Dissertations / Theses on the topic 'Credit risk'

We first present a Complex Singular Value Decomposition (CSVD)analysis of credit cyle and explore the lead-lag relation betweencredit cycle and business cycle, then propose a GeneralizedLinear Model (GLM) of credit rating transition probabilitiesunder the impact of business conditions.To detect the cyclic trend existence of credit condition in U.S.economy, all credit variables and business variables aretransformed to complex values and the transformed data matrix isapproximated by first order of CSVD analysis. We show that theeconomy, represented by both credit conditions and businessconditions, is changing recurrently but with different frequenciesfor different time periods. Credit variables making the greatestlinear contribution to first Principal Component can be identifiedas credit cycle indicators. The result of leading businessvariables to credit variables in an economy provides the basis topredict credit condition by business cycle indicators.The credit rating system is a publicly available measure of theriskiness of financial securities and a rating transition matrixquantifies the risk, by permitting calculation of the probabilityof downgrade or default. Credit migration is observed to beinfluenced both by business conditions and by an issuer's owncredit status. We assume the rating history for a particularinstitution is Markovian, and histories for differentinstitutions are assumed to be statistically independent, in bothcases the history of market conditions are known. With a simpleGLM, we investigate the significance of business conditions andtheir two major impacts - creditworthinessdeterioration/improvement and credit stability. We propose amodel of transition probability in discrete time and a model ofinstantaneous transition rates in continuous time, and fit themby maximum likelihood. Business conditions are shown to have asignificant effect: higher likelihood for credit qualityimprovement and stability under good business conditions whilehigher likelihood for credit quality deterioration and driftunder severe business conditions. The two business impacts aresignificant and business deterioration/improvement impact isgreater than its stability impact on credit rating transitions.Investment-grade rating transitions are more sensitive to longrate risk while speculative-grade rating transitions are moresensitive to short rate risk. Compared to a discrete model, thecontinuous transition model has much greater over-dispersion butis more practical.

This thesis examines cross-market correlations between means and variances in sovereign credit markets and captures the presence of any contagion effect by focusing on parallel movements between markets in the wake of the recent crisis. Furthermore, it focuses on the effect of policy interventions on the dynamics of these correlations. First, to look at the correlation between markets, we investigate the interaction between sovereign spreads and creditworthiness. Our results suggest that there are stable long-term cointegration relationships and significant short-term reactions between government CDS spreads to rating and outlook changes, with rating and outlook leading CDS spreads. After confirming the leading role of credit ratings, we further investigate the spillover effect from ratings to CDS spreads across markets and countries. We are concerned with the spillover effect of a change in the sovereign credit rating and outlook of one country on the sovereign CDS spreads of other countries. We find that rating and outlook announcements originating from different countries have a strong spillover effect across countries but not across regions, while countries’ initial credit status has limited effect on such spillover. Moreover, the US market is a strong source of global spillover to all the countries. After controlling for US factors, the international spillover effects are found to be stronger during crisis periods than in tranquil periods. In addition, credit outlook changes have a greater impact on sovereign CDS spread responses than rating change announcements, suggesting that outlook changes carry more new information. Furthermore, we are also concerned with the influences of rescue plans by the European Union (EU) and the International Monetary Fund (IMF) on the interdependence of sovereign credit risk, measured by CDS spreads, in the Eurozone. The study focuses on the interaction between two groups of nations, ‘cores’ (Austria, Belgium, France, Germany and the UK) and ‘PIIGS’ (Portugal, Ireland, Italy, Greece and Spain), before and after these bailouts. We are able to control for the rating and other external influences affecting sovereign CDS spreads. There are three principal findings. (1) Before the EU interventions, the spreads of the rescued countries – Greece, Ireland, Portugal and Spain (PIGS) – had a strong influence on rating changes in Austria, Belgium, France, Germany and the UK (core European countries). (2) After bailout, our results underline increased interdependencies between sovereign credit risk in the EU area, especially between the rescued country and the core countries. This suggests that these bailout plans not only increase the influence of the rescued country on the development of the core nations, but also amplify the sensitivity of PIIGS to changes in the cores. (3) Different countries will vary in their financial stability and their fundamentals will differ, so they will be expected to respond differently to a bailout. Indeed, distinctive interaction behaviours across countries, related to country-specific characteristics (fiscal outlook), is found for each of the financial policy interventions. Second, to look at the correlation between variances, this study investigated correlation between 9 major EMU countries’ CDS markets during the sovereign debt crisis, and hence examined the impacts of policy interventions on these markets, using the DCC-GARCH model. The main purpose was to assess the extent to which the policy interventions influenced the dynamics of correlations in sovereign CDS markets, after controlling for international influence (US VIX), and both domestic and foreign sovereign credit rating and outlook. Our results suggest that correlations are time-varying for all the sample countries. Most of the policy interventions led to a significant increase in the pairwise correlations. Our interpretation is that the “two-way feedback” between the healthy country and the bailed-out country causes the public-to-public risk transfer. The increased debt and deficit partly result from assisting other troubled nations. Through policy interventions, any deterioration in the sovereign creditworthiness of the healthy countries could transmit back to the bailed-out countries. Moreover, the estimation result suggests that policy interventions, rather than VIX and credit rating/outlook, play the most direct and significant role in shaping the structure of dynamic correlation in the EMU markets.

Business Administration
Ph.D.
This dissertation investigates the role of credit in the evaluation of risk and return. The research comprises three essays, which analyze the use of credit from different perspectives. Chapter 1: The first essay proposes a comprehensive theory for the assessment and implementation of "acceptable" underwriting and rating variables. While the use of personal credit was the driving force behind the essay, we extend our theory and models to include all controversial rating classifications. It is shown that a rating classification would be appropriate when the cost to society is relatively small. The use of personal credit in the automobile insurance industry is provided as an application of the proposed models, and other considerations are explored. Chapter 2: For many years, gamblers have developed strategies to reach specific monetary and survival goals. In the second essay, a strategy is introduced in which a speculator engages in bet doubling to increase his chances of walking home a winner. It is shown that with enough credit it is quite possible to become a winner with a high degree of certainty--99.9%, even while facing a losing proposition. However, huge returns require huge risks, and so adopting such a strategy would eventually lead to large losses and negative expected profits. It is also shown that limited liability and a cost of obtaining credit are important factors to consider when analyzing expected gains. Chapter 3: "Hazardously immoral" contracts force external parties to bear significant losses without their consent. Abuses are particularly likely to occur when the threat of system-wide disruption is sufficient to make governments and international agencies bail out the offending organizations in order to limit total damages. The models provided in chapter 2 are presented in the third essay as strategies for externalizing extreme risks, and several results are derived.
Temple University--Theses

Thesis Portfolio Credit Risk Modeling focuses on state-of-the-art credit models largely implemented by banks into their banking risk-assessment and complementary valuation system frameworks. Reader is provided in general with both theoretical and applied (practical) approaches that are giving a clear notion how selected portfolio models perform in real-world environment. Our study comprises CreditMetrics, CreditRisk+ and KMV model. In the first part of the thesis, our intention is to clarify theoretically main features, modeling principles and moreover we also suggest hypotheses about strengths/drawbacks of every scrutinized model. Subsequently, in the applied part we test the models in a lab-environment but with real-world market data. Noticeable stress is also put on model calibration. This enables us to con firm/reject the assumptions we made in the theoretical part. In the very end there follows a straightforward general overview of all outputs and a conclusion.

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

This thesis is an analysis of sovereign default using option pricing models. The first part of the thesis applies the structural credit risk models of Gapen, Gray, Lim and Xiao, (GGLX) and Karmann and Maltritz (KM) to 25 countries accounting for about 75% of global GDP. The GGLX model underestimates sovereign spread and hence the probability of default. This confirms one of the main criticisms of structural credit risk models when applied to corporate default. By contrast, the estimates produced by the KM are far too high; the estimated probability of default is almost one in some cases. The second part of the thesis estimates the default risk indicators using the GGLX model in conjunction a number of different assumptions about the value of sovereign assets. It also uses market values of sovereign spread, which thus becomes an input to the model rather than an output. These approaches have not been reported in the literature before. In addition, Ito's lemma is used derive the corresponding geometric Brownian motion for sovereign spread. Using the new approach, the implied probabilities of default are larger than those obtained using standard GGLX. The model also gives revised values for domestic currency liability and its volatility. These are larger than the values reported by national agencies, thus contributing to the explanation of why structural credit risk models underestimate real-world credit spreads and the risk of default. The outputs from the model also lead to the construction of balance sheet ratios, which contribute to information about the likelihood of sovereign default. Overall, the new model results in default rankings and associated measures which are significantly more realistic than those produced by the standard GGLX model.

With the current situation of credit spread contagion illustrated by the European sovereign bonds crisis and the chain reaction triggered by the derivatives books of Lehman Brothers, financial institutions have increasingly focused on pricing and risk management of counterparty credit risk. Recent credit contagion through financial contingent claims highlight the fact that contagion links impact the value of products when investors are exposed to counterparty risk. This thesis plan to build on reduced-form credit risk models to assess the credit risk contagion that is inherent in a obligor multivariate framework. The aim is to evaluate the requirements that are necessary in generating a mathematical framework consistent with the valuation of financial claims, credit and non-credit related, where the parties of those claims exhibit credit risk contagion. By applying a multivariate framework of credit contagion to counterparty credit risk based on a queueing theory, called phase-type distribution, we hope to highlight the benefit of bottom-up models versus top-down ones in terms of extracting information relative to dependence within an identi able obligor set. We will review the mathematical literature in addressing credit dependence modelling in dynamic and static format. This will be the opportunity to value a set of claims under our model to show that claims that contain "credit leverage" are particularly sensible to credit risk contagion and could benefit from our developed framework to gain adequate counterparty credit risk pricing.

In this thesis we have a review of the critical issues of CVA/DVA/FVA pricing framework, provide detailed economic interpretations of these xVA terms and present empirical studies on DVA hedging practice in the marketplace and a new approach to hedge DVAs. The economic drivers and implications of central clearing and initial margins on derivatives are addressed as well.

Mestrado em Ciências Actuariais
Uma parte considerável do negócio bancário inclui naturalmente o empréstimo de dinheiro. Inerentemente, o risco de não receber de volta o montante emprestado é assumido pela instituição bancária. Neste trabalho, o risco de incumprimento é estudado através da função de distribuição das perdas agregadas. Depois de feita a ponte entre as características de uma carteira de empréstimos de um banco e as características de uma carteira de apólices de seguros vida, os resultados da Teoria de Risco podem ser aplicados à carteira em estudo. O CreditRisk+, geralmente classificado como o modelo actuarial, é um modelo de risco de crédito que tem por base esta ponte. Para aplicação deste modelo, é necessária informação relativa às probabilidades de incumprimento de cada devedor e a exposição ao risco, que no nosso caso é igual ao montante em dívida. Na primeira parte deste trabalho é estimada a probabilidade de incumprimento através de um modelo logit, tendo em conta alguns indicadores financeiros da empresa. Seguidamente, no contexto de um modelo de risco coletivo, é aplicado o método iterativo de Panjer. Seguindo a metodologia proposta pelo modelo CreditRisk+, a carteira é seguidamente dividida em setores e, em cada setor, é introduzida volatilidade à probabilidade de incumprimento. No final, conclui-se que conseguem ser obtidos resultados semelhantes utilizando métodos de aproximação menos dispendiosos, nomeadamente com a aproximação NP. Finalmente, a taxa de juro média que o banco deveria aplicar aos empréstimos em carteira é calculada, assim como a reserva que deveria ter sido constituída.
A considerable part of the banking business includes the lending of money. Inherently, a bank incurs the risk of not receiving back the money lent. In this work, default risk is studied through the distribution function of the aggregate losses. After making the link between the characteristics of a portfolio of loans and of a life insurance policies portfolio, Risk Theory results are applied to the portfolio of loans under study. CreditRisk+, usually classified as the actuarial model, is a credit risk model which uses this link. As an input to this model, both the individual probabilities of default for each obligor and the exposure at risk are needed. The first part of this work focus on the estimation of the probability of default through a logit model, taking into account some financial indicators of the company. Then, in the context of a collective risk model, Panjer?s recursive algorithm is applied. Following the methodology of CreditRisk+, the portfolio is then divided into sectors and default volatility is introduced in each sector, reaching a different aggregate loss distribution function. At the end, we find that similar results are obtained with less time consuming approximation methods, particularly with NP approximation. Finally, the average interest rate that the bank should have charged to the loans in the portfolio is found as well as the amount of money that should have been reserved to account for losses.
info:eu-repo/semantics/publishedVersion

In this thesis we present the intensity-based approach to consider default in a general local-stochastic volatility model with stochastic interest rate. In this setting we describe, as in [18], a technique to find approximate solutions of the corresponding partial differential equations and we provide numerical examples in the particular case of JDCEV and Vasicek model, respectively, for the dynamics of the asset and the short rate. Finally, we introduce a formula for the par CDS spreads and applying the approximation method we calibrate our intensity model to credit data finding the model parameters matching the default probabilities implicit in CDS prices (by bootstrapping) to the default probabilities implied by the model itself.

A model for large portfolio credit risk is developed by using results on the asymptotic behaviour of stochastic networks. We analyse some of the charac- teristics of the model by studying the infinitesimal generator of the portfolio default process using some results of the theory of Piecewise Deterministic processes (PDPs). An efficient pricing technique is proposed using a newly- 1ntroduced quadrature algorithm using a decomposition of the sample space similar to the canonical Poisson space decomposition. Accurate calibration to iTraxx spreads is demonstrated.

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.

This thesis aims to make a contribution to the understanding of the key economic and company specific components of credit spreads in the investment and non-investment grade US bond market for different maturing bond indices. It calls for the full integration of different market andfirm specific variables into a unique framework, in order to predict credit spread changes. Key determinants of default risk are employed to determine credit migration risk. Particularly, this thesis provides evidence as to the relation between different macroeconomic factors and credit spread changes in all different maturities and rating categories, it supports the use of the consumer confidence index, as the most important variable explaining changes in credit spreads in investment and high yield companies, but most importantly it provides support for the strong informational content of high yield spreads as predictors of output growth, based on Option Adjusted Spreads. It favours the inclusion of implied volatilities in explaining credit spread changes, while it criticises the incorporation of historical ones. Throughout the thesis, it becomes evident that BBB-rated bonds exhibit highly volatile patterns and are very difficult to model. Financial ratios adjusted to reflect depreciation and amortisation expenses, which are usually very high for non-investment grade companies, prove to be very important in explaining changes of high yield spreads. However, firm specific risk, accounts only for a small fraction of the variation in the investment grade category. Ultimately, it is shown that by using solely market (equity and macro variables) and firm specific variables, i. e. some of the key determinants of default risk and the price of credit risky debt in most Merton-type models, we can accurately forecast credit spread changes at least one year ahead, particularly based on results provided from the investment grade sample. Moreover, credit spread forecasts, based on our set of OAS, tend to be overestimated rather than underestimated, as opposed to results provided by previous studies. This makes forecasts more conservative and therefore more appealing for risk management purposes. In particular, this thesis is focused on the main drivers of credit spread movements in the US corporate bond market. There are four issues mainly considered. The first part of the thesis examines a question that is a point of central focus in the fixed income literature, i. e. the relation between credit spread changes and the macroeconomic cycle. This chapter is inspired by the relatively little work that has been done on the empirical relationship between credit spread changes and the macroeconomy, since most of the literature on this issue focuses on macroeconomic variables and the modelling of default risk. We investigate how this relation evolves, not only with respect to short, medium and long term maturities but also for investment and non-investment rated companies, by testing the direction of causation among economic variables and credit spreads and by employing different sets of data and estimation techniques to explore the relation. We find that irrespective of the statistical method used or the time period tested that the most important variable in explaining the variation of credit spread changes is the US Consumer Confidence Index. We affirm the negative relation between the consumer confidence index, money supply and changes in credit spreads but not for the variables of GDP and industrial production. The negative relation between the term structure and credit spreads is also asserted for investment grade bonds of all maturities, consistent with the structural model's theory, while we find this relation to be positive for non-investment grade companies. Results from the OLS regressions suggest that macroeconomic variables alone, can explain at best a 17% of the variation in medium and long term maturing indices, and a 20.5% in short term indices. Findings from cross sectional regressions suggest that macroeconomic factors alone can explain 27.9% of the variation in credit spreads for investment grade bonds and a 44.4% for high yield ones. When testing the direction of causation, wefind thatfor long and medium term maturity investment grade indices we reject the null hypothesis that macroeconomic variables don't granger cause changes in credit spreads, but not for short term maturities and the high yield sector. Indeed, results provided on that respect from the high yield category, provide evidence that non-investment grade spreads may be a good proxy for predicting estimating overall financial conditions. Secondly, the relation between credit spreads and equities together with their implied and historical volatilities is examined. This chapter constitutes an effort to fill the gap in the existing literature, which has focused mainly on bond returns or yield changes, while very limited work has been done in modelling credit spread changes. 12 Empirical evidence points out to the fact that debt markets not only in the US but also in Europe and elsewhere seem to be greatly affected by the movements in the equity markets. If that is the case we should expect changes in equity prices to affect changes in credit spreads. This assumption is tested on a cross sectional and time series basis, for quarterly and monthly frequencies and by using company specific equity prices against the respective credit spreads, but also by including equity and volatility indices. We find that there is a negative relation between credit spread and equity changes, irrespective of maturity or rating category. Results provided by univariate regressions, based on changes in equity prices alone, explain haýr of the variation of B-rated corporate spreads. Results affirm the positive relation between implied volatilities and their high explanatory power on credit spread changes while findings derived from historical volatilities although statistically significant don't even marginally support the hypothesis of explaining the variation in credit spreads. In particular, results from pooled regressions suggest that when implied volatilities are substitutedfor the historical ones, adjusted R2 sfell to 6% and 28%for the investment and non-investment grade samples respectively (from 25% and 50.3% for investment and non-investment grade companies, when implied volatilities are considered). Resultsfrom OLS regressions, suggest that equity variables explain at best a 44% for short term maturing indices, and 35% and 37% for medium and long term maturing indices 2 as reflected by the adjusted R S. We also strongly reject the null hypothesis that implied volatilities don't granger cause changes in credit spreads but only with regards to short and medium term maturities. The next chapter of the thesis focuses on how changes in a company's financials, as those are presented by ratios, actually infiuence changes in credit spreads. The reason for including this chapter is due to the fact that although traditional ratio analysis has been widely investigated, it has mainly been tested within the context of default risk, while very limited literature exists on the use of traditional credit risk analysis in determining credit spread changes. Cross sectional analysis is employed in this chapter to test the hypothesis that credit spread changes are influenced by changes in accounting factors, both in investment and high yield categories. On a multivariate basis, we find that 63.5% of the variation in high yield credit spreads is explained by the changes in financial ratios, as reflected by the adjusted R2, compared to an adjusted R2 of 19.2% for investment grade companies. Consistently, 13 in the randomly selected group of companies, we find that traditional ratios can explain one third of the variation in credit spreads in the high yield sector, although less than 10% in the investment grade sample. A reason for the higher explanatory power in the high yield sector entails the use of ratios adjusted, to reflect depreciation and amortisation expenses, which hasn't been considered before. The most statistically and economically significant coefficient was obtained from the current market capitalisation, which was used as a proxy for the firm's size. The last part of the thesis, constitutes an effort to combine all the above factors (macroeconomic, equity and financials), in order to forecast credit spread changes one and two years ahead. We show that on a multiple regression context, results provided are consistent with previous chapters and indeed highly significant in explaining credit spread variation, irrespective of the time period tested. For the total sample we get an adjusted R2 of 95% or 52% as part of the weighted and unweighted statistics respectively. A robust model is identified for forecasting credit spread changes one year ahead, with the employment of the dynamic solution method. The accuracy of the model doesn't fall below 85% within the first year, while we choose as the most vigorous method for estimating coefficients the GLS method adjustedfor heteroscedasticity, since it consistently provides more conservative forecasts.

The financial crisis and the bankruptcy of Lehman Brothers in 2008 lead to harder regulations for the banking industry which included larger capital reserves for the banks. One of the parts that contributed to this increased capital reserve was the the credit valuation adjustment capital charge which can be explained as the market value of the counterparty default risk. The purpose of the credit valuation adjustment capital charge is to capitalize the risk of future changes in the market value of the counterparty default risk. One financial contract that had a key role in the financial crisis was the credit default swap. A credit default swap involves three different parts, a contract seller, a contract buyer and a reference entity. The credit default swap can be seen as an insurance against a credit event, a default for example of the reference entity. This thesis focuses on the study and calculation of the credit valuation adjustment of credit default swaps. The credit valuation adjustment on a credit default swap can be implemented with two different assumptions. In the first case, the seller (buyer) of the contract is assumed to be default risk free and then only the buyer (seller) contributes to the default risk. In the second case, both the seller and the buyer of the contract is assumed to be default risky and therefore, both parts contributes to the default risk.
Finanskrisen och Lehman Brothers konkurs 2008 ledde till hårdare regleringar för banksektorn som bland annat innefattade krav på större kapitalreserver för bankerna. En del som bidrog till denna ökning av kapitalreserverna var kreditvärdighetsjusteringens kapitalkrav som kan förklaras som marknadsvärdet av motpartsrisken. Syftet med kreditvärdighetsjusteringens kapitalkrav är att kapitalisera risken för framtida förändringar i marknadsvärdet av motpartsrisken. Ett derivat som hade en nyckelroll under finanskrisen var kreditswappen. En kreditswap innefattar tre parter, en säljare, en köpare och ett referensföretag. Kreditswappen kan ses som en försäkring mot en kredithändelse, till exempel en konkurs på referensföretaget. Detta arbete fokuserar på studier och beräkningar av kreditvärdesjusteringen på kreditswappar. Kreditvärdesjusteringen på en kreditswap kan implementeras med två olika antaganden. I det första fallet antas säljaren (köparen) vara riskfri och då bidrar bara köparen (säljaren) till konkursrisken. I det andra fallet antas både säljaren och köparen bidra till konkursrisken.

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Finance from the NOVA – School of Business and Economics
Although there is substantial literature on credit risk, studies often do not consider financial institutions. However, considering that several entities are exposed to these institutions, namely through the counterparty role that they play, it is of major relevance the accurate assessment of its credit risk. As such, this study aims at analysing three different models to measure credit risk of financial institutions and conclude which one best predicts credit rating downgrades. The three models studied comprise a credit scoring model; a naïve approach of the Merton (1974) Model; and CDS spreads. The results show that all three models are statistically significant to predict credit rating downgrades of financial institutions, though the latter two prove to better and more timely anticipate downgrades than the credit scoring model.

Mestrado em Finanças
O risco de crédito para o sector bancário é um assunto muito importante. Nesse sentido, é primordial adquirir ferramentas para medir este risco com algum grau de segurança de modo a ser possível tomar as decisões corretas sobre o crédito cedido a clientes. O objetivo deste trabalho é compreender o quão importante é o risco de crédito para as instituições financeiras e apresentar uma forma de o medir associado com o crédito a empresas, analisando um modelo de score para avaliar que o mesmo seja avaliado. Este trabalho também descreve as atividades desenvolvidas nos principais departamentos de uma instituição bancária, de acordo com um estágio que teve lugar no Banco BIC, desenvolvendo desta forma uma revisão da literatura ao risco de crédito, uma descrição sobre a evolução da banca, modelos de avaliação assim como também uma análise a uma empresa, utilizando o modelo Z-Score, comparando o resultado obtido com a classificação fornecida por uma agência de rating. Os resultados provaram que o modelo em análise foi eficaz, proporcionando uma avaliação, dentro das suas limitações, de acordo com a classificação fornecida por esta agência de rating.
Credit risk in banking industry is a very important subject. Therefore, it is important to acquire tools to measure it, with some degree of reliability, in order to be possible to take the correct decisions regarding client loans. The objective of this final project is to understand the importance of the credit risk to financial institutions and to present a way of measuring this risk associated with loans to companies, analysing a score model to evaluate this risk. This project also describes the activities developed by the main departments of a banking institution in accordance to an internship which took place in Banco BIC, developing this way a literature review to credit risk, banking evolution and score models as well as analysing a company using the Z-Score model, comparing the results obtained with the rating provided by a rating agency. The results proved that the model under analysis was effective, providing a reliable output within its limitations, correspondingly to the rating provided by this rating agency.

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.

The thesis covers a wide range of topics from the credit risk modeling with the emphasis put on pricing of the claims subject to the default risk. Starting with a separate general contingent claim pricing framework the key topics are classified into three fundamental parts: firm-value models, reduced-form models, portfolio problems, with a possible finer sub-classification. Every part provides a theoretical discussion, proposal of self-developed methodologies and related applications that are designed so as to be close to the real-world problems. The text also reveals several new findings from various fields of credit risk modeling. In particular, it is shown (i) that the stock option market is a good source of credit information, (ii) how the reduced-form modeling framework can be extended to capture more complicated problems, (iii) that the double t copula together with a self-developed portfolio modeling framework outperforms the classical Gaussian copula approaches. Many other, partial findings are presented in the relevant chapters and some other results are also discussed in the Appendix.

The thesis is concerned with the design of conceptual structures in credit risk modelling. The focus is on designing mathematical constructs that serve as a unified framework for reasoning about credit risk modelling. Three contributions are made to this area of research: category theory, providing a powerful tool to study the relations of common structures underlying credit risk modelling; stacking model, to address issues of inconsistent and biased performance measurement; and the Kelly criterion, shifting the focus from dichotomous classification to optimal credit risk allocation.

A well functioning economy requires a stable credit market. Computational intelligence methods could provide a method to reduce the amount of uncertainty in the markets. This report examines four different methods for predicting the probability for defaults of credit card clients in Taiwan. The four selected methods were Linear Discriminant Analysis, Support Vector Machines, Artificial Neural Networks and Deep Neural Networks. The models were then evaluated with regards to five different methods: Area Under the Curve for the Receiver Operating Characteristic, accuracy, precision, sensitivity and specificity. The results showed that all models performed better than random with similar results, except for the Support Vector Machine which in our testing configuration incorrectly classified almost all debtors that defaulted on their debt. Although there was no clearly superior model the results showed that the Deep Neural Networks and Linear Discriminant Analysis were the two most promising methods.
En välfungerande ekonomi behöver en stabil kreditmarknad. Maskininlärningsmetoder har potential att reducera osäkerheten på marknaden. Rapporten undersöker fyra olika metoder för att beräkna sannolikheten att en låntagare återbetalar sin kreditkortsskuld baserat på kreditkortsdata från Taiwan. Metoderna som valdes var Linear Discriminant Analysis, Support Vector Machines, Arti- ficiella Neurala Nätverk och Djupa Neurala Nätverk. Modellerna utvärderades med avseende på fem olika metoder: Area Under the Curve for the Receiver Operating Characteristic, nogrannhet, precision, sensitivitet och specificitet. Resultaten visade att alla modeller presterade bättre än slump med liknande resultat utom för Support Vector Machines som i vår testkonfiguration felaktigt klassificerade nästintill alla låntagare som inte skulle återbetala. Även om ingen modell var tydligt bättre än de andra visade resultaten att Djupa Neurala Nätverk och Linear Discriminant Analysis är metoderna som visar mest potential.

This doctoral thesis consists of 4 self-contained chapters: Sovereign Credit Default Swap Premia. This comprehensive review of the literature on sovereign CDS spreads highlights current academic debates and contrasts them with contradictory statements from the popular press.  Real Economic Shocks and Sovereign Credit Risk. New empirical evidence highlights that global macroeconomic risk unspanned by global financial risk bears some responsibility for the strong co-movement in sovereign spreads. A model with only two global macroeconomic state variables rationalizes the existence of time-varying risk premia as a compensation for exposure to common U.S. business cycle risk. The Term Structure of CDS Spreads and Sovereign Credit Risk. The term structure of CDS spreads is an informative signal about the relative importance of global and country-specific risk factors for the time variation of sovereign credit spreads. An empirically validated model illustrates how local risk matters relatively more when the slope is negative, while systematic risk bears more responsibility when the slope is positive. Squeezed Everywhere - Disentangling Types of Liquidity and Testing Limits-to-Arbitrage. The CDS-Bond basis is used as a laboratory to disentangle different types of liquidity and to test limits-of-arbitrage. While asset-specific liquidity is cross-correlated in both the cash and derivative market, funding and market liquidity matter only for the former. The tests find strong evidence in favor of margin-based asset pricing and flight-to-quality effects.

Diss. Stockholm : Handelshögskolan, 2013. Sammanfattning jämte 4 uppsatser

This thesis has the object to give a general overview of the Credit and Counterparty Risk and to evaluate how the factors linked to this risk influence the price of the OTC products. CCR comes from the probability at default of an institute that could potentially cause losses to its own creditors. The fundamental concept that is linked to this value of risk is called Exposure, concept that will be carefully presented and computed to a set of OTC products. There exist several measures that allow to quantify the amount of credit exposure during the life of a product. Measures as Expected Exposure, Potential Future Exposure or Expected Positive Exposure are auxiliary to the definition of the fundamental measure Credit Value Adjustment (CVA). This metric define the true value of a risky product, as it is computed as the difference between the risk free value of a contract and its real value taking into consideration the probability at default of the counterparty. The last part of the thesis is dedicated to the computation of the Exposure and the CVA for European Options making use of the Internal Method Model (IMM) elaborate by one of the biggest bank institute.

This dissertation comprises two essays; the first of which investigates sovereign credit risk interdependencies, while the second examines the reaction of corporate credit risk to sovereign credit risk events. The first essay titled, Characterizing Sovereign Credit Risk Interdependencies: Evidence from the Credit Default Swap Market, investigates the relationships that exist among disparate sovereign credit default swaps (CDS) and the implications on sovereign creditworthiness. We exploit emerging market sovereign CDS spreads to examine the reaction of sovereign credit risk to changes in country-specific and global financial factors. Utilizing aVAR model fitted with DCC GARCH, we find that comovements of spreads generally exhibit significant time-varying correlations, suggesting that spreads are commonly affected by global financial factors. We construct 19 country-specific commodity price indexes to instrument for country terms of trade, obtaining significant results. Our commodity price indexes account for significant variation in CDS spreads, controlling for global financial factors. In addition, sovereign spreads are found to be related to U.S. stock market returns and the VIX volatility risk premium global factors. Notwithstanding, our results suggest that terms of trade and commodity prices have a statistically and economically significant effect on the sovereign credit risk of emerging economies. Our results apply broadly to investors, financial institutions and policy makers motivated to utilize profitable factors in global portfolios. The second essay is titled, Differential Stock Market Returns and Corporate Credit Risk of Listed Firms. This essay explores the information transfer effect of shocks to sovereign credit risk as captured in the CDS and stock market returns of cross-listed and local stock exchange listed firms. Based on changes in sovereign credit ratings and outlooks, we find that widening CDS spreads of firms imply that negative credit events dominate, whereas tightening spreads indicate positive events. Grouping firms into companies with cross-listings and those without, we compare the spillover effects and find strong evidence of contagion across equity and CDS markets in both company groupings. Our findings suggest that the sensitivity of corporate CDS prices to sovereign credit events is significantly larger for non-cross-listed firms. Possible reasons for this finding could in fact be due to cross-listed firms’ better access to external capital and less degree of asymmetric information, relative to non-cross-listed peers with lower level of investor recognition. Our results provide new evidence relevant to investors and financial institutions in determining sovereign credit risk germane to corporate financial risk, for the construction of debt and equity portfolios, and hedging considerations in today’s dynamic environment.

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.

Counterparty credit risk is present in trades offinancial obligations. This master thesis investigates the up and comingtechnology blockchain and how it could be used to mitigate counterparty creditrisk. The study intends to cover essentials of the mathematical model expectedloss, along with an introduction to the blockchain technology. After modellinga simple smart contract and using historical financial data, it was evidentthat there is a possible opportunity to reduce counterparty credit risk withthe use of blockchain. From the market study of this thesis, it is obvious thatthe current financial market needs more education about blockchain technology.
Motpartsrisk är närvarande i finansiella obligationer. Den här uppsatsen un- dersöker den lovande teknologin blockkedjan och hur den kan användas för att reducera motpartsrisk. Studien har för avsikt att täcka det essentiel- la i den matematiska modellen för förväntad förlust, samt en introduktion om blockkedjeteknologi. Efter att ha modellerat ett enkelt smart kontrakt, där historiska finansiella data använts, var det tydligt att det kan finnas en möjlighet att reducera motpartsrisk med hjälp av blockkedjan. Från mark- nadsundersökningen gjord i studien var det uppenbart att den nuvarande finansiella marknaden är i stort behov av mer utbildning om blockkedjan.