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Santos, Bárbara Leitão. "Practical approach for probability of default estimation under IFRS 9." Master's thesis, Instituto Superior de Economia e Gestão, 2018. http://hdl.handle.net/10400.5/17350.
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Mestrado em Mathematical FinanceThis report is part of the conclusion of the Master degree in Mathematical Finance, as a result of a 6-month internship at EY, in Financial Services - Advisory. Due to a recent financial crisis, credit entities had to deal with uncertainty, being credit risk one of the main concerns. Risk management in this type of entities is crucial to assure financial stability, and therefore, there is always a constant need of improvement. During the financial crisis of 2008, risk management failed its man purpose since risk models reveal insufficient to capture the risk deterioration on exposures and fail to estimate credit losses under a change in the economic cycle. Therefore, IFRS 9 becomes the new standard imposed by IASB, in order to replace IAS 39. This internship was a vector to expand my knowledge concerning impairment models and the new regulatory framework of the International Accounting Standard Board based on IFRS 9 Financial instruments, by studying a general approach on a specific perspective of a Portuguese bank. This report focuses on collective impairment, regarding the choices and validation of the model for the risk parameter PD used by the bank institution in analysis under this new standard.
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Kauffmann, Luiz Henrique Outi. "Uma abordagem Forward-Looking para estimar a PD segundo IFRS9." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/55/55137/tde-06112018-182558/.
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Este trabalho tem por objetivo discutir as metodologias de estimação da PD utilizadas na indústria financeira. Além disso, contextualizar a aplicação do trabalho ao IFRS9 e seu direcionamento para o tema de Risco de Crédito. Historicamente os grandes bancos múltiplos utilizam variadas metodologias econométricas para modelar a Probabilidade de Descumprimento (PD),um dos métodos mais tradicionais é a regressão logística, entretanto com a necessidade do cálculo da Perda Esperada de Crédito através do IFRS9, se torna necessário mudar o paradigma de estimação para uma abordagem forward-looking, isto está sendo interpretado por muitas instituições e consultorias como a inclusão de fatores e variáveis projetadas dentro do processo de estimação, ou seja, não serão utilizados apenas os dados históricos para prever o descumprimento ou inadimplência. Dentro deste contexto será proposto uma abordagem que une a estimação da Probabilidade de Descumprimento com a inclusão de um fator foward-looking.This paper aims to discuss the methodologies used to estimate the Probability Of Default used in the financial industry. In addition, contextualize the application of the work to IFRS9 requirements and its targeting to the Credit Risk theme. Historically large multi-banks use a variety of econometric methodologies to model the Probability of Default, one of the more traditional methods is logistic regression. However, with the need to calculate the expected credit loss through IFRS9, it becomes necessary to change the estimation paradigm to a forwardlooking approach, this is being interpreted by many institutions and consultancies companies as the inclusion of factors and variables projected within the estimation process, that is, not only historical data are used to predict the default. Within this context will be proposed an approach that joins the estimation of Probability of Default with the inclusion of a forward-looking factor.
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Pereira, Bernardo Vieira Gonçalves. "Estudo sobre evolução do balanço de um banco em situação de stress económico : modelo macroeconómico de PD." Master's thesis, Instituto Superior de Economia e Gestão, 2019. http://hdl.handle.net/10400.5/19770.
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Mestrado em Econometria Aplicada e PrevisãoEste relatório de estágio enquadra-se no âmbito do Trabalho Final de Mestrado para o Mestrado de Econometria Aplicada e Previsão do Instituto Superior de Economia e Gestão (ISEG). O estágio decorreu na Deloitte Consultores, S.A., num dos escritórios de Lisboa, com o intuito de me preparar para a realidade do mercado de trabalho através de um processo de integração na empresa durante a realização das atividades propostas. Neste relatório pretende-se expor um retrato daquilo que foram os meus primeiros seis meses de trabalho na Deloitte, através da descrição aprofundada do tema de trabalho, procurando sempre relacioná-lo com os conceitos adquiridos no decorrer do mestrado. A Deloitte faz parte das Big 4, grupo de empresas que domina o setor de serviços profissionais, sendo de realçar o seu grau de exigência e profissionalismo nos projetos a que se compromete. Na Deloitte, as minhas funções passaram pela análise das taxas de incumprimento e de migrações entre classe de risco de uma carteira de clientes empresa de um banco, tendo estabelecido um modelo econométrico que permite a previsão da evolução dessas taxas com base em variáveis macroeconómicas. Este tipo de modelos é aplicado na obtenção da perda esperada usada, por exemplo, no cálculo de provisões por imparidade e em testes de stress.
This report consists of my thesis for the Master in Applied Econometrics and Forecasting from ISEG, Lisbon School of Econmics. My internship took place in Deloitte Consultores, S.A., in one of the Lisbon offices, with the intent of not only adapting myself to the labor market reality but also to get acquainted with the company practices and costumes while performing my planned activities. In this report it is exposed my first six months of work for this company, through a detailed description of what was done, while always attempting to correlate it with the knowledge acquired thoughout my master degree. Deloitte is part of the Big 4, a group of consulting companies that rule the great majority of the market, and it is known for its high work quality and demand. In Deloitte, my job was to analyse default ratios and migration matrices, resultant from an undisclosed financial institution's portfolio, producing a macroeconomic regression model that would allow for the forecast of this default probability. These kind of models, to obtain estimates of the Expected Loss, are used, for example, in the computation of impairment provisions and stress tests.
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Čabrada, Jiří. "Kreditní rizika z pohledu Basel II." Master's thesis, Vysoká škola ekonomická v Praze, 2007. http://www.nusl.cz/ntk/nusl-5575.
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The thesis "Credit risk from Basel II point of view" deals with new capital concept with main focus on the credit risk. The particular emphasis is laid on the chief issue of Basel II concept i.e. internal models. The thesis quite in detail describes the usage of basel parameters - LGD particularly - in various day-to-day business processes of credit institutions. An individual part of the thesis is devoted to credit risk mitigants and their impacts on the amount of capital requirements. The analysis carried out precedent Basel II implementation indicated the launching of Basel II should imply risk weighted assests to credit risk decline. This documents the last chapter.
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Zhang, Jie. "Modelling examples of loss given default and probability of default." Thesis, University of Southampton, 2011. https://eprints.soton.ac.uk/172581/.
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The Basel II accord regulates risk and capital management requirements to ensure that a bank holds enough capital proportional to the exposed risk of its lending practices. Under the advanced internal ratings based (IRB) approach, Basel II allows banks to develop their own empirical models based on historical data for probability of default (PD), loss given default (LGD) and exposure at default (EAD). This thesis looks at some examples of modelling LGD and PD. One part of this thesis investigates modelling LGD for unsecured personal loans. LGD is estimated through estimating Recovery Rate (RR, RR=1-LGD). Firstly, the research examines whether it is better to estimate RR or Recovery Amounts. Linear regression and survival analysis models are built and compared when modelling RR and Recovery Amount, so as to predict LGD. Secondly, mixture distribution models are developed based on linear regression and survival analysis approaches. A comparison between single distribution models and mixture distribution models is made and their advantages and disadvantages are discussed. Thirdly, it is examined whether short-term recovery information is helpful in modelling final RR. It is found that early payment patterns and short-term RR after default are very significant variables in final RR prediction models. Thus, two-stage models are built. In the stage-one model short-term Recoveries are predicted, and then the predicted short-term Recoveries are used in the final RR prediction models. Fourthly, macroeconomic variables are added in both the short-term Recoveries models and final RR models, and the influences of macroeconomic environment on estimating RR are looked at. The other part of this thesis looks at PD modelling. One area where there is little literature of PD modelling is in invoice discounting, where a bank lends a company a proportion of the amount it has invoiced its customers in exchange for receiving the cash flow from these invoices. Default here means that the invoicing company defaults, at which point the bank cannot collect on the invoices. Like other small firms, the economic conditions affect the default risk of invoicing companies. The aim of this research is to develop estimates of default that incorporate the details of the firm, the current and past position concerning the invoices, and also economic variables.
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Zollinger, Lance M. "Probability of default rating methodology review." Thesis, Kansas State University, 2014. http://hdl.handle.net/2097/18811.
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Master of AgribusinessDepartment of Agricultural Economics
Allen M. Featherstone
Institutions of the Farm Credit System (FCS) focus on risk-based lending in accordance with regulatory direction. The rating of risk also assists retail staff in loan approval, risk-based pricing, and allowance decisions. FCS institutions have developed models to analyze financial and related customer information in determining qualitative and quantitative risk measures. The objective of this thesis is to examine empirical account data from 2006-2012 to review the probability of default (PD) rating methodology within the overall risk rating system implemented by a Farm Credit System association. This analysis provides insight into the effectiveness of this methodology in predicting the migration of accounts across the association’s currently-established PD ratings where negative migration may be an apparent precursor to actual loan default. The analysis indicates that average PD ratings hold relatively consistent over the years, though the distribution of the majority of PD ratings shifted to higher quality by two rating categories over the time period. Various regressions run in the analysis indicate that the debt to asset ratio is most consistently statistically significant in estimating future PD ratings. The current ratio appears to be superior to working capital to gross profit as a liquidity measure in predicting PD rating migration. Funded debt to EBITDA is more effective in predicting PD rating movement as a measure of earnings to debt than gross profit to total liabilities, although the change of these ratios over time appear to be weaker indicators of the change in PD rating potentially due to the variable nature of annual earnings of production agriculture operations due to commodity price volatility. The debt coverage ratio is important as it relates to future PD migration, though the same variability in commodity price volatility suggests the need implement multi-year averaging for calculation of earnings-based ratios. These ratios were important in predicting the PD rating of observations one year into the future for production agriculture operations. To further test the predictive ability of the PD ratings, similar regression analyses were completed comparing current year rating and ratios to future PD ratings beyond one year, specifically for three and five years. Results from these regression models indicate that current year PD rating and ratios are less effective in predicting future PD ratings beyond one year. Furthermore, because of the variation in regression results between the analyses completed for one, three and five years into the future, it is important to regularly capture ratio and rating information, at least annually.
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Lan, Yi. "Survival Probability and Intensity Derived from Credit Default Swaps." Digital WPI, 2012. https://digitalcommons.wpi.edu/etd-theses/82.
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This project discusses the intensity and survival probability derived from Credit Default Swaps (CDS). We utilize two models, the reduced intensity model and the Shift Square Root Diffusion (SSRD) model. In the reduced intensity model, we assume a deterministic intensity and implement a computer simulation to derive the survival probability and intensity from the CDS market quotes of the company. In the SSRD model, the interest rate and intensity are both stochastic and correlated. We discuss the impaction of correlation on the interest rate and intensity. We also conduct a Monte Carlo simulation to determine the dynamics of stochastic interest rate and intensity.
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Caetano, João Manuel Nunes. "Predictive models of probability of default : an empirical application." Master's thesis, Instituto Superior de Economia e Gestão, 2014. http://hdl.handle.net/10400.5/7704.
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Mestrado em FinançasEste estudo tem como objetivo realizar uma pesquisa dos modelos de previsão do incumprimento a empresas listadas em bolsa. Foram abordadas as metodologias do modelo de Merton (1974), modelo Contabilístico e Híbrido. Testou-se uma amostra de 172 empresas presentes no mercado Americano dos setores do Consumo, Distribuição, Produção e Telecomunicações nas quais 82 entram em incumprimento. Para cada metodologia, a capacidade preditiva foi testada através dos erros Tipo I e II. Os resultados sugerem que o modelo Híbrido, i.e., a combinação de modelos de mercado e análise contabilística, confere maior poder de precisão na classificação de incumprimento, ao invés de cada modelo individualmente.
This study intends to conduct a survey of Probability of Default models to listed companies. The methodologies of Merton (1974) model, Accounting model and Hybrid were addressed. We tested a sample of 172 American companies in the sectors of Consumer Products, Distribution, Manufacturing and Telecommunications in which 82 entered into default. For each methodology, the predictive ability was tested with Type I and II errors. The results suggests that the Hybrid model, i.e. a combination of market models and accounting analysis, have a better performance in the classification of credit default than each model individually.
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Azeredo, Daniela Rita Charrua Cabral de. "Structural models to estimate financial institution´s default probability." Master's thesis, Instituto Superior de Economia e Gestão, 2014. http://hdl.handle.net/10400.5/7898.
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Mestrado em FinançasNeste estudo procurámos, no âmbito do Modelo de Merton (1973), determinar a Distância ao Incumprimento (DD) para uma amostra de bancos Ibéricos. Através da especificação de três diferentes Barreiras de Imcumprimento (DB), foi possivel obter diferentes resultados, sublinhando a importância da DB para output do modelo. Durante a crise, o risco de liquidez foi atenuado pelas políticas de cedência de liquidez levadas a cabo pelo BCE. As definições usadas para db1 e db2, diferem na forma como são tratados os emprestimos do BCE, permitindo implementar um procedimento assente no cálculo da DD para quantificar a redução no risco dos bancos induzida por estas medidas. Os nossos resultados demonstram que as políticas do BCE reduziram o risco de incumprimento dos bancos que constituem a amostra.
This paper is intended to model the default probabilities for selected Iberian Financial Institutions through the application of Merton's Model (1973) framework. Through the use of three different Default Barrier (db) definitions, we were able to obtain very different outputs, stressing how crucial db definition is to the structural model output. Throughout this crisis, liquidity risk was, in some dimension, offset by the ECB funding policies. db1 and db2 definitions, differing only on the way Central Bank loans were treated, were convenient to test non-standard applications of the model. In our study we introduce and test a procedure anchored on Distance to Distress calculation, to quantify the reduction in risk induced by ECB measures, finding that ECB actions effectively reduced bank's default risk.
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Kornfeld, Sarah. "Predicting Default Probability in Credit Risk using Machine Learning Algorithms." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-275656.
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This thesis has explored the field of internally developed models for measuring the probability of default (PD) in credit risk. As regulators put restrictions on modelling practices and inhibit the advance of risk measurement, the fields of data science and machine learning are advancing. The tradeoff between stricter regulation on internally developed models and the advancement of data analytics was investigated by comparing model performance of the benchmark method Logistic Regression for estimating PD with the machine learning methods Decision Trees, Random Forest, Gradient Boosting and Artificial Neural Networks (ANN). The data was supplied by SEB and contained 45 variables and 24 635 samples. As the machine learning techniques become increasingly complex to favour enhanced performance, it is often at the expense of the interpretability of the model. An exploratory analysis was therefore made with the objective of measuring variable importance in the machine learning techniques. The findings from the exploratory analysis will be compared to the results from benchmark methods that exist for measuring variable importance. The results of this study shows that logistic regression outperformed the machine learning techniques based on the model performance measure AUC with a score of 0.906. The findings from the exploratory analysis did increase the interpretability of the machine learning techniques and were validated by the results from the benchmark methods.Denna uppsats har undersökt internt utvecklade modeller för att estimera sannolikheten för utebliven betalning (PD) inom kreditrisk. Samtidigt som nya regelverk sätter restriktioner på metoder för modellering av kreditrisk och i viss mån hämmar utvecklingen av riskmätning, utvecklas samtidigt mer avancerade metoder inom maskinlärning för riskmätning. Således har avvägningen mellan strängare regelverk av internt utvecklade modeller och framsteg i dataanalys undersökts genom jämförelse av modellprestanda för referens metoden logistisk regression för uppskattning av PD med maskininlärningsteknikerna beslutsträd, Random Forest, Gradient Boosting och artificiella neurala nätverk (ANN). Dataunderlaget kommer från SEB och består utav 45 variabler och 24 635 observationer. När maskininlärningsteknikerna blir mer komplexa för att gynna förbättrad prestanda är det ofta på bekostnad av modellens tolkbarhet. En undersökande analys gjordes därför med målet att mäta förklarningsvariablers betydelse i maskininlärningsteknikerna. Resultaten från den undersökande analysen kommer att jämföras med resultat från etablerade metoder som mäter variabelsignifikans. Resultatet av studien visar att den logistiska regressionen presterade bättre än maskininlärningsteknikerna baserat på prestandamåttet AUC som mätte 0.906. Resultatet from den undersökande analysen för förklarningsvariablers betydelse ökade tolkbarheten för maskininlärningsteknikerna. Resultatet blev även validerat med utkomsten av de etablerade metoderna för att mäta variabelsignifikans.
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Soares, Nuno Filipe de Almeida. "Modeling of lifetime probability of default and forward-looking adjustment." Master's thesis, Instituto Superior de Economia e Gestão, 2017. http://hdl.handle.net/10400.5/14963.
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Mestrado em Mathematical FinanceA 1 de Janeiro de 2018, a nova norma contabilística para instrumentos financeiros, IFRS 9 Financial Instruments, tornar-se-á obrigatória. Convergendo as necessidades da crise de 2007 para mudanças técnicas, o seu objetivo é alinhar a contabilidade com a gestão de risco. Uma das principais adaptações é o novo modelo de imparidade, que passa de "perdas incorridas" na IAS 39 para "perdas esperadas" na IFRS 9. Para fazer essa transição, é necessário incorporar informação forward-looking nas estimações. Neste caso, a incorporação necessitava de ser feita para as Probabilidades de Default, uma das variáveis usadas para calcular "perdas esperadas". Portanto, nosso objetivo era desenvolver e validar um modelo, alavancando o trabalho anterior, que integrasse projeções macroeconómicas nas estimativas das Probabilidades de Default. Para isso duas abordagens foram comparadas, sendo uma mais técnica, e, a outra mais simples e mais prática. Após a comparação, o modelo final foi definido ao ajustar a melhor abordagem.
On January 1st, 2018, the new financial instruments standard, IFRS 9 Financial Instruments, will turn mandatory. Converging 2007's crisis' needs for technical changes, its objective is to align accounting with risk management. One of the main adaptations is the new impairment model, which passes from "incurred losses" in IAS 39 to "expected losses" in IFRS 9. To make this transition forward-looking information must be incorporated in the estimations. In this case, the incorporation needed to be made for the Probabilities of Default, one of the variables used to calculate "expected losses". Therefore, our objective was to develop and validate a model, while leveraging previous work, to integrate macroeconomic projections in the estimations of the Probabilities of Default. To do so, two approaches were compared, with one being more technical while the other simpler and more practical. After the comparison, the final model was defined by adjusting the best approach.
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Hild, Andreas. "ESTIMATING AND EVALUATING THE PROBABILITY OF DEFAULT – A MACHINE LEARNING APPROACH." Thesis, Uppsala universitet, Statistiska institutionen, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-447385.
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In this thesis, we analyse and evaluate classification models for panel credit risk data. Variables are selected based on results from recursive feature elimination as well as economic reasoning where the probability of default is estimated. We employ several machine learning and statistical techniques and assess the performance of each model based on AUC, Brier score as well as the absolute mean difference between the predicted and the actual outcome, carried out with cross validation of four folds and extensive hyperparameter optimization. The LightGBM model had the best performance and many machine learning models showed a superior performance compared to traditional models like logistic regression. Hence, the results of this thesis show that machine learning models like gradient boosting models, neural networks and voting models have the capacity to challenge traditional statistical methods such as logistic regression within credit risk modelling.
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Gunnvald, Rickard. "Estimating Probability of Default Using Rating Migrations in Discrete and Continuous Time." Thesis, KTH, Matematisk statistik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-150428.
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During the financial crisis that began in 2008, even whole countries and very large companies defaulted or were on the verge of defaulting. The turmoil made risk managers and regulators more vigilant in scrutinising their risk assessment. The probability of default (PD) is an essential parameter in measuring counterparty credit risk, which in turn has impact on pricing of loans and derivatives. The last decade, a method using Markov chains to estimate rating migrations, migration matrices and PD has evolved to become an industry standard. In this thesis, a holistic approach to implementing this approach in discrete and continuous time is taken. The results show that an implementation in continuous time has many advantages. Also, it is indicated that a bootstrap method is preferred to calculate confidence intervals for the PDs. Moreover, an investigation show that the frequently used assumption of time-homogeneous migration matrices is most probably wrong. By studying expansions and recessions, specific expansion and recession migration matrices are calculated to mitigate the impact of time-inhomogeneity. The results indicate large differences of estimated PDs over the economic cycle, which is important knowledge to be able to quote correct prices for financial transactions involving counterparty credit risk.
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Mirzaei, Maryam [Verfasser]. "Corporate Probability Default Prediction With Industry Effects Using Data Mining Techniques / Maryam Mirzaei." Munich : GRIN Verlag, 2016. http://d-nb.info/1114737127/34.
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Hallblad, Jessica. "The Multi-year Through-the-cycle and Point-in-time Probability of Default." Thesis, Umeå universitet, Nationalekonomi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-102823.
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Coutinho, Cristina Fonseca. "Sovereign default probabilities within the european crisis." Master's thesis, Instituto Superior de Economia e Gestão, 2012. http://hdl.handle.net/10400.5/4955.
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Mestrado em Matemática FinanceiraIn this thesis we assess the real default probabilities of three groups of European sovereigns - peripheral, central and safe haven - in order to get a forward looking measure of the market sentiment about their default, as well as their evolution within the current European crisis. We follow Moody's CDS-implied EDF Credit Measures and Fair-Value Spreads methodology by extracting risk-neutral probabilities of default, assumed to be Weibull distributed, from CDS spreads and convert them into real probabilities of default, using an adaptation of the Merton model to remove the risk premium. We use CDS spreads data from 2008 to 2011 and country dependent market prices of risk as proxy for the risk premium based on the equity benchmark indices of each country. The obtained real default probabilities proved to be a suitable indicator to predict defaults according to the credit events. They have increased severely since 2009/2010, in particular for the peripheral economies - Greece, Ireland and Portugal. The Greece's 1-year probability of default reached 55% at the end of 2011 and a default took place in March 2012. These three countries had to request a bailout from the EU/IMF authorities, Greece and Ireland in 2010 and Portugal in April 2011. Spain and Italy, the central economies, have been a concern for investors, which is reected in their real probabilities of default that increased substantially during the second half of 2011. The safe haven sovereigns - Germany and France - were also not immune to the economic slowdown in Eurozone and its GDP started to shrink, however, the rise in the default probabilities was more limited.
Nesta tese apresentamos as probabilidades de incumprimento objectivas de três grupos de soberanos Europeus - periféricos, centrais e seguros - com o objectivo de captar antecipadamente o sentimento de mercado acerca dos mesmos, bem como analisar a evolução dessas probabilidades no contexto de crise europeia. Foi seguida a metodologia descrita em CDS-implied EDF Credit Measures and Fair-Value Spreads da Moody's, extraindo as probabilidades de incumprimento risco-neutrais, que se assume seguirem a distribuição Weibull, a partir dos preços dos CDS e convertendo-as em probabilidades de incumprimento objectivas, usando uma adaptação do modelo de Merton para expurgar o prémio de risco. Foram usados os preços dos CDS de 2008 a 2011 e os índices de Sharpe, variáveis com o país como proxy para o prémio de risco, baseados nos índices accionistas de referência de cada país. As probabilidades de incumprimento objectivas obtidas parecem ser indicadas para prever os incumprimentos de acordo com os acontecimentos reais. As probabilidades têm aumentado drasticamente desde 2009/2010, especialmente para os países periféricos - Grécia, Irlanda e Portugal. A probabilidade de incumprimento a um ano da Grécia era de 55% no final de 2011 e o incumprimento ocorreu efectivamente em Março de 2012. Estes três países tiveram de recorrer à ajuda financeira das autoridades União Europeia e do Fundo Monetário Internacional, a Grécia e a Irlanda em 2010 e Portugal em Abril de 2011. Espanha e Itália, as economias centrais, têm sido uma preocupação para os investidores, reflectida no aumento substancial das probabilidades de incumprimento no segundo semestre de 2011. Os soberanos seguros - Alemanha e França - também não ficaram imunes ao abrandamento económico na zona Euro e o seu PIB diminuiu, no entanto, o aumento das suas probabilidades de incumprimento foi mais limitado.
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Paiva, Francisco Salvador de. "Main drivers of bank default situations." Master's thesis, Instituto Superior de Economia e Gestão, 2014. http://hdl.handle.net/10400.5/7602.
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Mestrado em Economia Monetária e FinanceiraDesde a crise financeira internacional que se iniciou em 2007, que temos vindo a assistir a um número sem precedentes de instituições financeiras que tiveram de ser alvo de programas de resgate conduzidos por instituições públicas. Este trabalho tem como objectivo estudar as situações de incumprimento por parte dos bancos da zona euro que ocorreram entre 2001 e 2012, baseado numa base de dados de bancos listados em índices bolsistas dos próprios países. Este trabalho utiliza não só variáveis especificas financeiras inerentes aos próprios bancos mas também variáveis macroeconómicas que permitam analisar o ambiente macroeconómico onde a instituição se encontra inserida. Os principais resultados mostram que ambos os tipos de variáveis, financeiras especificas e macroeconómicas, são relevantes na percepção de eventos de incumprimento. A capitalização, a qualidade dos activos, a disciplina de mercado, a taxa de crescimento do Produto Interno Bruto e a taxa de inflação são as variáveis mais relevantes apresentadas pelos resultados das estimações efectuadas, e como tal, devem ser tomadas em conta em análises futuras sobre incumprimento de instituições financeiras.
Since the 2007's financial crisis we have witnessed an unprecedented number of financial institutions that either failed or had to be bailed out by the public sector. This work studies distress situations in financial institutions in the Euro area between 2001 and 2012, based on a data set with listed banks. The database is used to understand the main drivers for bank defaults, either financial (bank-specific) indicators, but also macroeconomic indicators are considered in the performed estimations. The main results show that both bank-specific and macroeconomic variables impact on default events. Capitalization, asset quality, market discipline, GDP growth rates and inflation rates are the most significant on the estimation results and should be taken into consideration when analyzing bank distress situations.
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Camargo, Gonzalo, and Mayko Camargo. "Country Risk: An Empirical Approach to Estirnate the Probability of Default in Emergent markets'." Economía, 2012. http://repositorio.pucp.edu.pe/index/handle/123456789/117844.
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In this paper we have suggested a new methodology to estimate the probability of defaultof a country as a function of other macroeconomics variables. Such methodologyis based in the valuation of the prices in the secondary market of bonds issued by debtorcountries. We have chosen the Brady bonds because their institutional characteristicsdo not depend on the issuer country, which allows us to build a homogeneouspanel. The methodology proposed takes elements of traditional models such as thefunctional structure of the probability and elernents of term structure models. The paperdemonstrates a new way to extract sovereign nsk, implicit in trade bond prices.En el presente trabajo, se sugiere una metodología nueva para estimar la probabilidadde que un país incumpla sus compromisos de pago de deuda. Dicha probabilidad seexpresa como función de distintas variables macroeconómicas. La metodología sebasa en valorar los precios en el mercado secundario de instrumentos de deuda (bonos)emitidos por dichos países. Los bonos elegidos han sido los Bradies, debido aque sus características institucionales son similares para distintos emisores. La metodologíapropuesta toma elementos de los modelos tradicionales, como la estructurafuncional de la probabilidad de impago y de los modelos de estructura de términos. Enresumen, este trabajo presenta una nueva manera de extraer el riesgo soberano quese encuentra implícito en los precios de los bonos elegidos en el mercado secundario.
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Antonsson, Hermina. "Macroeconomic factors in Probability of Default : A study applied to a Swedish credit portfolio." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-239403.
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Macroeconomic conditions can impact the payment capacity of individual mortgage holders' household loans. If the clients of a bank's retail credit portfolio experience deteriorating paymentcapacity it will reflect on the probability of default of the overall portfolio. With IFRS 9, banks are expected to sophisticate their calculations of expected credit loss, demanding forward-looking estimates of probability of default by incorporation of macroeconomic forecasts. Finding what macroeconomic factors have a statistical significant relationship to the actual default frequency of a portfolio can aid banks in estimating probability of default with reference to current and forecasted macroeconomic conditions. This study aims to explore the relationship between macroeconomic factors and the default frequency in a Swedish retail credit portfolio. The research is based on quantitative data analysis of historical default data, complemented by implications of the macroeconomic condition on the payment capacity of households from a theoretical perspective. Macroeconomic factors studied are the Swedish gross domestic product, house price index, reporate and unemployment rate. The supporting data consists of default data from Nordea's Swedishretail credit portfolio. The time period covers 2008-2015 and provides basis for analysis of a timeperiod with different conditions in the macroeconomy, including effects of the 2008 financial crisis. A multiple linear regression model is used as a method to suggest the relationship between themacroeconomic factors and the default frequency. The model coefficients are estimated with calculations of Ordinary Least Squares and the significance supported by statistical test. Results show that gross domestic product and repo rate are statistically significant macroeconomic variables in explaining changes in the default frequency and thus probability of default of a Swedish retail credit portfolio.Makroekonomiska omständigheter kan påverka hushållens betalningsförmåga och i sin tur återbetalningsförmågan hos bolånetagare. Om flertalet låntagare inom en banks retailportfölj upplever en försämrad betalningsförmåga kommer det att avspeglas på sannolikheten för fallissemang (probability of default) i den totala portföljen. Med IFRS 9 förväntas banker förfina sina beräkningar av förväntade kreditförluster, vilket kräver framåtblickande beräkningar av probability of default med makroekonomiska prognoser i åtanke. Genom att identifiera vilka makroekonomiska faktorer som har statistisk signifikans för förändringar i historisk fallissemangsfrekvens i en portfölj förväntas banker kunna integrera dessa i, och därmed förbättra, sina beräkningar av probability of default. Denna studie syftar till att utreda sambandet mellan makroekonomiska faktorer och fallissemangsfrekvensen i en svensk retailportfölj. Den kvantitativa analysen av data över historiska fallissemang och makroekonomiska faktorer kompletteras med teoretiska implikationer av makroekonomiska omständigheter för hushållens betalningsförmåga. De makroekonomiska faktorer som studeras är svensk BNP, Boprisindex, Reporänta och Arbetslöshet. Fallissemangsfrekvensen baseras på data från Nordeas svenska retailportfölj som täcker åren 2008-2015 och därmed inkluderar följdeffekter av finanskrisen 2008. En multipel linjär regressionsmodell används för att förklara relationen mellan de makroekonomiska faktorerna och fallissemangsfrekvensen. Regressionskoefficienterna estimeras med hjälp av minstakvadratmetoden och kompletteras med diagnostiska test. Resultaten visar att BNP och Reporäntan är statistiskt signifikanta makroekonomiska faktorer för påvisandet av förändringar i fallissemangsfrekvensen och följaktligen Probability of Default i en svensk retailkreditportfölj.
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Van, Breda Ryan. "Quantification of the default probability of the top 42 non-financial South African firms." Master's thesis, University of Cape Town, 2007. http://hdl.handle.net/11427/19041.
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The focus of this dissertation is to quantify the probability of firm default focusing on the top 42 non-financial firms listed on the Johannesburg Stock Exchange. This paper follows the same methodology as outlined in the Moody's KMV white papers in implementing the Merton (1974) model. The model of default prediction builds upon option theory as pioneered by Black and Scholes and derives the probability of default predominately from the price and volatility of equity. In addition, BEE (Black Economic Empowerment) transactions currently being experienced within the South African corporate sector are further incorporated into the model. The results of this dissertation show that the Merton (1974) model may be used as a source of information of the underlying credit risk of publicly traded firms in South Africa.
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Penha, Ricardo Miguel do Brito. "Default risk : analysis of a credit risk model." Master's thesis, Instituto Superior de Economia e Gestão, 2016. http://hdl.handle.net/10400.5/12902.
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Mestrado em Ciências ActuariaisUma 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.
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Jovanovic, Filip, and Paul Singh. "Modelling default probabilities: The classical vs. machine learning approach." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273570.
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Fintech companies that offer Buy Now, Pay Later products are heavily dependent on accurate default probability models. This is since the fintech companies bear the risk of customers not fulfilling their obligations. In order to minimize the losses incurred to customers defaulting several machine learning algorithms can be applied but in an era in which machine learning is gaining popularity, there is a vast amount of algorithms to select from. This thesis aims to address this issue by applying three fundamentally different machine learning algorithms in order to find the best algorithm according to a selection of chosen metrics such as ROCAUC and precision-recall AUC. The algorithms that were compared are Logistic Regression, Random Forest and CatBoost. All these algorithms were benchmarked against Klarna's current XGBoost model. The results indicated that the CatBoost model is the optimal one according to the main metric of comparison, the ROCAUC-score. The CatBoost model outperformed the Logistic Regression model by seven percentage points, the Random Forest model by three percentage points and the XGBoost model by one percentage point.Fintechbolag som erbjuder Köp Nu, Betala Senare-tjänster är starkt beroende av välfungerande fallissemangmodeller. Detta då dessa fintechbolag bär risken av att kunder inte betalar tillbaka sina krediter. För att minimera förlusterna som uppkommer när en kund inte betalar tillbaka finns flera olika maskininlärningsalgoritmer att applicera, men i dagens explosiva utveckling på maskininlärningsfronten finns det ett stort antal algoritmer att välja mellan. Denna avhandling ämnar att testa tre olika maskininlärningsalgoritmer för att fastställa vilken av dessa som presterar bäst sett till olika prestationsmått så som ROCAUC och precision-recall AUC. Algoritmerna som jämförs är Logistisk Regression, Random Forest och CatBoost. Samtliga algoritmers prestanda jämförs även med Klarnas nuvarande XGBoost-modell. Resultaten visar på att CatBoost-modellen är den mest optimala sett till det primära prestationsmåttet ROCAUC. CatBoost-modellen var överlägset bättre med sju procentenheter högre ROCAUC än Logistisk Regression, tre procentenheter högre ROCAUC än Random Forest och en procentenhet högre ROCAUC än Klarnas nuvarande XGBoost-modell
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Järnberg, Emelie. "Dynamic Credit Models : An analysis using Monte Carlo methods and variance reduction techniques." Thesis, KTH, Matematisk statistik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-197322.
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In this thesis, the credit worthiness of a company is modelled using a stochastic process. Two credit models are considered; Merton's model, which models the value of a firm's assets using geometric Brownian motion, and the distance to default model, which is driven by a two factor jump diffusion process. The probability of default and the default time are simulated using Monte Carlo and the number of scenarios needed to obtain convergence in the simulations is investigated. The simulations are performed using the probability matrix method (PMM), which means that a transition probability matrix describing the process is created and used for the simulations. Besides this, two variance reduction techniques are investigated; importance sampling and antithetic variates.I den här uppsatsen modelleras kreditvärdigheten hos ett företag med hjälp av en stokastisk process. Två kreditmodeller betraktas; Merton's modell, som modellerar värdet av ett företags tillgångar med geometrisk Brownsk rörelse, och "distance to default", som drivs av en två-dimensionell stokastisk process med både diffusion och hopp. Sannolikheten för konkurs och den förväntade tidpunkten för konkurs simuleras med hjälp av Monte Carlo och antalet scenarion som behövs för konvergens i simuleringarna undersöks. Vid simuleringen används metoden "probability matrix method", där en övergångssannolikhetsmatris som beskriver processen används. Dessutom undersöks två metoder för variansreducering; viktad simulering (importance sampling) och antitetiska variabler (antithetic variates).
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Mustafa, Khalil, and Victor Persson. "Credit Risk Model for loans to SMEs in Sweden : Calculating Probability of Default for SMEs in Sweden based on historical data, to estimate a financial institution’s risk exposure." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-137317.
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As a consequence from the last financial crisis that began 2007 in USA, regulatory frameworks are continuously improved in order to limit the banks’ risk exposure. Two of the amendments are Basel III and IFRS 9. Basel III regulates the capital a bank is required to hold while IFRS 9 is an accounting standard for how banks and insurance companies should classify their assets and estimate their future credit losses. Mutually for both Basel III and IFRS 9 is the estimation of future credit losses which include probability of default in the calculations.The objective of this thesis was therefore to develop scoring model that can estimate the probability of default in lending capital to enterprises based on information from financial statements. The aim is that the developed model also can be used in the daily operations to reduce fixed costs by optimizing the processes and increase the profit on each loan issued. The model should estimate probability of default within 500 days from the last known information and be customized for small and medium size enterprises.The model is based on logistic regression and is therefore returning values between 0 and 1. Parameters that the model consists of can either be calculated or retrieved directly from financial statements. The authors have during the development of the model divided the data, consisting of information from enterprises, based on branches. The grouping of data has been performed to create as homogenous sets of data as possible in order to increase the degree of explanation for each model. The final solution will thus consist of several models, one for each set of data. The validation of the models is performed, on a new set of enterprises where it is observed how well the models can discriminate enterprises defined as defaults from non-defaults.The master thesis did result in a number of models that are calibrated on default, non-defaults and models developed on data divided on branches. By using the calibrated models, it is possible to discriminate defaulting from non-defaulting enterprises which has been the objective of this thesis. During the project the importance of dividing data into homogenous groups has been shown in order to better create models that more accurately can identify defaults from non-defaults.Som en konsekvens av finanskrisen som började 2007 i USA tillkom ytterligare regelverk för att minimera bankers riskexponering. Två av de regelverk som tillkommit är Basel III och IFRS 9. Basel III reglerar kapitaltäckningen för en bank medan IFRS 9 är en standard för hur banker och försäkringsbolag skall klassificera tillgångar samt estimera framtida kreditförluster. Gemensamt för de båda regelverken är estimeringen av kreditförluster som bland annat baseras på risken för fallissemang.Målet med detta examensarbete är därför att utveckla en scoringmodell som kan estimera risken för fallissemang vid utlåning till företag baserat på information från dess årsredovisningar. Modellen kommer även kunna användas i den operativa verksamheten för att reducera fasta kostnaderna genom att effektivisera processer och då öka avkastningen på varje utlånad krona. Modellen kommer att estimera risken för fallissemang inom 500 dagar från senast kända informationen och den kommer att anpassas till svenska små och medelstora företag.Modellen är baserad på logistisk regression och kommer därför att returnera värden mellan 0 och 1 samt bestå av parametrar som antingen kan beräknas eller hämtas direkt ur en årsredovisning. För att öka modellens förklaringsgrad har författarna vid kalibreringen av modellerna delat in datat efter branscher. Uppdelningen har gjorts för att skapa så homogena grupper som möjligt och lösningen kommer därför att bestå av flera olika modeller. Validering av modellerna sker genom att på nytt data testa hur bra företag som definierats som fallissemang kan diskrimineras från företag som inte definieras som fallissemang.Rapporten resulterar i ett antal modeller som är baserade på konkurser, icke konkurser samt modeller baserade på ett data som är uppdelat på branscher. Genom att använda de kalibrerade modellerna så går det att diskriminera konkurser från icke konkurser vilket varit målet med denna rapport. Arbetet har också påvisat vikten av att dela in datat i homogena grupper för att på ett bättre sätt skapa modeller som mer exakt kan urskilja konkurser från icke konkurser.
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Martins, Joana Sofia Luís. "Credit risk of financial institutions." Master's thesis, NSBE - UNL, 2014. http://hdl.handle.net/10362/11692.
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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 EconomicsAlthough 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.
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Howard, Scott T. "Optimal Interest Rate for a Borrower with Estimated Default and Prepayment Risk." Diss., CLICK HERE for online access, 2008. http://contentdm.lib.byu.edu/ETD/image/etd2400.pdf.
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Botelho, Rodrigo Azevedo de Castro. "Estudo sobre o efeito de variáveis macro econômico e do spread de credit default swap no risco de evento de crédito soberano." reponame:Repositório Institucional do FGV, 2012. http://hdl.handle.net/10438/10459.
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This paper explores the sovereign default due to the structure of Credit Default Swap spreads. These spreads show the default probability of a country. The methodology proposed in this paper applied for Argentina, Korea, Ecuador, Indonesia, Mexico, Peru, Turkey, Ukraine, Venezuela and Rússia. We could show that a single factor model following a lognormal process captures the probability of default. We also show that the macro economic variables like inflation, unemployment e growth do not explain the dependent variable of this study. Each country responds differently to the economic crisis that leads to don’t honor their commitments debts.
Este trabalho explora a realização de default soberano em função da estrutura de spreads de CDS (Credit Default Swap). Pode-se dizer que os spreads revelam a probabilidade de default de um país. Aplicamos a metodologia proposta neste trabalho para Argentina, Coreia, Equador, Indonésia, México, Peru, Turquia, Ucrânia, Venezuela e Rússia. Nós mostramos que um modelo de um único fator seguindo um processo lognormal captura a probabilidade de default. Também mostramos que as variáveis macro econômicas inflação, desemprego e crescimento não explicam a variável dependente do estudo (probabilidade de default). Cada país reage de maneira diferente a crise econômica que a leva a não honrar seus compromissos com as dívidas contraídas.
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Granström, Daria, and Johan Abrahamsson. "Loan Default Prediction using Supervised Machine Learning Algorithms." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252312.
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It is essential for a bank to estimate the credit risk it carries and the magnitude of exposure it has in case of non-performing customers. Estimation of this kind of risk has been done by statistical methods through decades and with respect to recent development in the field of machine learning, there has been an interest in investigating if machine learning techniques can perform better quantification of the risk. The aim of this thesis is to examine which method from a chosen set of machine learning techniques exhibits the best performance in default prediction with regards to chosen model evaluation parameters. The investigated techniques were Logistic Regression, Random Forest, Decision Tree, AdaBoost, XGBoost, Artificial Neural Network and Support Vector Machine. An oversampling technique called SMOTE was implemented in order to treat the imbalance between classes for the response variable. The results showed that XGBoost without implementation of SMOTE obtained the best result with respect to the chosen model evaluation metric.Det är nödvändigt för en bank att ha en bra uppskattning på hur stor risk den bär med avseende på kunders fallissemang. Olika statistiska metoder har använts för att estimera denna risk, men med den nuvarande utvecklingen inom maskininlärningsområdet har det väckt ett intesse att utforska om maskininlärningsmetoder kan förbättra kvaliteten på riskuppskattningen. Syftet med denna avhandling är att undersöka vilken metod av de implementerade maskininlärningsmetoderna presterar bäst för modellering av fallissemangprediktion med avseende på valda modelvaldieringsparametrar. De implementerade metoderna var Logistisk Regression, Random Forest, Decision Tree, AdaBoost, XGBoost, Artificiella neurala nätverk och Stödvektormaskin. En översamplingsteknik, SMOTE, användes för att behandla obalansen i klassfördelningen för svarsvariabeln. Resultatet blev följande: XGBoost utan implementering av SMOTE visade bäst resultat med avseende på den valda metriken.
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Okubo, Hitoshi, Fuminobu Shimizu, and Naoki Hayakawa. "Estimation of partial discharge inception voltage of magnet wires under inverter surge voltage by volume-time theory." IEEE, 2012. http://hdl.handle.net/2237/20735.
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Anar, Hatice. "Credit Risk Modeling And Credit Default Swap Pricing Under Variance Gamma Process." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/3/12609840/index.pdf.
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In this thesis, the structural model in credit risk and the credit derivatives is studied under both Black-Scholes setting and Variance Gamma (VG) setting. Using a Variance Gamma process, the distribution of the firm value process becomes asymmetric and leptokurtic. Also, the jump structure of VG processes
allows random default times of the reference entities. Among structural models, the most emphasis is made on the Black-Cox model by building a relation between the survival probabilities of the Black-Cox model and the value of a binary down and out barrier option. The survival probabilities under VG setting are
calculated via a Partial Integro Differential Equation (PIDE). Some applications of binary down and out barrier options, default probabilities and Credit Default Swap par spreads are also illustrated in this study.
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Silva, Daniel António Rego da. "Rating Masterscale : diferenciação por segmento : Large Corporate." Master's thesis, 2014. http://hdl.handle.net/10400.14/19396.
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Em momento anterior ao Millennium bcp (MBCP) – organização acolhedora
do estágio curricular – conceder um crédito, o risco de crédito dos seus Clientes
é classificado com base numa única rating masterscale, não havendo
diferenciação entre diferentes segmentos de risco.
Para colmatar este desajuste, o MBCP identificou as seguintes questões: Q1 –
Como diferenciar a rating masterscale para o segmento Large Corporate? – e Q2 –
Qual o impacto da nova rating masterscale no capital do MBCP? Estas questões
constituem o objeto do presente Trabalho Final de Mestrado (TFM).
Para responder a Q1, recorreu-se ao princípio da estimativa mais prudente
[Pluto e Tasche (2005)], tendo-se chegado a uma nova escala de risco, LCRM
(Large Corporate Rating Masterscale) 4, específica do segmento Large Corporate,
alargando-se o número de Graus de Risco (GR) e reajustando-se a Probability of
Default (PD) de cada GR.
Usando a LCRM 4 para se responder a Q2, analisaram-se inicialmente duas
empresas Large Corporate e, posteriormente, fez-se uma abordagem global com
recurso a uma proxy do portfólio Large Corporate para se comparar a evolução
do Risk Weighted Asset (RWA) e da Expected Loss (EL). Chegou-se, deste modo, à
conclusão que o impacto de LCRM 4 no capital do MBCP é positivo, já que se
consegue uma redução de cerca de 27 % no RWA e uma redução de cerca de
28 % na EL para a carteira de Clientes Large Corporate.Before then Millennium bcp (MBCP) – welcoming traineeship organization – grants credit, the credit risk of its customers is classified by a single rating masterscale, with no differentiation between different risk segments. To address this imbalance, MBCP identified the following questions: Q1 – How to differentiate the rating masterscale for the Large Corporate segment? – and Q2 – What is the impact of the new rating masterscale in the MBCP capital? These issues are the subject of this dissertation. To answer Q1, the most prudent estimation principle [Pluto and Tasche (2005)] was resorted, to reach a new LCRM (Large Corporate Rating Masterscale) 4 risk scale, specific to the Large Corporate segment, widening the number of risk degrees and readjusting the Probability of Default (PD) of each risk degree. Using LCRM 4 to answer Q2, two Large Corporate companies were initially analyzed and subsequently a Large Corporate portfolio proxy was made to compare the evolution of Risk Weighted Asset (RWA) and Expected Loss (EL). Thus, a conclusion has been reached that the impact of LCRM 4 in the MBCP capital is positive, once there is a reduction of about 27% in the RWA and a reduction of about 28% in the EL for the Large Corporate customers portfolio.
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Pinto, Rodolfo da Fonseca Pignatelli Soares Varela. "Aplicação do conceito de perfil de risco de crédito na análise dos sistemas bancários." Doctoral thesis, 2012. http://hdl.handle.net/10400.5/13450.
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Doutoramento em EconomiaThe dissertation is focused on the domain of financial stability, then addressing the perspective of the credit risk of default. The quantification of this risk in ensured through the estimations of the parameter Probability of Default (PD), as established in Basel 2. Regarding these estimations, it is presented a proposal of a structured framework of quantitative validation procedures, covering in detail the validation of time series, discriminatory power, calibration and stability of rating systems. The contribution of this thesis results, also, from the presentation of three new conceptions based on the exploration of the concept of credit risk profile, including the respective empirical analyses: Forward-looking credit risk pressure indicators; Anticyclical minimum capital ratios, variable depending on the credit risk profile of banks, complemented by a countercyclical incentives scheme; Sensitivity analyses to assess the impact on banks‟ solvency of a deterioration of risk weighted assets (RWA) and expected losses (EL), caused by a shock on the probability and level of default of credit portfolios. To conclude, we emphasize the advantages of investing in collecting and exploring information on banks‟ credit risk portfolios profiles, in order to, namely, developing new tools that may contribute to achieve the goal of financial stability.
A presente investigação enquadra-se no domínio da estabilidade financeira, focando a perspectiva do risco de incumprimento associado ao crédito. A quantificação deste risco é concretizada através das estimativas do parâmetro Probabilidade de Incumprimento (PD), estabelecido por Basileia 2. Sobre estas estimativas, é apresentada uma proposta de estrutura de procedimentos de validação, com especial incidência em séries históricas, poder discriminante, calibração e estabilidade dos sistemas de notação. O contributo científico do estudo resulta, ainda, da apresentação de três novas concepções baseadas na exploração do conceito de perfil de risco de crédito, incluindo as análises empíricas correspondentes: Indicadores avançados sobre a pressão do risco de crédito; Rácios mínimos de capital anticíclicos, variáveis em função do perfil de risco de crédito dos bancos, complementados por um esquema de incentivos contracíclicos; Análises de sensibilidade para avaliação do impacto na solvabilidade do agravamento dos activos ponderados pelo risco (RWA) e das perdas esperadas (EL), devido a choques na probabilidade e nos níveis de incumprimento das carteiras de crédito. Como conclusão sublinham-se as vantagens do investimento em informação sobre os perfis de risco das carteiras de crédito dos bancos, com vista, nomeadamente, ao desenvolvimento de novos meios que contribuam para a prossecução do objectivo de estabilidade financeira.
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Li, Huei-Ting, and 李惠婷. "Default Probability Analysis of Basket CLN." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/67999639963046428095.
Full textAbstract:
碩士國立高雄第一科技大學
金融營運所
97
This thesis focused on the relevance between baskets of the credit default rate of credit linked notes and credit default relation of underlying bond. We use Brace, Gatarek and Musiela (BGM, 1997) to model the long-term interest rate tree, and utilize the evaluation formula of credit linked note to infer the invisible credit default rate of the basket of credit linked notes in release date. There are 288 of the credit default rate of the basket of credit linked notes, 10 industries of underlying bonds, and 24 of portfolio bonds to investigate in this thesis. Due to the discrepancy of the credit default relation in different portfolio bonds, we can link the credit default relation of portfolio bonds and the credit default rate of CDS together. The thesis use the model of Credit Metrics, KMV to assume the relationship of the rate of stock return instead of the credit default relation of debt financing company because the data of the credit default relation of portfolio bonds cannot be find. The thesis classified the credit linked notes into two parts: one is first to basket CLN, which means the credit linked notes break the contract when one of the investment combinations breaks the contract. The second type is Second to basket CLN, refers to that the contract will be break if there are two of the investment combinations break the contract at the same time. Empirical results showed it related to the negative correlations between the credit default relation of the bonds group which based on dual bonds and the credit default of the basket of the credit linked notes, and the credit default rate of the basket of the credit linked notes is lower than first to basket CLN. We allocated 288 investment combination by industry department after the first step of the classified, and found out that the credit default relation of the industry and industrial department has 56 percents are more than 0.3, so that the thesis would have industrial department in investment combination to decrease the credit default rate of credit linked notes.
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Chuang, Fu-chiao, and 莊富喬. "A Study for Implied Probability of Default on Credit Default Swaps." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/32768158581445362728.
Full textAbstract:
碩士雲林科技大學
財務金融系碩士班
98
The crisis of subprime mortgage, the hyperinflation of oil and the raid of The Financial Tsunami have confirmed the investors will face increasingly credit risks in the financial market. When the scale of credit default swap ( CDS ) market inflated sharply, the default events happen suddenly beyond the anticipations of investors. The world economy is struck by the default events. Consequently, it is important problem how to calculate default probabilities accurately. This article investigates the implied default probabilities of credit default swap (CDS). We respectively apply approaches of Bystrom ( 2005 ) and Martin, Thompson and Browne ( 2001 ) to estimate the default probabilities with the market data of iTraxx Europe CDS index. In result, there are only small differences in default probabilities between two approaches. Additionally, the default probabilities of the final maturity to estimate theoretical spreads of CDS can provide information to CDS investors.
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Lu, Chien-Wei, and 呂建緯. "Default Risk Probability of Collateralized Debt Obligation." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/15120308343440789146.
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碩士國立臺灣大學
國際企業學研究所
94
Asset Securitization was formed at 1970. The enterprises and financial institutions group assets which can bring cash flow, and then issue securities to investors in the market. Collateralized Debt Obligation is a credit risk product backed by a pool of debt obligation. They create securities or classes of securities from a portfolio of debt instruments. When the underlying assets are bonds, the CDO is referred to as a collateralized bond obligation, CBO. When the underlying assets are loans, the CDO is referred to as a collateralized loan obligation, CLO. The assets in CDO come from different industries and credit levels. As a result, we have to capture real characteristics of assets to measure risk accurately. That includes individual default rate, recovery rate and default correlations between assets. There are many methods to evaluate Collateralized Debt Obligation in the market, for example, Moody’s Binomial Expansion Technique, Infectious model, Copula method, etc. The article focuses mainly on the lack of Binomial Expansion Technique and Infectious Model and makes use of Poisson model with common shocks by Cossette and Marceau[2000].We bring assets correlations and time factors to the model. Finally, we analyze the default probabilities of assets under different credit level and time factors
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Tai, Hsien-Chu, and 戴仙珠. "A Dynamic Default Probability Analysis of CDS Index." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/83161845088237680092.
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碩士國立高雄第一科技大學
金融營運所
97
We use Black, Gatarek and Musiela (1997) model to construct the term structure of interest rate. We also assume the risky assets will be default or not default at each period, and we can estimate each default rate of different period. By considering the interest rate process and the default status, we can value a credit default swaps index (CDX). In virtue of the value of a CDX contract is zero at issuance, when we give the market price of the CDX, we can find out the implied default probability of CDX. Because a CDX contract involves a number of reference entities, we also consider the different default status of the reference entities to find out the implied default probability of CDX. Our study shows that implied default probability is positive correlated with the CDX spread, which means the default probability increase as the CDX spread increase. We also find that a great decrease in First-to-Default Basket CDX when the interest rate of the market with a great decrease, and the conclusions fit in with the economical viewpoint.
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Chen, Shu-Jane, and 陳淑真. "Default Probability Estimation through a Barrier Option Framework." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/z2rbpd.
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碩士銘傳大學
財務金融學系碩士班
93
This paper constructs a default risk model based on a barrier option pricing framework. The market data of the construction industry in TAIEX are used to test the performance of the default risk model. Within our default risk model, the equity will be regarded as a down-and-out call option (DOC), which means that a firm will be seemed as default when its asset value goes below the barrier level. Our empirical results show that a positive the barrier level exists significantly, implying that the DOC framework is suitable for the measurement of default risk. Meanwhile, our default risk model demonstrates a superior early-warning ability, and it is still a better measure for default forecasting when comparing with Altman’s Z-Score and Zeta. Besides, we apply this default risk model to examine the relationship between default risk and stock returns.
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Kuo, Chih-min, and 郭智民. "An Analysis of Default Probability in CreditGrades Model." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/39727890614716613923.
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碩士東吳大學
財務工程與精算數學系
99
Since 2007 subprime mortgage crisis has been, events of default occur in many companies, so credit risk become an important topic. CreditGrades model is the credit risk model by Finger et al. (2002) proposed, and the purpose is to measure default probability of corporate, in CreditGrades model assumes that recovery rate follow the lognormal distribution, but this way may be make the recovery rate greater than 1. In this article, we will continue the structure of CreditGrades model, and propose the modified model assum that recovery rate follow the runcated lognormal distribution, it will guarantee that recovery rate can not greater than 1, it lead to that the modified model of CreditGrades model be more reasonable. This article also calculate the default probability of corporate under the modified model of CreditGrades model, and compare two models under the recovery rate in different parameters.
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Rao, An-xi, and 饒安喜. "Estimate probability of default in public offer company." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/3edu35.
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碩士東吳大學
經濟學系
93
The purpose of this paper use Logistic regression model to estimate probability of default on company. In the data set, we collect 31 financial distrerss and 31 normal company’s finance data and TCRI credit ratting in 2001 to 2004 from the database of Taiwan Finance Database of Taiwan Economic Journal . First, in this study, we issue some hypothesis about Pre-Recognization of company financial distrerss. There provide four hypothesis : (1) in the bad financial structure, the financial distrerss company is more distrerss then normal company. (2) on the low liquidity level, financial distrerss company is more distrerss then normal company . (3) if the company get bad cash-flow, then the financial distrerss company is more distrerss then normal company. (4) the financial distrerss company is less credit ratting then normal company. The result show that the hypothesis(1),(3),(4) is true. Second, estimating probability of default on company , we find (1) To estimate probability of default on normal company, normal company sometimes is higher then the financial distrerss company. (2) Macroeconomic variability will make effect for probability of default of company. (3) In the lower probability of default, the financial distrerss company’s has times higher default then normal company.
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Lee, Chia-Tien, and 李佳恬. "Estimation of Probability of Default Using Bayesian Approach." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/25h97f.
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碩士東吳大學
商用數學系
96
I. A Bayesian Approach for Probability of Default under One Factor Model Abstract Quantitative rating systems are progressively being used for the purposes of pricing credits and capital allocation. However, historic default data may not sufficiently reliable. Thus, it is important to validate the accuracy of the probability of default (PD) estimated by the rating system. To incorporate expert opinion or macroeconomic phenomenon, in this paper, we derive the posterior distribution of PD through Bayesian approach under one factor model. We provide a process and practical demonstration to facilitate risk assessment in the absence of sufficient historic default data or even in the circumstance of zero default observation and historic default data from Standard & Poor’s. Given various types of Beta prior distribution of PD (in which each represents a specific expert opinion), it allows us to determine the posterior distribution of PD, and thereby provide a framework in establishing the upper bound for a PD in association with a low default portfolio. In all, by focusing on different prior distribution of PD, this paper provides valuable insight on the effects of estimated posterior distribution of PD and its corresponding upper bound for various levels of correlation and number of obligors. II. Confidence Intervals for Probability of Default via MCMC Abstract Due to the New Basel Capital Accord, financial institutions strengthen the credit risk of management. Financial institutions would like to set up a complete system to be regarded as the best model for pricing credits and capital allocation. For these purpose, estimating the accuracy of the probability of default (PD) is a very important issue. We provide the empirical study using historic default rates from Standard & Poor’s data on U.S firms in rating category. In the one factor model framework, a Bayesian approach using Markov chain Monte Carlo method obtains the posterior distribution of PD and asset correlation in the credit portfolio. In view of the low default portfolio, we could calculate the confidence intervals of PDs and asset correlations from the posterior distribution in each rating category. Special emphasis given various types of prior distribution of PD, we could observe the influence of prior distribution of PD for the investment grade on the posterior distribution of PD and asset correlation in each rating category.
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Chen, Yi-Jyun, and 陳怡均. "Default Probability Estimation by Using Exponential State Space Model." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/19597029195865080733.
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碩士東吳大學
財務工程與精算數學系
101
Over the years, credit rating companies provides the ratings to the market as a sign of economic cycle. After the financial tsunami, the result of default on credit rating information and market influence is more obvious. In this thesis, I use exponential family state space model to describe the default rate, and estimate probability of default and its potential impact factor by using Kalman filter, Kalman smoother, importance sampling method and maximum likelihood estimation method. In the empirical study, I use the report from S&P (Standard Poor's, S&P), “2012 Annual Global Corporate Default Study and Rating Transitions.”, to estimate parameters in the model. The empirical results show that default probability after transforming by state space model is broadly in line with the actual default rates. The potential impact factor of default rate can also be expressed by number. In addition, this paper also uses default data obtained by simulation under different parameters setup. My simulation results show using the exponential family state space model can effectively describe the market of default.
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CHEN, CHIEN-LIANG, and 陳建良. "Probability of Default and Creidt Risk Management of Banks." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/88480249273556235536.
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Lin, Ting-Chun, and 林亭均. "The impact of institutional factors on probability of default." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/k7wk9a.
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碩士元智大學
財務金融暨會計碩士班(財務金融學程)
106
The thesis studies the impact of institutional factors on probability of default (PD). I mainly discuss the effect on the level of countries’ judicial impartiality, adopting the International Financial Reporting Standards (IFRS) or not, member or non-member of Organization for Economic Cooperation and Development (OECD), the countries’ corruption perception index (CPI) and the percentage of state-operated enterprises (SOE). In this thesis, I measure the probability of default by Merton (1974) option-pricing theories, and there are 313,469 firm-year observations from 46 countries over the period 1995 to 2015. In the result, I find that countries whose citizens have stronger faith on judicial system keep lower PD, countries adopt IFRS have lower PD, with higher corruption degree countries cause the higher PD, OECD members have relatively stable financial structure lead to the lower PD and countries with the lower degree of enterprises keep lower PD.
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黃偉倫. "A Note on the Default Probability with Price Limits." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/44537512237302121172.
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Lin, Hsiu-Ho, and 林修禾. "On study of the recovery rate via the credit default swap spread and default probability." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/8sy634.
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碩士國立東華大學
應用數學系
106
We use a discrete-time default model to obtain the model-based theoretical estimators of the credit default swap spread for one-year, three-year, and five-year contracts. Due to that those estimators include an unknown recovery rate, we combine the daily credit default swap spreads with model theoretical estimators and apply the least square method to estimate the recovery rate. To validate this proposed method, we collect 12 companies from the Datastream database and the Credit Research Initiative (CRI) database of Risk Management Institute in the National University of Singapore to estimate recovery rates. We compare the estimated recovery rates of the 12 companies with the recovery rates collected from Moody's Default & Recovery database (DRD). The empirical results show that the estimated recovery rate based on the five-year credit default swap contract is closer to actual recovery value.
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Chu, Tzu-Lin, and 朱賜麟. "Effectiveness of the Default-Corpus from Linguistic Data Mining on the Prediction of Corporate Default Probability." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/r6rv3g.
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碩士銘傳大學
財務金融學系碩士班
97
This paper applies the linguistic data mining technique to extract relevant information from financial news. The Chinese corpuses for financial crisis and distress abstracted from our mining process are quantified through text corpus analysis, keywords frequency analysis, entropy estimation and default intensity analysis. The Chinese corpuses for financial crisis and distress are then transformed into a measure concerning the intensity to financial distress. We put such a distress-corpus variable (intensity of default-corpus, ITDC) together with other variables about financial structure, corporation governance, treatment variable, and macroeconomic variables into logistic regression to investigate whether news plays an important role in improving the financial warning capability. The empirical results show that ITDC improve the explanation power of each financial distress model regardless of what independent variables are incorporated. For the prediction effectiveness, the results show that ITDC significantly contribute to reduce the type I error and improve identification accuracy of financial distress. The one-quarter ahead forecast presents the minimum type I error (13.33%) and largest identification accuracy (89.35%). Our results prove that intensity of default-corpus variable from linguistic data mining of Chinese financial news does improve the effectiveness of the prediction of corporate default probability.
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Pan, Chiu-Mei, and 潘秋梅. "Forecasting probability of default of corporate using Logistic Regression Model." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/15420979017908989755.
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碩士國立高雄應用科技大學
金融資訊研究所
95
ABSTRACT The corporate default risk is a major composition of credit risk. Bank management have more concerned with corporate default risk management to abate business risk and enhance stockholders’ equity. Taiwan belongs to a high credit risk financial market, the great part of bank official according to the at least requirement of regulation to make loan provision. It may cause too low coverage rate than other country, hence it is most important for bank management to reinforce forecasting’s accuracy of credit risk. In this paper, we propose a model to achieve efficient and feasible to predict probability of default of corporate of credit risk based on Binary Logistic Regression Model. This model will produce a risk signal to advise bank management to make a right decision. This paper data from Taiwan Economic Journal Database, from December of 1996 to June of 2006 in season’s the financial variables, the external rating variables, company information variables, the accountant's variables, the macroeconomic variables and the corporate governance variables on the general industry of TSE and OTC of Taiwan, the way of mating by1:1 of the normal company and the default company in the same industry, the same data period and similar asset size, forecasting correct rates use Logistic Regression Model. The major research findings include：forecasting accuracy are high for the financial variables model and synthesize variables model, the effect valuation-Receiver Operating Characteristic and Kolmogorov-Smirnov Test those confirmed are suitable of correct rates of model. This paper make a contribution to choose significant variables and build a predict corporate default risk model accurately , but it is to be short of Override variables, also it has not used the merge financial reports, and segment credit rating grades of corporate loan by probability of default yet, It is satisfy risk management requirement for bank to suggest the future researcher improve the model refer to the banker opinion, outside credit rating and BaselⅡ. Key word：Probability of Default of Corporate、Credit Risk、Logistic Regression
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Lu, Kun-Hui, and 盧昆輝. "Calibration of Default Probability in Credit Risk with Normal Test." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/85149631901149942548.
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碩士東吳大學
商用數學系
95
The New Basel Capital Accord allows that banks can develop models for estimating default probabilities and other parameters to calculate capital requirements, so calibration of default probability is quite important for banks and regulator. This thesis divides into two papers, the first paper introduces the method on default probability calibration provided by The New Basel Capital Accord: normal test, using it to test forecasted default probability, and simulating type I error and type II error with different asset correlations in testing through the simulation method provided by Basel. Due to the unideal result provided by normal test, the second paper introduces a revised method of normal test that includes asset correlation, besides, this paper use two statistic methods, saddlepoint approximation and Cornish-Fisher expansion to test forecasted default probability, so there are 3 revised test methods totally. Because that revised methods include estimated asset correlation, but the method of estimating asset correlation do not discussed in this paper, so we compare type I error and type II error of these methods (including original normal test, 4 methods totally) under different true asset correlation where each situation given several estimating values .
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Wu, Chia-Pin, and 吳佳萍. "A Study on Default Probability, Asset Correlation and Firm Scale." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/28688062798283167628.
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碩士朝陽科技大學
財務金融系碩士班
94
The Basel Accord is always the principle and basis of international banks implement the internal financial administration. Owing to the global financial structure change, Basel I Accord already can’t fit in with nowadays financial environment, so touch on the New Basel II Accord advent. Based on the Basel II Accord, it got three pillars such as minimum capital requirement, supervisory review process and market disciplines. For the moment, the researches on the New Basel II Accord, almost focus on how to implement the system and how can it be created for different environments, but minor further study on the calculation of Minimum Capital Requirement. For this reason, this paper try to use Option Pricing Model and Standard & Poor’s credit ratings for adjusting the KMV Model, to calculate the default probability of domestic and abroad listed company, respectively. Afterward, I put default probability of domestic and abroad value to substitute asset correlation formula for the calculation of asset correlation. Finally, I consider the firm scale factor simultaneously. Through the empirical study, it shows that the firm scale and asset correlation is positive at domestic information electronic industry. With regard to default probability, asset correlation and firm scale, neither the samples of domestic nor abroad is verified. But if only consider the default probability, the relationships among these three variables is significant.
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Wu, I.-Shin, and 吳宜欣. "The discussion about the default probability under the dynamical model." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/17551259989651239767.
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碩士國立臺灣大學
數學研究所
98
This thesis revises the model proposed by Hull and White (2008). We discuss the errors between the simulation data and the market data under the different assumptions of the hazard rate. We find as time goes by, the structure of the hazard rate rises firstly and declines finally is better than the constant traditionally. The result is consistent with the outcome we obtain form the Moody’s cumulative default probability. On the other hand, the jump is the important characteristic of the dynamical model, so we compare the simulation data between the dynamical model with jump and the dynamical model without jump. We find the dynamical model without jump has the low-estimated probability which absorb higher loss. Finally, we discuss the properties of the probability distribution of the number of the defaults, and use the chi-square distribution to approximate it.