaluated were the Bayesian Networks Classifiers, with structures of type: Naive Bayes, Tree Augmented Naive Bayes (TAN) and General Bayesian Network (GBN). The RB structures were developed using a Structure Learning technique from a real database. The models performance were evaluated and compared through the hit rates observed in Confusion Matrix, Kolmogorov-Smirnov statistic and Gini coefficient. The development and validation samples were obtained using a Cross-Validation criteria with 10-fold. The analysis showed that the fitted BN models have the same performance as the Logistic Regression Models, evaluating the Kolmogorov-Smirnov statistic and Gini coefficient. The TAN Classifier was selected as the best BN model, because it performed better in prediction of bad customers and allowed an interaction effects analysis between variables.