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

Made available in DSpace on 2016-04-25T16:44:21Z (GMT). No. of bitstreams: 1 Jose Renato de Paula Lamberti.pdf: 1340923 bytes, checksum: 9600626bdbb142fe874e7ba242bc30b1 (MD5) Previous issue date: 2011-05-19 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Evaluating the risk of default by a company became an indispensable object when making the decision to grant to the financial credit institutions. Taking in account the current record of credit growth, the strategic role of risk management for financial institutions, the constant innovations in the process of detection risk and the large volume of academic researches related to management models for credit risk, because of this, it was considered the appropriate occasion for the development of this dissertation. The present work aims to confront the traditional analysis of risk based on accounting ratios and the results of KMV model to determine the probability of default of publicly traded companies. The dissertation will address the theoretical review of the traditional analysis of credit risk and the model of KWN (based on the theory of Black and Scholes 1973 and Merton, 1974). The next step will apply the accounting ratios and the KMV model, calculating the probability of default of the building sample. Therefore, we will discuss the limitations of the KMV model and some recommendations in order to improve management of credit / Avaliar o risco de inadimplência de uma empresa tornou-se objeto indispensável para a tomada de decisão de concessão de crédito para as instituições financeiras. O atual histórico de crescimento do crédito, o papel estratégico do gerenciamento do risco para as instituições financeiras, as constantes inovações nos procedimentos de detecção de risco e o grande volume de pesquisas acadêmicas abordando modelos de gestão dos riscos de crédito, considerou-se oportuna a ocasião para o desenvolvimento desta dissertação. O presente trabalho tem como objetivo confrontar a análise tradicional de risco baseada em índices contábeis e o resultado do modelo KMV para determinar a probabilidade de default das empresas de capital aberto. A dissertação abordará a revisão teórica da análise tradicional do risco de crédito e em seguida, a modelagem do KMV ( baseada na teoria de Black e Scholes, 1973 e Merton, 1974). O próximo passo aplicará os índices contábeis e a modelagem do KMV, calculando a probabilidade de default das empresas da amostra. Por conseguinte, serão discutidas as limitações do modelo KMV e algumas recomendações com o intuito de aprimorar o gerenciamento do crédito

Made available in DSpace on 2016-03-15T19:26:13Z (GMT). No. of bitstreams: 1 Rodrigo Vogliotti.pdf: 868389 bytes, checksum: e1b90368c40778893d720b9e7a0fe869 (MD5) Previous issue date: 2012-08-17 / The difficulty in developing a statistical model that includes random variables and the need for intensive data processing capacity are the main challenges for the measurement of counterparty credit risk. The need to know the exposure value at the time of default (EAD) on a derivative instrument is a decisive factor for pricing, portfolio management and capital allocation. Recent events such as the creation of innovative products, coming from the new Basel Accord (Basel II) and the credit crisis of 2007/08 reinforce the importance of knowing what the actual credit risk exposure in a particular transaction. The aim of this study was to develop models for measuring credit risk of the counterparty from the estimation of counterparty exposure to bonds, equities and forward contract through the use of Monte Carlo simulation. The results of the sensitivity analysis indicate that certain parameters such as the interest rate, the mean and standard deviation show strong linear correlation with exposure (EAD) and this issue can be an important driver for the decision-making process. In the model of forward contract was found that correlated random variables can potentiate the exposure value. / A dificuldade em desenvolver um modelo estatístico que contemple variáveis aleatórias e a necessidade de intensa capacidade para processamento de dados são os principais desafios para a mensuração do risco de crédito de contraparte. A necessidade em conhecer o valor da exposição no momento do default (EAD) em um instrumento derivativo é fator decisivo para a precificação, gestão do portfólio e alocação de capital. Recentes acontecimentos como a criação de produtos inovadores, o advindo do novo acordo de Basileia (Basileia II) e a crise de crédito de 2007/08 reforçaram a importância de se saber qual o risco de crédito efetivo que cada contraparte está exposta em uma determinada transação. O objetivo deste estudo foi desenvolver modelos para mensuração do risco de crédito da contraparte a partir da estimação da exposição da contraparte para títulos, ações e contrato a termo de ações através da utilização da simulação de Monte Carlo. Os resultados da análise de sensibilidade indicam que certos parâmetros como a taxa de juro, a média e o desvio padrão apresentam forte correlação linear com a exposição (EAD) calculada e podem ser importantes direcionadores para o processo decisório. No modelo de contrato a termo de ações foi verificado que variáveis aleatórias correlacionadas potencializam o valor da exposição.

過去數年來，隨著金融市場的逐漸開放，帶動了整個金融市場的蓬勃發展。不過，隨之而來的風險，也產生了不少的金融災難。而這些的金融災難，大多是由於金融商品投資避險上的操作不當，或者是風險控管失衡所造成的。為了避免如是情況再度發生，近年來，國際間相繼有許多學者投入風險管理的研究。 而在所有的產業之中，風險管理對於銀行業來說，更為重要。銀行作為產業的金融媒介，一旦發生金融災難，不只產業會受到衝擊，連帶地資金來源的存款戶也受害，影響層面極為廣大。所以，各國政府莫不對銀行業設有相當嚴格的管理規定，以健全整體金融環境的發展。例如，有名的巴塞爾資本協定，即為國際間對於金融環境的風險管理規範。 但是，隨著時空環境的變遷，原有的協定早以不敷需求。終於，在2004年中，巴塞爾銀行監理委員會公佈了定版的新巴塞爾資本協定，並決定於2006年底開始實施。新協定在原有資本準備方面，將作業風險納入風險評估的範圍，並大幅修訂信用風險的衡量方式，允許銀行使用自行開發的內部模型，並採認降低信用風險的工具。而且，更增加了監理審查程序及市場紀律的相關規定，期待以多方面的角度，強化國際金融體系。 本研究將由新巴塞爾資本協定談起，簡介新協定的相關內容，比較新舊協定不同之處，然後針對銀行主要面臨的信用風險部分，探討在新協定所允許使用的信用風險內部模型，以及信用風險抵減技術。分別介紹目前業界常見的四種信用風險模型：專業信用分析公司KMV的KMV模型、CSFP的CreditRisk+模型、J.P. Morgan的CreditMetrics™模型、McKinsey的CreditPortfolioView模型，以及信用衍生性商品與信用風險證券化概念，最後探討未來風險管理發展的可能方向。

Acteurs majeurs du tissu économique, les petites et moyennes entreprises (PME) font l’objet d’une attention croissante de la part des économistes depuis plusieurs années. Pour financer leur développement, ces entreprises privilégient le recours à l’endettement bancaire. Or ce mode de financement est générateur d'un risque de crédit, principalement lié à la probabilité de défaillance de l’entreprise. La question de l’évaluation du risque de crédit des entreprises est généralement abordée de manière indifférenciée. Cependant il est possible que les PME internationalisées présentent des caractéristiques spécifiques relatives à leur ouverture sur les marchés internationaux. Nous procédons à une analyse des déterminants du risque de défaillance des PME d’une part, et d'autre part, des risques auxquels elles sont confrontées dans leur processus d’internationalisation. A travers des estimations économétriques sur données de panel, nous montrons notamment que l’augmentation de l’intensité des exportations des PME françaises réduit leur probabilité de défaillance. Une PME internationalisée présente alors un risque de crédit moins important qu'une PME purement domestique. Nous montrons ensuite que cette information doit être intégrée dans la modélisation du risque de crédit, réalisée sur la base de variables financières. Celle-ci est plus performante (dans le sens d'un meilleur pouvoir prédictif)lorsque l’on estime la probabilité de défaillance à l’aide de modèles distincts pour les PME internationalisées et les PME domestiques. Selon ces résultats, l'internationalisation est un facteur important qui devrait être considéré dans la recherche future sur le risque de crédit des PME. / Small and medium-sized enterprises (SMEs) dominate the French business environment making a significant contribution to the national economy. Unsurprisingly, an extensive set of empirical studies explores critical issues that affect SMEs including factors that can reduce the credit risk associated with bank debt. Despite that internationalisation has a number of key characteristics that can influence credit risk, the nexus between internationalisation and credit risks remains underexplored. This thesis aims to address this knowledge gap by examining this nexus for a panel of French SMEs. To do so, the thesis estimates the effect of export intensity of French SMEs on their default probability. Key findings illustrate that internationalisation plays a critical role in decreasing the credit risk. Motivated by these results, the thesis assesses the relationship between internationalisation and modelling credit risk through evaluating the effect of several financial variables on default probability of domestic and international SMEs, separately. Interestingly, the findings reveal that modelling the credit risk of SMEs could be improved by considering domestic and international SMEs separately. According to these findings, internationalisation is one of the most important factors that should be considered in future research in relation to SMEs.

This thesis aims to investigate different machine learning (ML) models and their performance to find the best performing model to predict credit risk at a specific company. Since granting credit to corporate customers is a part of this company's core business, managing the credit risk is of high importance. The company has of today only one credit risk measurement, which is obtained through an external company, and the goal is to find a model that outperforms this measurement.     The study consists of two ML models, Logistic Regression (LR) and eXtreme Gradient Boosting. This thesis proves that both methods perform better than the external risk measurement and the LR method achieves the overall best performance. One of the most important analyses done in this thesis was handling the dataset and finding the best-suited combination of features that the ML models should use.

Creditworthiness is a quality that is important to all stakeholders of an organisation, especially bondholders. It is posited that good corporate governance practices assist the confidence that stakeholders have in an organization’s ability to generate the strong cash flows that are needed to meet financial obligations, which in turn should enhance credit risk assessments. Much research has been conducted into rating assessments, but these have largely been directed at developed markets and they have not generally been focused on the impact of good corporate governance practices and procedures. The primary focus of this research is to address this issue through an investigation into the impact of key factors upon the credit risk assessments of listed companies on the Amman Stock Exchange (ASE) in Jordan, as assessed by World'vest Base Inc. (WVB) credit risk assessment scores for Jordanian companies between 2005 and 2007 inclusively. Drawing upon insights from agency (including management disciplining and wealth redistribution hypotheses), stewardship, stakeholder, signalling, legitimacy and the diffusion of innovation theories, this thesis investigates the determinants of WVB credit risk assessments of Jordanian firms under five headings: accounting and financial aspects, market and regulatory perspectives, influence of ownership structure, financial transparency/disclosure and corporate governance factors. To achieve this, an array of modelling techniques is used in order to provide a more comprehensive picture. They include bivariate analysis, one-way analysis of variance, ordinary least square regressions for numerical scores, binary logistic regressions, and ordinal logistic regression. The results demonstrate that accounting and financial factors have a significant impact on credit risk assessments but not capital intensity. Profitability is positively associated with credit risk assessments, while leverage and loss propensity have a negative association. With respect to market and regulatory factors, size and Tobin’s Q are positively associated with credit risk assessments. By contrast type of sector and audit are not related to credit risk assessments. Foreign ownership enhances ratings, whilst institutional ownership has a negative impact. Also, insider ownership and family ownership have some importance. It was surprising to find that whilst financial transparency and disclosure variables are significantly associated positively with credit risk assessments in some models, they were generally not significant across other models. Nevertheless, the study finds empirical evidence to support a degree of association between credit risk assessments and corporate governance factors. There is also a positive association between board size and credit risk assessments, but the most important aspect of corporate governance for Jordanian firms is board expertise. The originality of this thesis also embraces the inclusion not only of externally published WVB risk assessments in the Jordanian context, but also internal numerical ratings that were made available with kind permission from the WVB agency for the purposes of this research. The question is whether there are insights that can be gained from such internal ratings that have not hitherto been made available to other researchers. The answer is in the affirmative, for role duality on the board of directors is evidently more important to WVB’s own internal numerical rating assessments than is evidenced by the WVB externally published credit risk assessments. Specifically, the significance of corporate governance (role duality) is missed by multivariate models that are based solely on externally published data. Furthermore, financial transparency and disclosure variables reveal more (albeit moderate) support for the more refined internal scores of WVB than for the external assessment ratings. Finally, family ownership is also important to WVB’s internal scores. Thus, this research has enabled deeper insights to be gained into credit risk assessment determinants within the Jordanian context.

The recent U.S. subprime mortgage crisis rapidly spread throughout the world and put the global financial system under extraordinary pressure. The main implication of the recent crisis is that complex banking regulations failed to adequately identify and limit riskiness of banking systems at both domestic and international levels. In spite of a large empirical literature on the causes and remedies of the recent crisis, there remains substantial uncertainty on (i) how risk measuring models performed during crisis, (ii) how systematic factors such as house prices affected the financial system, and (iii) how effectively government policy responses resolved the financial crisis. This thesis seeks to narrow this gap in the literature by offering three empirical essays. The first essay investigates the performance of alternative parametric VaR models in forecasting riskiness of international equity portfolios. Notably, alternative univariate VaR models are compared to multivariate conditional volatility models with special focus given to conditional correlation models. Conditional correlation models include the constant conditional correlation (CCC), dynamic conditional correlation (DCC), and asymmetric DCC (ADCC) models. Various criteria are then applied for backtesting VaR models and to evaluate their one-day-ahead forecasting ability in a wide range of countries and during different global financial conditions. It is found that most VaR models have satisfactory performance with small number of violations during pre-crisis period. However, the number of violations, mean deviation of violations, and maximum deviation of violations dramatically increase during crisis period. Furthermore, portfolio models incur lower number of violations compared to univariate models while DCC and ADCC models perform better than CCC models during crisis period. From risk management perspective, most single index models fail to pass Basel criteria for internal VaR models during crisis period, whereas empirical evidence on the choice between CCC, DCC, and ADCC models is mixed. The recent crisis also raised serious concerns about factors that can systematically destabilise the whole banking system. In particular, the collapse of house prices in the United States triggered the recent subprime mortgage crisis, which was associated with a sharp increase in the number of nonperforming loans and bank failures. This in turn demonstrates the key role that house prices play in systematically undermining the whole banking system. The second essay investigates the determinants of nonperforming loans (NPL) with a special focus on house price fluctuations as a key systematic factor. Using a panel of U.S. banking institutions from 1999 to 2012, the analysis is carried out across different loan categories, different types of banks, and different bank size. It is found that house price fluctuations have a significant impact on the evolution of nonperforming loans, while the magnitude of their impact varies across loan categories, institution types, and between large and small banks. Also, the impact of house price fluctuations on nonperforming loans is more pronounced during crisis period. The last essay of this thesis investigates the effectiveness of the U.S. government strategy to combat the crisis. As a comprehensive response to the recent financial crisis, the US government created the Troubled Asset Relief Program (TARP). The Capital Purchase Program (CPP) was launched as an initial program under the TARP. The CPP was designed to purchase preferred stocks or equity warrants from viable financial institutions. Using a large panel of the U.S. commercial banks over the period 2007Q1 to 2012Q4, survival analysis is used to investigate the impact of TARP funds on the likelihood of survival in the recipient banks. It is found that larger recipient banks are more likely to avoid regulatory closure, while receiving capital assistance does not effectively help banks to avoid technical failure. This implies that governmental capital assistance serves larger banks much better than their smaller counterparts. In addition, TARP recipients are more likely to be acquired, regardless of their size and financial health. In summary, the empirical findings reveal that capital infusions do not enhance the survival likelihood of the recipient banking institutions.

Credit models are essential to control credit risk and accurately predicting bankruptcy and financial distress is even more necessary after the recent global financial crisis. Although accounting and financial information have been the main variables in corporate credit models for decades, academics continue searching for new attributes to model the probability of default. This thesis investigates the use of corporate efficiency and corporate governance measures in standard statistical credit models using cross-sectional and hazard models. Relative efficiency as calculated by Data Envelopment Analysis (DEA) can be used in prediction but most previous literature that has used such variables has failed to follow the assumptions of Variable Returns to Scale and sample homogeneity and hence the efficiency may not be correctly measured. This research has built industry specific models to successfully incorporate DEA efficiency scores for different industries and it is the first to decompose overall Technical Efficiency into Pure Technical Efficiency and Scale Efficiency in the context of modelling financial distress. It has been found that efficiency measures can improve the predictive accuracy and Scale Efficiency is a more important measure of efficiency than others. Furthermore, as no literature has attempted a panel analysis of DEA scores to predict distress, this research has extended the cross sectional analysis to a survival analysis by using Malmquist DEA and discrete hazard models. Results show that dynamic efficiency scores calculated with reference to the global efficiency frontier have the best discriminant power to classify distressed and non-distressed companies. Four groups of corporate governance measures, board composition, ownership structure, management compensation and director and manager characteristics, are incorporated in the hazard models to predict financial distress. It has been found that state control, institutional ownership, salaries to independent directors, the Chair’s age, the CEO’s education, the work location of independent directors and the concurrent position of the CEO have significant associations with the risk of financial distress. The best predictive accuracy is made from the model of governance measures, financial ratios and macroeconomic variables. Policy implications are advised to the regulatory commission.

Thesis (MDF)--Stellenbosch University, 2012. / The study was carried out to find out the effect of credit risk and portfolio loan management on profitability of microfinance Banks (MFBs) in Lagos, Nigeria. To achieve the objective of the study, an econometric model was developed. A sample size of 14 microfinance banks was randomly selected, comprising four national, five state and five unit microfinance banks respectively. Five year annual financial statements of these 14 selected microfinance banks were obtained for this analysis using panel data that produce 70 observations for the period 2006 to 2010 The result reveals that the current value of all independent variables follow an expected relationship with the profitability of microfinance banks. That is, the net interest margin, asset mix proxied by ratio of loan to total asset, and ratio of equity to total assets have a positive relationship with the profitability of microfinance banks (MFBs) in Lagos state, Nigeria. Asset quality (ratio of non-performing loan to total loan) and the interest earnings to total assets ratio have a negative relationship with profitability of microfinance banks. However, the result reveals that of the five immediate past value of these independent variables, only net interest margin and interest earnings to total assets ratio maintained expected relationship with the performance (profitability) of microfinance banks. From the hypothesis test, it was found that credit risk management has a significant effect on the profitability of microfinance banks in Lagos state, Nigeria The study is set against the background and realisation that many MFBs in Lagos seem to continue to seek growth and profit without much attention to addressing credit risk issues – a necessity for their survival on a sustainable basis. The results indicated that the credit evaluation process was positively and significantly related to the quality of the loan portfolio in MFBs. The study also found out that internal rather than external to the MFB’s are more likely to provide the main explanation for MFBs’ profitability. To enhance their profitability, loan products which seem to have various defects which make loans even more risky need to be reviewed. The defects include: long loan processing procedures, absence of training to clients on proper utilisation of loans, lack of mechanisms to assess the suitability and viability of the business proposal for which loans were applied, inappropriate mechanism for assessing character for loan applicants, absence of moratorium periods between taking of a loan and repayment of a first instalment as clients were requested to repay their first instalment within the first month. The study recommended that MFBs should have a broad outlook in its credit risk and portfolio management strategy and this calls for radical reforms within the MFB’s operations and policies as well as more aggressive approaches most especially before availing credit and in its loan recovery as it had a direct impact on profitability.