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Azevedo, José Henrique Sousa de. "Macroeconomics determinants of loss given default." Master's thesis, Instituto Superior de Economia e Gestão, 2015. http://hdl.handle.net/10400.5/10719.
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Mestrado em FinançasEsta dissertação modeliza a base de dados Moody's Ultimate Recovery Database, concluindo que o ambiente macroeconómico influencia o loss given default (LGD)e que as taxas de recuperação no crédito concedido são menos susceptíveis a serem influenciadas pelas condicionantes macroeconómicas do que as taxas de recuperação das obrigações. A metodologia econométrica tem por base a regressão OLS. São também discutidas outras metodologias passíveis de serem utilizadas.
This dissertation models Moody's Ultimate Recovery Database to show that general macroeconomic conditions influence loss given default and that loans' recovery rates are less susceptible to macroeconomic conditions than bonds'. Available data was studied with Ordinary Least Squares regressions. Alternative methodologies are also discussed.
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Yao, Xiao. "Modelling loss given default of corporate bonds and bank loans." Thesis, University of Edinburgh, 2015. http://hdl.handle.net/1842/26020.
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Loss given default (LGD) modelling has become increasingly important for banks as they are required to comply with the Basel Accords for their internal computations of economic capital. Banks and financial institutions are encouraged to develop separate models for different types of products. In this thesis we apply and improve several new algorithms including support vector machine (SVM) techniques and mixed effects models to predict LGD for both corporate bonds and retail loans. SVM techniques are known to be powerful for classification problems and have been successfully applied to credit scoring and rating business. We improve the support vector regression models by modifying the SVR model to account for heterogeneity of bond seniorities to increase the predictive accuracy of LGD. We find the proposed improved versions of support vector regression techniques outperform other methods significantly at the aggregated level, and the support vector regression methods demonstrate significantly better predictive abilities compared with the other statistical models at the segmented level. To further investigate the impacts of unobservable firm heterogeneity on modelling recovery rates of corporate bonds a mixed effects model is considered, and we find that an obligor-varying linear factor model presents significant improvements in explaining the variations of recovery rates with a remarkably high intra-class correlation being observed. Our study emphasizes that the inclusion of an obligor-varying random effect term has effectively explained the unobservable firm level information shared by instruments of the same issuer. At last we incorporate the SVM techniques into a two-stage modelling framework to predict recovery rates of credit cards. The two-stage model with a support vector machine classifier is found to be advantageous on an out-of-time sample compared with other methods, suggesting that an SVM model is preferred to a logistic regression at the classification stage. We suggest that the choice of regression models is less influential in prediction of recovery rates than the choice of classification methods in the first step of two-stage models based on the empirical evidence. The risk weighted assets of financial institutions are determined by the estimates of LGD together with PD and EAD. A robust and accurate LGD model impacts banks when making business decisions including setting credit risk strategies and pricing credit products. The regulatory capital determined by the expected and unexpected losses is also important to the financial market stability which should be carefully examined by the regulators. In summary this research highlights the importance of LGD models and provides a new perspective for practitioners and regulators to manage credit risk quantitatively.
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Moura, Telmo Correia de Pina e. "Forecasting loss given default with the nearest neighbor algorithm." Master's thesis, Instituto Superior de Economia e Gestão, 2012. http://hdl.handle.net/10400.5/10314.
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Mestrado em Matemática FinanceiraNos últimos anos, a previsão do Loss Given Default (LGD) tem sido um dos principais desafios no âmbito da gestão do risco de crédito. Investigadores académicos e profissionais da indústria bancária têm-se dedicado ao estudo deste parâmetro de risco em particular. Apesar de todas as diferentes abordagens já desenvolvidas e publicadas até hoje, a previsão do LGD continua a ser um tema de estudo académico intenso e sobre o qual ainda não existe um "consenso" metodológico na banca. Este trabalho apresenta uma abordagem alternativa para a previsão do LGD baseada na utilização de um simples, mas intuitivo, algoritmo de Machine Learning: o algoritmo nearest neighbor. De forma a avaliar a perfomance desta técnica não paramétrica na previsão do LGD, são utilizadas determinadas métricas de avaliação que permitem a comparação com um modelo paramétrico mais convencional e com a utilização do LGD médio histórico.
In recent years, forecasting Loss Given Default (LGD) has been a major challenge in the field of credit risk management. Practitioners and academic researchers have focused on the study of this particular risk dimension. Despite all different approaches that have been developed and published so far, it remains an area of intense academic study and with lack of consensual solutions in the banking industry. This paper presents an LGD forecasting approach based on a simple and intuitive Machine Learning algorithm: the nearest neighbor algorithm. In order to evaluate the performance of this non parametric technique, some proper evaluation metrics are used to compare it to a more ?classical? parametric model and to the use of historical recovery rates to predict LGD.
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Hallström, Richard. "Estimating Loss-Given-Default through Survival Analysis : A quantitative study of Nordea's default portfolio consisting of corporate customers." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-122914.
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In Sweden, all banks must report their regulatory capital in their reports to the market and their models for calculating this capital must be approved by the financial authority, Finansinspektionen. The regulatory capital is the capital that a bank has to hold as a security for credit risk and this capital should serve as a buffer if they would loose unexpected amounts of money in their lending business. Loss-Given-Default (LGD) is one of the main drivers of the regulatory capital and the minimum required capital is highly sensitive to the reported LGD. Workout LGD is based on the discounted future cash flows obtained from defaulted customers. The main issue with workout LGD is the incomplete workouts, which in turn results in two problems for banks when they calculate their workout LGD. A bank either has to wait for the workout period to end, in which some cases take several years, or to exclude or make rough assumptions about those incomplete workouts in their calculations. In this study the idea from Survival analysis (SA) methods has been used to solve these problems. The mostly used SA model, the Cox proportional hazards model (Cox model), has been applied to investigate the effect of covariates on the length of survival for a monetary unit. The considered covariates are Country of booking, Secured/Unsecured, Collateral code, Loan-To-Value, Industry code, Exposure-At- Default and Multi-collateral. The data sample was first split into 80 % training sample and 20 % test sample. The applied Cox model was based on the training sample and then validated with the test sample through interpretation of the Kaplan-Meier survival curves for risk groups created from the prognostic index (PI). The results show that the model correctly rank the expected LGD for new customers but is not always able to distinguish the difference between risk groups. With the results presented in the study, Nordea can get an expected LGD for newly defaulted customers, given the customers’ information on the considered covariates in this study. They can also get a clear picture of what factors that drive a low respectively high LGD.I Sverige måste alla banker rapportera sitt lagstadgade kapital i deras rapporter till marknaden och modellerna för att beräkna detta kapital måste vara godkända av den finansiella myndigheten, Finansinspektionen. Det lagstadgade kapitalet är det kapital som en bank måste hålla som en säkerhet för kreditrisk och den agerar som en buffert om banken skulle förlora oväntade summor pengar i deras utlåningsverksamhet. Loss- Given-Default (LGD) är en av de främsta faktorerna i det lagstadgade kapitalet och kravet på det minimala kapitalet är mycket känsligt för det rapporterade LGD. Workout LGD är baserat på diskonteringen av framtida kassaflöden från kunder som gått i default. Det huvudsakliga problemet med workout LGD är ofullständiga workouts, vilket i sin tur resulterar i två problem för banker när de ska beräkna workout LGD. Banken måste antingen vänta på att workout-perioden ska ta slut, vilket i vissa fall kan ta upp till flera år, eller så får banken exkludera eller göra grova antaganden om dessa ofullständiga workouts i sina beräkningar. I den här studien har idén från Survival analysis (SA) metoder använts för att lösa dessa problem. Den mest använda SA modellen, Cox proportional hazards model (Cox model), har applicerats för att undersöka effekten av kovariat på livslängden hos en monetär enhet. De undersökta kovariaten var Land, Säkrat/Osäkrat, Kollateral-kod, Loan-To-Value, Industri-kod Exposure-At-Default och Multipla-kollateral. Dataurvalet uppdelades först i 80 % träningsurval och 20 % testurval. Den applicerade Cox modellen baserades på träningsurvalet och validerades på testurvalet genom tolkning av Kaplan-Meier överlevnadskurvor för riskgrupperna skapade från prognosindexet (PI). Med de presenterade resultaten kan Nordea beräkna ett förväntat LGD för nya kunder i default, givet informationen i den här studiens undersökta kovariat. Nordea kan också få en klar bild över vilka faktorer som driver ett lågt respektive högt LGD.
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Silva, João Flávio Andrade. "Modelos preditivos para LGD." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/104/104131/tde-13112018-084000/.
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As instituições financeiras que pretendem utilizar a IRB (Internal Ratings Based) avançada precisam desenvolver métodos para estimar a componente de risco LGD (Loss Given Default). Desde a década de 1950 são apresentadas propostas para modelagem da PD (Probability of default), em contrapartida, a previsão da LGD somente recebeu maior atenção após a publicação do Acordo Basileia II. A LGD possui ainda uma literatura pequena, se comparada a PD, e não há um método eficiente em termos de acurácia e interpretação como é a regressão logística para a PD. Modelos de regressão para LGD desempenham um papel fundamental na gestão de risco das instituições financeiras. Devido sua importância este trabalho propõe uma metodologia para quantificar a componente de risco LGD. Considerando as características relatadas sobre a distribuição da LGD e na forma flexível que a distribuição beta pode assumir, propomos uma metodologia de estimação da LGD por meio do modelo de regressão beta bimodal inflacionado em zero. Desenvolvemos a distribuição beta bimodal inflacionada em zero, apresentamos algumas propriedades, incluindo momentos, definimos estimadores via máxima verossimilhança e construímos o modelo de regressão para este modelo probabilístico, apresentamos intervalos de confiança assintóticos e teste de hipóteses para este modelo, bem como critérios para seleção de modelos, realizamos um estudo de simulação para avaliar o desempenho dos estimadores de máxima verossimilhança para os parâmetros da distribuição beta bimodal inflacionada em zero. Para comparação com nossa proposta selecionamos os modelos de regressão beta e regressão beta inflacionada, que são abordagens mais usuais, e o algoritmo SVR , devido a significativa superioridade relatada em outros trabalhos.Financial institutions willing to use the advanced Internal Ratings Based (IRB) need to develop methods to estimate the LGD (Loss Given Default) risk component. Proposals for PD (Probability of default) modeling have been presented since the 1950s, in contrast, LGDs forecast has received more attention only after the publication of the Basel II Accord. LGD also has a small literature, compared to PD, and there is no efficient method in terms of accuracy and interpretation such as logistic regression for PD. Regression models for LGD play a key role in the risk management of financial institutions, due to their importance this work proposes a methodology to quantify the LGD risk component. Considering the characteristics reported on the distribution of LGD and in the flexible form that the beta distribution may assume, we propose a methodology for estimation of LGD using the zero inflated bimodal beta regression model. We developed the zero inflated bimodal beta distribution, presented some properties, including moments, defined estimators via maximum likelihood and constructed the regression model for this probabilistic model, presented asymptotic confidence intervals and hypothesis test for this model, as well as selection criteria of models, we performed a simulation study to evaluate the performance of the maximum likelihood estimators for the parameters of the zero inflated bimodal beta distribution. For comparison with our proposal we selected the beta regression models and inflated beta regression, which are more usual approaches, and the SVR algorithm, due to the significant superiority reported in other studies.
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Cruz, José Eduardo Fidalgo Freire. "Cálculo da Loss Given Default no crédito à habitação com Cadeias de Markov." Master's thesis, Instituto Superior de Economia e Gestão, 2012. http://hdl.handle.net/10400.5/10730.
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Mestrado em Matemática FinanceiraO Acordo de Basileia II determina, entre outros, o requisito de fundos mínimos que os bancos necessitam de manter, de modo a protegerem-se do risco de crédito. Um dos parâmetros essenciais na avaliação do requisito é a LGD - Loss Given Default, que representa a perda sofrida pela instituição quando os seus clientes entram em incumprimento. O objetivo principal do projeto é desenvolver um modelo e uma metodologia que possibilitem o cálculo deste parâmetro recorrendo a Cadeias de Markov. O estudo incidirá sobre o crédito à habitação, pela sua importância para a maioria dos bancos. Ao longo da exposição será visto que as Cadeias de Markov constituem um instrumento adequado para completar a informação necessária ao cálculo da LGD, cumprindo todas as exigências que o Acordo determina.
Basel II determines, among other things, the minimum capital requirement, which is the amount that banks need to keep in order to protect against credit risk. One of the key parameters for the calculation of the capital requirements is the LGD - Loss Given Default. The objective of this project is to develop a model and a framework to calculate the LGD using Markov Chains. A special attention is given to mortgages due to the importance of this kind of loans to the banking sector. In this work, using Markov Chains, it will be possible to predict the missing information that is required by Basel II to calculate the minimum capital requirements.
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Rezende, Gustavo de Magalhães. "Estimativas de LGD em portfólios de crédito simulados: análises comparativas." Universidade Presbiteriana Mackenzie, 2011. http://tede.mackenzie.br/jspui/handle/tede/537.
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Basel II Accord will allow banks in Brazil to calculate their capital requirements using internal ratings based on the advanced IRB (Internal Rating-Based) approach, depending on their credit risk exposure. The main modeling components that must be estimated are the probability of default (PD), loss given default (LGD) and exposure at default (EAD). The aim of this dissertation is to estimate the parameter LGD using different models found in the literature in order to compare the obtained results. For that, the credit portfolios within this study will be simulated via Monte Carlo simulation, due to the difficulty in getting real losses data.
O acordo de Basileia II no Brasil vai permitir que os bancos utilizem modelos internos, na abordagem IRB avançada (Internal Rating-Based), que sirvam de base para o cálculo dos requisitos mínimos de capital em função do nível de exposição ao risco de crédito. Dentre os principais componentes estimados estão a probabilidade de default (PD probability of default), a perda dado o default (LGD loss given default) e a exposição no default (EAD exposure at default). Esta dissertação tem como objetivo realizar estimativas de LGD utilizando alguns modelos descritos na literatura e comparando os resultados obtidos. Para tanto, os portfólios de crédito do estudo serão simulados através de técnicas de Monte Carlo, dada a escassez de dados de perdas reais.
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Brown, Iain Leonard Johnston. "Basel II compliant credit risk modelling : model development for imbalanced credit scoring data sets, loss given default (LGD) and exposure at default (EAD)." Thesis, University of Southampton, 2012. https://eprints.soton.ac.uk/341517/.
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The purpose of this thesis is to determine and to better inform industry practitioners to the most appropriate classification and regression techniques for modelling the three key credit risk components of the Basel II minimum capital requirement; probability of default (PD), loss given default (LGD), and exposure at default (EAD). 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 each of PD, LGD and EAD. In this thesis, first the issue of imbalanced credit scoring data sets, a special case of PD modelling where the number of defaulting observations in a data set is much lower than the number of observations that do not default, is identified, and the suitability of various classification techniques are analysed and presented. As well as using traditional classification techniques this thesis also explores the suitability of gradient boosting, least square support vector machines and random forests as a form of classification. The second part of this thesis focuses on the prediction of LGD, which measures the economic loss, expressed as a percentage of the exposure, in case of default. In this thesis, various state-of-the-art regression techniques to model LGD are considered. In the final part of this thesis we investigate models for predicting the exposure at default (EAD). For off-balance-sheet items (for example credit cards) to calculate the EAD one requires the committed but unused loan amount times a credit conversion factor (CCF). Ordinary least squares (OLS), logistic and cumulative logistic regression models are analysed, as well as an OLS with Beta transformation model, with the main aim of finding the most robust and comprehensible model for the prediction of the CCF. Also a direct estimation of EAD, using an OLS model, will be analysed. All the models built and presented in this thesis have been applied to real-life data sets from major global banking institutions.
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Castaldini, Christian. "Il mercato NPL: analisi econometrica d'impatto delle operazioni di cessione." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018.
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La risoluzione della problematica dei non-performing loans (NPL) rappresenta ad oggi per il settore bancario europeo uno dei temi di maggior rilievo, nonché uno dei principali lasciti del duplice periodo di recessione. Il percorso di riduzione intrapreso negli ultimi anni sta vedendo nelle cessioni uno strumento particolarmente efficace per una rapida dismissione di tali asset, in un contesto di crescente sviluppo del mercato secondario del credito. Il significativo sconto richiesto dagli investitori in questa tipologia di operazioni, tuttavia, si è riflesso in particolari oneri a carico degli enti, specialmente per quelli che utilizzano modelli avanzati di rating interni (AIRB) per la valutazione del rischio di credito. I minori recuperi associati alle posizioni cedute comportano un peggioramento nelle stime del parametro di rischio Loss Given Default (LGD), impattando conseguentemente il capitale di vigilanza. Il presente elaborato si propone di analizzare la tematica dei non-performing loans offrendo innanzitutto un quadro delle maggiori cause e conseguenze sul sistema bancario, discutendo inoltre le metodologie di gestione che il contesto attuale richiede. Si analizzeranno in seguito le dinamiche in atto nel mercato secondario del credito, italiano ed europeo, assieme alle principali criticità in termini di efficienza, le prospettive future e gli strumenti introdotti. La trattazione si focalizzerà quindi sugli impatti che le operazioni di cessione comportano sulle stime LGD e sul requisito patrimoniale, così come i meccanismi in gioco e i temi particolarmente dibattuti. Si presenteranno in questo ambito i principali approcci metodologici di inclusione delle cessioni nei modelli LGD, analizzando quantitativamente un’applicazione concreta e valutandone gli impatti complessivi.
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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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Honal, Martin. "Loss Given Default von Mobilien-Leasingverträgen." Wiesbaden Gabler, 2008. http://d-nb.info/992304717/04.
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Lopes, Maria Leonor Grossinho Fontinha Jacinto. "Loss given default : a backtesting exercise." Master's thesis, Instituto Superior de Economia e Gestão, 2018. http://hdl.handle.net/10400.5/18107.
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Mestrado em Mathematical FinanceAfter January 2018, the new accounting standard IFRS 9 Financial Instruments was mandatory practice for all Financial Institutions. Introducing the new impairment model, which focus on expected credit losses (ECL) instead of incurred losses established previous in IAS 39 Measurement and Recognition. According to the new standard, the risk parameters involved in the computation of the ECL are required to be periodically revised. The Loss Given Default (LGD) is a risk input which represents the loss in case of a financial instrument defaults. Hence, the aim of the present report is to validate the risk input through a back testing exercise, considering statistical tests.
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Matos, Sara Madeira. "Interpretable models of loss given default." Master's thesis, Instituto Superior de Economia e Gestão, 2021. http://hdl.handle.net/10400.5/20981.
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Mestrado em Econometria Aplicada e PrevisãoA gestão do risco de crédito é uma área em que os reguladores esperam que os bancos adotem modelos de risco transparentes e auditáveis colocando de parte o uso de modelos de black-box apesar destes serem mais precisos. Neste estudo, mostramos que os bancos não precisam de sacrificar a precisão preditiva ao custo da transparência do modelo para estar em conformidade com os requisitos regulatórios. Ilustramos isso mostrando que as previsões de perdas de crédito fornecidas por um modelo black-box podem ser facilmente explicadas em termos dos seus inputs.
Credit risk management is an area where regulators expect banks to have transparent and auditable risk models, which would preclude the use of more accurate black-box models. Furthermore, the opaqueness of these models may hide unknown biases that may lead to unfair lending decisions. In this study, we show that banks do not have to sacrifice predictive accuracy at the cost of model transparency to be compliant with regulatory requirements. We illustrate this by showing that the predictions of credit losses given by a black-box model can be easily explained in terms of their inputs. Because black-box models fit better the data, banks should consider the determinants of credit losses suggested by these models in lending decisions and pricing of credit exposures.
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Dahlin, Fredrik, and Samuel Storkitt. "Estimation of Loss Given Default for Low Default Portfolios." Thesis, KTH, Matematisk statistik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-145149.
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The Basel framework allows banks to assess their credit risk by using their own estimates of Loss Given Default (LGD). However, for a Low Default Portfolio (LDP), estimating LGD is difficult due to shortage of default data. This study evaluates different LGD estimation approaches in an LDP setting by using pooled industry data obtained from a subset of the PECDC LGD database. Based on the characteristics of a LDP a Workout LGD approach is suggested. Six estimation techniques, including OLS regression, Ridge regression, two techniques combining logistic regressions with OLS regressions and two tree models, are tested. All tested models give similar error levels when tested against the data but the tree models might produce rather different estimates for specific exposures compared to the other models. Using historical averages yield worse results than the tested models within and out of sample but are not considerably worse out of time.
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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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Wildenauer, Nicole. "Modellierung der loss rate given default im Kreditrisikomanagement /." Duisburg ; Köln : WiKu-Verl.***5003820, 2007. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=016568274&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.
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De, Moraes Angela Rita Freitas. "Novel information in estimating loss given default in Brazil." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/33187.
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The Basel Accord regulates risk and capital requirements to ensure that a bank holds capital proportional to the exposed risk of its lending practices. 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). Brazil was among the first emerging market countries to release a timetable for the implementation of the Basel II Accord and aimed to apply it uniformly to all Brazilian financial institutions from 2005 to 2011. Within this context, the necessity arises of conducting research that could assist the financial institutions in improving the accuracy of their models. This thesis has three objectives. The first is to develop a macro-economic model to predict the behaviour of the aggregate delinquency in Brazilian consumer loans. The model consists in testing co-integrating relationships and then estimating a short run error correction model. The results based on monthly data from 2000 to 2012 show that the delinquency rate is particularly sensitive to shocks on GDP and to the variation of workers' income. The analysis then shifts to micro or account level to model the behaviour of borrowers and certain novel types of information that can be used for prediction. Second, customers fail to make loan repayments for a number of reasons, ranging from simple forgetfulness to deliberate attempts. For this reason, the second objective is to investigate the reasons for default and to explore ways of incorporating these variables into Recovery Rate (RR = 1 - LGD) models, since the standard approach overlooks real reasons for default and uses proxies for them such as marital status and length of employment. Customers who failed to repay their loans were interviewed in order to discover the causes for this failure. In addition, the interviews included questions aimed to measure the customer's personality traits and their financial knowledge in relation to the reasons for default. The empirical results show that the variables proposed in this study, namely, reason for missing payment, financial knowledge and risk taken, improve the prediction of the recovery rate. Thirdly, it is known that recovery depends on the debt collection process and on the different options or actions that collection departments can take. Yet there is practically no literature exploring the impact of the lender's collection actions on RR/LGD. This work fills this gap by investigating the role of different collection actions at the loan-level for a retail credit product, and by estimating LGD models using Panel Data regressions.
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Leow, Mindy. "Credit risk models for mortgage loan loss given default." Thesis, University of Southampton, 2010. https://eprints.soton.ac.uk/170515/.
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Arguably, the credit risk models reported in the literature for the retail lending sector have so far been less developed than those for the corporate sector, mainly due to the lack of publicly available data. Having been given access to a dataset on defaulted mortgages kindly provided by a major UK bank, this work first investigates the Loss Given Default (LGD) of mortgage loans with the development of two separate component models, the Probability of Repossession (given default) Model and the Haircut (given repossession) Model. They are then combined into an expected loss percentage. Performance-wise, this two-stage LGD model is shown to do better than a single-stage LGD model (which directly models LGD from loan and collateral characteristics), as it achieves a better Rsquare value, and it more accurately matches the distribution of observed LGD. We next investigate the possibility of including macroeconomic variables into either or both component models to improve LGD prediction. Indicators relating to net lending, gross domestic product, national default rates and interest rates are considered and the interest rate is found to be most beneficial to both component models. Finally, we develop a competing risk survival analysis model to predict the time taken for a defaulted mortgage loan to reach some outcome (i.e. repossession or non-repossession). This allows for a more accurate prediction of (discounted) loss as these periods could vary from months to years depending on the health of the economy. Besides loan- or collateral-related characteristics, we incorporate a time-dependent macroeconomic variable based on the house price index (HPI) to investigate its impact on repossession risk. We find that observations of different loan-to-value ratios at default and different security type are affected differently by the economy. This model is then used for stress test purposes by applying a Monte Carlo simulation, and by varying the HPI forecast, to get different loss distributions for different economic outlooks.
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Velka, Elina. "Loss Given Default Estimation with Machine Learning Ensemble Methods." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279846.
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This thesis evaluates the performance of three machine learning methods in prediction of the Loss Given Default (LGD). LGD can be seen as the opposite of the recovery rate, i.e. the ratio of an outstanding loan that the loan issuer would not be able to recover in case the customer would default. The methods investigated are decision trees, random forest and boosted methods. All of the methods investigated performed well in predicting the cases were the loan is not recovered, LGD = 1 (100%), or the loan is totally recovered, LGD = 0 (0% ). When the performance of the models was evaluated on a dataset where the observations with LGD = 1 were removed, a significant decrease in performance was observed. The random forest model built on an unbalanced training dataset showed better performance on the test dataset that included values LGD = 1 and the random forest model built on a balanced training dataset performed better on the test set where the observations of LGD = 1 were removed. Boosted models evaluated in this study showed less accurate predictions than other methods used. Overall, the performance of random forest models showed slightly better results than the performance of decision tree models, although the computational time (the cost) was considerably longer when running the random forest models. Therefore decision tree models would be suggested for prediction of the Loss Given Default.Denna uppsats undersöker och jämför tre maskininlärningsmetoder som estimerar förlust vid fallissemang (Loss Given Default, LGD). LGD kan ses som motsatsen till återhämtningsgrad, dvs. andelen av det utstående lånet som långivaren inte skulle återfå ifall kunden skulle fallera. Maskininlärningsmetoder som undersöks i detta arbete är decision trees, random forest och boosted metoder. Alla metoder fungerade väl vid estimering av lån som antingen inte återbetalas, dvs. LGD = 1 (100%), eller av lån som betalas i sin helhet, LGD = 0 (0%). En tydlig minskning i modellernas träffsäkerhet påvisades när modellerna kördes med ett dataset där observationer med LGD = 1 var borttagna. Random forest modeller byggda på ett obalanserat träningsdataset presterade bättre än de övriga modellerna på testset som inkluderade observationer där LGD = 1. Då observationer med LGD = 1 var borttagna visade det sig att random forest modeller byggda på ett balanserat träningsdataset presterade bättre än de övriga modellerna. Boosted modeller visade den svagaste träffsäkerheten av de tre metoderna som blev undersökta i denna studie. Totalt sett visade studien att random forest modeller byggda på ett obalanserat träningsdataset presterade en aning bättre än decision tree modeller, men beräkningstiden (kostnaden) var betydligt längre när random forest modeller kördes. Därför skulle decision tree modeller föredras vid estimering av förlust vid fallissemang.
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Betz, Jennifer [Verfasser], and Daniel [Akademischer Betreuer] Rösch. "Resolution of defaulted loan contracts - An empirical analysis of default resolution time and loss given default / Jennifer Betz ; Betreuer: Daniel Rösch." Regensburg : Universitätsbibliothek Regensburg, 2018. http://d-nb.info/1164765604/34.
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Macedo, Cristiana Gobbi. "Determinação da perda de crédito por meio de modelos estruturais: aplicação da abordagem de implied market loss given default." Universidade de São Paulo, 2014. http://www.teses.usp.br/teses/disponiveis/12/12136/tde-25062014-154725/.
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Em busca da adequação aos requisitos apresentados pelo Acordo de Basiléia, as instituições financeiras estão despendendo esforços para o desenvolvimento de mensurações e processos. Neste contexto se insere o desenvolvimento de modelos quantitativos para às organizações que pretendem se candidatar à abordagem avançada. O problema de pesquisa propõe mensurar o parâmetro de perda de crédito, ou loss given default, em situações em que não existam eventos de inadimplência observados. A literatura a respeito indica a utilização de modelos estruturais para estes cenários: o modelo proposto por Merton (1974) ou suas derivações são largamente empregados na determinação da probabilidade de perdas (probability of default - em inglês) e perdas de credito (loss given default - em inglês). Nesta metodologia a solução é encontrada de maneira implícita, por meio de preços de títulos e ações. Este trabalho aplica o modelo de Merton e verifica implicações deste uso para o calculo da perda de credito, neutra ao risco e implícita, em empresas listadas na Bolsa de Valores de São Paulo (Bovespa). O público foi selecionado no período de dezembro de 2006 a junho de 2013 e, informações como preço e quantidade de ações e valor da dívida contábil foram coletadas. Os principais resultados encontrados, de modo similar a de outros autores, mostram que: (i) a perda de crédito é maior em momentos de instabilidade financeira, como observado em 2008, época em que os preços dos ativos possuíram alta volatilidade, (ii) o maturity, ou duration, utilizado possui grande peso nos valores de perda de crédito: maturity maior, recuperação menor e (iii) quanto maior o peso da dívida contábil no valor da empresa, menor a volatilidade da própria.In an effort to comply with the Basel Agreement requirements, financial institutions have engaged in developing their own measures and processes. Within that context, quantitative models are being developed for organizations seeking an advanced approach. The research-related problem aims to estimate the credit loss parameter, loss given default, in situations where events of default are not observed. Literature in that respect indicates the utilization of structural models in such scenarios: the model proposed by Merton (1974) or its derivations are widely employed in determining the probability of default and loss given default. In this methodology the solution is found in an implied manner through the price of bonds and equities. This work applies the Merton model and verifies the implications of its use in calculating loss given default, risk neutral and implicit, in companies listed on the São Paulo Stock Exchange (Bovespa). The target populations in the period from December 2006 to June 2013 and such data as stock price, number of outstanding shares and debt book value have been collected. The main results found, in a manner very similar to other authors, demonstrate that: (i) the loss given default is greater at moments of financial instability, as observed in 2008, a time at which asset prices showed high volatility, (ii) the maturity, or duration, has a great influence on loss given default: higher the maturity, lower the recovery, and as well (iii) higher the book´s value debt weight on firm value, the lower is the firm´s value volatility.
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Töws, Eugen [Verfasser], Thomas [Akademischer Betreuer] Hartmann-Wendels, Dieter [Akademischer Betreuer] Hess, and Heinrich [Akademischer Betreuer] Schradin. "Advanced Methods for Loss Given Default Estimation / Eugen Töws. Gutachter: Thomas Hartmann-Wendels ; Dieter Hess ; Heinrich Schradin." Köln : Universitäts- und Stadtbibliothek Köln, 2016. http://d-nb.info/1082030473/34.
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Krüger, Steffen Verfasser], and Daniel [Akademischer Betreuer] [Rösch. "Advanced Dependency Modeling in Credit Risk - Lessons for Loss Given Default, Lifetime Expected Loss and Bank Capital Requirements / Steffen Krüger ; Betreuer: Daniel Rösch." Regensburg : Universitätsbibliothek Regensburg, 2017. http://d-nb.info/1139892398/34.
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Ljung, Carolina, and Maria Svedberg. "Estimation of Loss Given Default Distributions for Non-Performing Loans Using Zero-and-One Inflated Beta Regression Type Models." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273593.
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This thesis investigates three different techniques for estimating loss given default of non-performing consumer loans. This is a contribution to a credit risk evaluation model compliant with the regulations stipulated by the Basel Accords, regulating the capital requirements of European financial institutions. First, multiple linear regression is applied, and thereafter, zero-and-one inflated beta regression is implemented in two versions, with and without Bayesian inference. The model performances confirm that modeling loss given default data is challenging, however, the result shows that the zero-and-one inflated beta regression is superior to the other models in predicting LGD. Although, it shall be recognized that all models had difficulties in distinguishing low-risk loans, while the prediction accuracy of riskier loans, resulting in larger losses, were higher. It is further recommended, in future research, to include macroeconomic variables in the models to capture economic downturn conditions as well as adopting decision trees, for example by applying machine learning.Detta examensarbete undersöker tre olika metoder för att estimera förlusten vid fallissemang för icke-presterande konsumentlån. Detta som ett bidrag till en kreditrisksmodell i enlighet med bestämmelserna i Baselregelverken, som bland annat reglerar kapitalkraven för europeiska finansiella institut. Inledningsvis tillämpas multipel linjär regression, därefter implementeras två versioner av utvidgad betaregression, med och utan bayesiansk inferens. Resultatet bekräftar att modellering data för förlust givet fallissemang är utmanande, men visar även att den utvidgade betaregressionen utan bayesiansk inferens är bättre de andra modellerna. Det ska dock tilläggas att alla modeller visade svårigheter att estimera lån med låg risk, medan tillförlitligheten hos lån med hög risk, vilka generellt sett medför större förluster, var högre. Vidare rekommenderas det för framtida forskning att inkludera makroekonomiska variabler i modellerna för att fånga ekonomiska nedgångar samt att implementera beslutsträd, exempelvis genom applicering av maskininlärning.
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Schneider, Paul, Leopold Sögner, and Tanja Veza. "The Economic Role of Jumps and Recovery Rates in the Market for Corporate Default Risk." Cambridge University Press, 2010. http://dx.doi.org/10.1017/S0022109010000554.
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Using an extensive cross-section of US corporate CDS this paper offers an economic understanding
of implied loss given default (LGD) and jumps in default risk. We formulate and underpin empirical stylized facts about CDS spreads, which are then reproduced in our affine intensity-based jump-diffusion model. Implied LGD is well identified, with obligors possessing substantial tangible assets expected to recover more. Sudden increases
in the default risk of investment-grade obligors are higher relative to speculative grade. The probability of structural migration to default is low for investment-grade
and heavily regulated obligors because investors fear distress rather through rare but devastating events. (authors' abstract)
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Miller, Patrick Michel [Verfasser], Thomas [Gutachter] Hartmann-Wendels, Dieter [Gutachter] Hess, and Heinrich R. [Gutachter] Schradin. "Modeling and Estimating the Loss Given Default of Leasing Contracts / Patrick Michel Miller ; Gutachter: Thomas Hartmann-Wendels, Dieter Hess, Heinrich R. Schradin." Köln : Universitäts- und Stadtbibliothek Köln, 2017. http://d-nb.info/1124587748/34.
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Araújo, Evaristo Donato. "A taxa de recuperação de créditos ruins em bancos comerciais privados brasileiros." reponame:Repositório Institucional do FGV, 2004. http://hdl.handle.net/10438/2457.
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Previous issue date: 2004-04-07T00:00:00ZCredit risk comes from the possibility of the debtor not paying its debt at the maturity date, and the promised amount. When the debtor doesn’t pay in full its debt, we say he or she is in default. In this case, the creditor gets a loss. However, the loss could be reduced if the debtor pays part of his or her debt. The measurement of a debtor’s probability of default has been the subject of studies for decades. However, the measurement of how much one can receive from a defaulted credit – the recovery rate – has been given attention only recently. And, most of the time, this measure has been calculated for huge companies in United States financial markets, only. We have defined recovery rate based on financial reports of Brazilian commercial banks, and tracked the path of this variable pari passu to default rate, defined from the same reports also. We established a theoretical framework, and made hypothesis on how such variables as default rates and other credit quality indicators, economic level indicators, nominal and real interest rates, and capital markets indicators could explain variations on the recovery rates we have defined. We gathered information from 46 Brazilian private commercial banks, semiannually, bracing the period between June of 1994 and December 2002. These institutions were segmented by their share on the amount of credit of the private banking industry in Brazil and by the origin of its capital. Statistical models were run on explanatory variables based on original data and on variables obtained from principal components analysis. The models were able to explain most of the variation observed on the recovery rate we have defined, for the segments we have studied. The best models have shown that variations on the recovery rate could be explained by default rates and other indicators of credit quality, economic activity indicators and capital markets indicators.
O risco de crédito decorre da possibilidade de o devedor não honrar sua dívida no montante e na data aprazada. Quando o devedor não liquida sua dívida nas condições contratadas, diz-se que se torna inadimplente. Neste caso, o credor incorre em prejuízo. A perda, entretanto, pode ser reduzida se o cliente pagar parcialmente o que deve. A mensuração da probabilidade de um devedor inadimplir tem sido objeto de estudos há décadas. Entretanto, a quantificação do quanto o credor recebe em caso de inadimplência – a taxa de recuperação –só recentemente tem recebido atenção da academia. E, na maioria das vezes, esta quantificação tem-se limitado aos títulos de grandes empresas, negociados no mercado de capitais dos Estados Unidos da América. Neste trabalho, definiu-se uma taxa de recuperação baseada em informações contábeis de instituições bancárias brasileiras e analisou-se o comportamento desta variável pari passu à taxa de inadimplência, também definida a partir de dados contábeis. Estabeleceu-se um arcabouço teórico capaz de explicar de que forma variáveis como a taxa de inadimplência e outros indicadores de qualidade das carteiras de crédito, indicadores da atividade econômica, níveis de juros nominais e reais e indicadores do mercado de capitais, poderiam explicar as variações na taxa de recuperação das carteiras de crédito. Foram obtidas informações de um conjunto de 46 instituições bancárias privadas brasileiras, semestralmente, para o período compreendido entre junho de 1994 e dezembro de 2002. Essas instituições foram segmentadas pela representatividade de suas carteiras de crédito no volume total de créditos das instituições comerciais brasileiras e por origem de seu capital acionário. Elaboraram-se modelos estatísticos baseados em regressões multivariadas tanto de variáveis originais como de variáveis obtidas através de análise de componentes principais, que se mostraram capazes de explicar parte considerável das variações observadas na taxa de recuperação no conceito contábil, para os vários segmentos de instituições estudados. Mostraram-se como variáveis explicativas relevantes, nos melhores modelos, indicadores de inadimplência, indicadores da atividade econômica e indicadores do mercado de capitais.
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Lindgren, Jonathan. "Modeling credit risk for an SME loan portfolio: An Error Correction Model approach." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-136176.
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Sedan den globala finanskrisen 2008 har flera stora regelverk införts för att säkerställa att banker hanterar risker på sunt sätt. Bland dessa regelverk är Basel II som infört kapitalkrav för kreditrisk som baseras på Sannolikhet för Fallissemang och Förlust Givet Fallissemang. Basel II Advanced Internal-Based Approach ger banker möjligheten att skatta dessa riskmått för enskilda portföljer och göra interna kreditriskvärderingar. I överensstämmelse med Advanced Internal-Based-rating undersöker denna uppsats användningen av en Error Correction Model för modellering av Sannolikhet för Fallissemang. En modell som visat sin styrka inom stresstestning. Vidare implementeras en funktion för Förlust Givet Fallissemang som binder samman Sannolikhet för Fallissemang och Förlust Givet Fallissemang med systematisk risk. Error Correction Modellen modellerar Sannolikhet för Fallissemang av en SME-portfölj från en av de "fyra stora" bankerna i Sverige. Modellen utvärderas och stresstestas med Europeiska Bankmyndighetens stresstestscenario 2016 och analyseras, med lovande resultat.Since the global financial crisis of 2008, several big regulations have been implemented to assure that banks follow sound risk management. Among these are the Basel II Accords that implement capital requirements for credit risk. The core measures of credit risk evaluation are the Probability of Default and Loss Given Default. The Basel II Advanced Internal-Based-Rating Approach allows banks to model these measures for individual portfolios and make their own evaluations. This thesis, in compliance with the Advanced Internal-Based-rating approach, evaluates the use of an Error Correction Model when modeling the Probability of Default. A model proven to be strong in stress testing. Furthermore, a Loss Given Default function is implemented that ties Probability of Default and Loss Given Default to systematic risk. The Error Correction Model is implemented on an SME portfolio from one of the "big four" banks in Sweden. The model is evaluated and stress tested with the European Banking Authority's 2016 stress test scenario and analyzed, with promising results.
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Hlavatá, Ivana. "Modelování parametru LGD pomocí redukovaných modelů." Master's thesis, 2011. http://www.nusl.cz/ntk/nusl-312542.
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The master thesis deals with the advanced methods for estimating credit risk parameters from market prices: probability of default (PD) and loss given default (LGD). Precise evaluation of these parameters is important not only for banks to calculate their regulatory capital but also for investors to price risky bonds and credit derivatives. We provide forward looking reduced-form analytical method for calculation of PD and LGD of corporate defaultable bonds based on their quoted market prices, prices of equivalent risk-free bonds and quoted credit default swap spreads of the issuer of these bonds. This is reversed to most of the studies on credit risk modeling, as aim is not to price instruments based on estimated credit risk parameters, but to calculate these parameters based on the available market prices. Furthermore, compared to other studies, the LGD parameter is assumed to be endogenous and we provide the method for its simultaneous calculation with the probability of default. Finally, using developed methods, we estimate implied PD and LGD for five European banks assuming that the risk is priced correctly by other investors and the markets are efficient. JEL Classification: C02, C63, G13, G33 Keywords: credit risk, loss given default, probability of default, credit default swap Author's...
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Seidler, Jakub. "Implied market loss given default." Master's thesis, 2008. http://www.nusl.cz/ntk/nusl-294972.
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This thesis focuses on the key credit risk parameter - Loss Given Default (LGD). We describe its general properties and determinants with respect to seniority of debt, characteristics of debtors or macroeconomic conditions, and discuss its role in Basel II framework. Further, we illustrate how the LGD can be extracted from market observable information with help of both the structural and reduced- form models. Finally, by using the adjusted Mertonian approach, we estimate the 5-year expected LGDs for companies listed on Prague Stock Exchange and find out, that the average LGD for this analyzed sample is around 20%. To the author's best knowledge, those are the first implied market estimates of LGD in the Czech Republic. Powered by TCPDF (www.tcpdf.org)
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Lin, Hsiu-Chuan, and 林秀娟. "Modeling Loss Given Default of Corporate Loan." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/59628343447116972521.
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碩士國立清華大學
科技管理研究所
94
The purpose of this article is to construct a loss given default (LGD) estimation model which can discriminate LGD from different characteristics of obligation and collateral, and to offer a reference model for bank that will adopt Advance Internal Rating Based Approach to determine capital requirement of credit risk. This article is based on structured model to construct three different kinds of LGD estimation model which including the loan has no collateral, collateral value is constant and collateral value is stochastic. Specific to the model that concern collateral value is stochastic can take into account the correlation between collateral value and firm’s value, the volatility of collateral value and the volatility of assets value. In empirical analysis we find that the correlation between collateral value and firm’s assets value has significant effect on LGD. Under the same PD, higher correlation between collateral value and firm’s assets value resulting higher LGD. The liquidity of collateral is also an important factor that effect LGD. If the liquidity of collateral is low, it is more difficult to sell collateral to third party and will increase LGD.
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Jhou, Ming-Yi, and 周明儀. "The impact of institutional factors on loss given default." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/97uxrz.
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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)
105
The thesis studies the impact of institutional factors on loss given default (LGD), which discusses the effect on origin of legal system, stability of judicial regimes, degree of corruption, adoption of International Financial Reporting Standards (IFRS), financial structure of countries, and political economy. The sample period covers 46 countries from 1983 to 2015. I run the ordinary least square regression individually in accordance with adoption of civil law; rating of judicial impartiality; perception of corruption; utilizing of International Financial Reporting Standards (IFRS); OCED membership (financial structure guarantee); and percentage of country-level output of state-owned enterprise. I find results that civil law countries have higher LGD relative to the common law countries; countries which have more efficient judicial system keep lower LGD. Countries with high corruption degree lead to the higher LGD. Countries which adopt the IFRS obtain the lower LGD. OECD members have relatively stable financial structure enjoy the lower LGD. Countries with less government intervention cause lower LGD.
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YEH, CHIA-CHI, and 葉家齊. "Predicting the Loss Given Default During the Economic Recession." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/nnk5p3.
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碩士國立臺北大學
統計學系
107
Loss Given Default is one of the important variables which measures credit losses and estimate capital economic in IRB approach. In order to obtain a more conservative estimation of economic capital, the Basel II requires banks to use estimates of loss given default during the economic downturn.However, it'll be too late for banks to estimate loss given default when the economy is in a downturn. They must estimated the loss given default by converting the estimation of the loss given default during usual period. Compared with probability of default, Basel II does not specify a conversion function for the loss given default, which is also the goal of many scholars. The conversion function of loss given default during economic downturn is usually obtained by deriving the distribution of loss given default and calculating the conditional loss given default given an extreme quantile (indicating a poor economic situation). We believe that the distribution of loss given default is not necessarily a normal distribution.Our goal is to acquird a reasonable estimate of the loss given default by using different distribution hypotheses of loss given default, and comparing the performance of the transfer function under different distribution hypotheses.
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Tseng, Wei-Ming, and 曾維明. "A Case Study of Loss Given Default Business Loans." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/32129543329826778990.
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Tsai, Wei-jen, and 蔡偉仁. "A Study on Loss Given Default for Corporate Loan." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/q382jd.
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碩士東吳大學
國際經營與貿易學系
96
This study is extracted from a local commercial bank that has sold non-performing loans to the asset management company by choosing 135 cases of corporate loans from 1992 to 2002 as samples. In this article the main direction of this research is to discuss on the influence of the variable factors with LGD (loss given default) and the construction of a LGD model, so as to reduce the non-performing loans of a bank and to improve its competitiveness. First of all, we use the multiple linear regression analysis to forecast the variable factors and to discuss which one can influence the LGD. The experimental evidence showed that the enterprise period of service, loan rate, mortgage, house or stock take for collateral, the economical growth rate and the unemployment rate and the remaining seven variables, have a remarkable influence on the LGD. Secondly, we attempt to construct a LGD estimation model which can discriminate the LGD based on different characteristics of the economy. After this demonstration, its calculation and the prime number only differ by 3.78%, whereby the error is not big. Therefore this model is useful for the LGD examination when the lender applies for loan from financial institution.
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Lin, Ping-chi, and 林炳棋. "An Empirical Study on the Loss Given Default for Mortgage Loan." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/90982004327382382355.
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碩士朝陽科技大學
財務金融系碩士班
95
Risk exists in any place in the future all the time and the definition of risk also differs from person to person. The management of risk is the management of future uncertainty. In the field of banking and finance management, the risk that a financial institution faces can be regarded as its uncertainty of the return of portfolio.To build up the safety and stability of the financial system, BCBS of BIS (the Bank for International Settlements) issued “The International Regulations of Capital Measurement and Capital Standards” in July 1988, which regulated the ratio of the capital of a bank accounting for risk property should be higher than 8%. In Jan. 1996 it issued a revised version, called “International Convergence of Capital Measurement and Capital Standards”, which is shortened “Basel II.” Among loan business in domestic financial institutions, mortgage is one of the major businesses. The prediction of PD (Probability pf Default) and LGD (Loss Given Default) is thus a very important issue.When calculating RAROC (Risk Adjusted Return on Capital) or Capital Adequacy of a bank, LGD is one of the major variables. Taking a look at the domestic and international researches, we can find that many of them focus on the estimation of PD (Probability of Default), while few of them focus on LGD (Loss Given Default). This study adopts Logistic Regression Models, discussing the factors that influence the loss given default for mortgage of financial institutions. The data of LGD came from a domestic financial institution when its counterpart defaulted. It is expected that this study can help us understand the factors that influence the loss given default for mortgage, and analyze the location of collateral that provided by the mortgagor, LTV (Loan to Value), and the affecting degree caused by the loan and the loss given default of financial institutions. The finding is that when financial institutions lend money on mortgage, they have to prepare more loss reserve to meet the demand for capital adequacy since they are faced with higher risk of loss given default in terms of the non-urban location of collateral, high LTV ratio, and higher loan.
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LIN, JING XIU, and 林敬修. "Granularity Adjustment Method in Credit Risk with Stochastic Loss Given Default." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/95370620332725707016.
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碩士東吳大學
財務工程與精算數學系
98
In Basel II 2004, Basle Committee on Banking Supervision (BCBS) proposes financial institutions to establish assessment model internally. Since the distribution of percentage of loss of investment portfolio is hard to be estimated and complicated, it is the important topic to select an effective model which describes the loss of investment portfolio and how to calculate credit Value-at-Risk (VaR). Both of default model and random loss given default (LGD) are related to macroecnomic systematic factor in this assignment. By using Granularity Adjustment (GA) to calculate the credit VaR. When the lower default probability of obligor's exposure at default increase, and the other parameter invariable, thought generally credit VaR will reduce, Emmer and Tasche (2005) will be selected the example which credit VaR will decrease then increase; but when supposition LGD is stochastic whether also has the similar result.
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Cing, Fong-Sian, and 馮獻慶. "Using a Two-stage Model to Estimate the Loss Given Default Distribution." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/77591969479318458784.
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碩士國立東華大學
應用數學系
105
We propose a new two-stage model to estimate the loss given default (LGD) distribution. The first-stage model is the logistic regression model used to estimate probabilities of LGD equal to 0 and larger 0, respectively. The second-stage model is the right-tail extended beta model applied to generate the distribution of LGD between (0,1) and probability of LGD equal to 1. To implement the newly proposed two-stage model, we collect a sample of 4962 defaulted debts from Moody’s Default and Recovery Database. The empirical results show that the newly proposed two-stage model can generate the accurate LGD distribution estimate. Thus, it is useful for studying the LGD distribution.
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HSIEH, YI-LING, and 謝易陵. "A Study Factor on Loss Given Default of Corporate Loan： Application of AHP Method." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/uk7kc6.
Full textAbstract:
碩士東吳大學
國際經營與貿易學系
104
Abstract The expected loss estimation and credit risk management are emphasized in both Basel III and IFRS 9. This paper hopes to find out factors resulting in “higher loss given default for corporate loans” and “higher recovery rate of collateral after disposition” when importing the expected loss model before the full implementation of IFRS 9 in 2018 to be used as the reference for banking enterprises to measure and estimate the loss given default for corporate loans. It uses Modified Delphi Method and Analytic Hierarchy Process (AHP) and designs questionnaires in 2 stages; its subjects are 15 corporate loan credit checking staff and 15 debt collectors with experience in banking practice. The results show among factors resulting in “higher loss given default for corporate loans”, it can be observed from the empirical analysis that the experts interviewed selected “Operator and Management” as the most important dimension, followed by “Finance” and “Industry”; the top 2 indexes emphasized by them are “overdue payment of the operator’s principal debt or guaranteed debt in the most recent year” and “the accountant issues reports of adverse opinions or disclaimer of opinion in the financial statement in the most recent quarter”. In terms of factors resulting in “higher recovery rate of collateral after disposition”, all the experts interviewed sort the recovery rates of collaterals of different properties and the empirical results is: cash assets (excluding equity financial commodity) >real estate>guarantee of credit guarantee agency >other assets (including equity financial commodity); according to mortgage sequence setting of collaterals, the recovery rates are sorted: the first secured debt >the second secured debt. Keywords：Corporate loan、LGD、Recovery rate、IFRS 9、Basel、 Delphi Method、AHP
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TING, CHIA-QI, and 丁家麒. "A Study on Loss Given Default of Small and Medium - sized Enterprises in Taiwan." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/m79trv.
Full textAbstract:
碩士東吳大學
國際經營與貿易學系
105
Government requires banks and financial institutions in line with the implementation of Basel II in 2007, requiring banks should have internal risk management mechanism, banks and financial institutions to increase the risk sensitivity. Therefore, banks and financial institutions to develop an effective method to forecast LGD business loans to bank lending as a decision-making basis and reference, and use this method to reduce credit risk and reduce the bank's losses. This study suggests that, when the audit of financial institutions lending to SMEs, should pay attention to the study discussed in factors, as the basis for approval of the loan, reducing the risk of default , increase shareholders' equity.
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Fernandes, João Eduardo Dias. "Corporate credit risk modeling." Doctoral thesis, 2006. http://hdl.handle.net/10071/1401.
Full textAbstract:
Corporate credit risk modeling for privately-held firms is limited, although these firms represent a large fraction of the corporate sector worldwide. This study is an empirical application of credit scoring and rating techniques to a unique dataset on private firms bank loans of a European bank. It is divided in two chapters. The first chapter is concerned with modeling the probability of default. Several alternative scoring methodologies are presented, validated and compared. These methodologies include a multiple industry model, and a weighted sample model. Furthermore, two distinct strategies for grouping the individual scores into rating classes with PDs are developed, the first uses cluster algorithms and the second maps internal ratings to an external rating scale. Finally, the regulatory capital requirements under the New Basel Capital Accord are calculated for a simulated portfolio, and compared to the capital requirements under the current regulation. On the second chapter, we model long-term Loss-Given-Default on loan, guarantee and customer characteristics using a random, 7-year sample. Two alternative modeling strategies are tested, taking in consideration the highly non-normal shape of the recovery rate distribution, and a fractional dependent variable. The first strategy is based on Beta transformation of the dependent variable, while the second is based on Generalizes Linear Models. The methodology can be used for long-term LGD prediction of a corporate bank loan portfolio and to comply with the New Basel Capital Accord Advanced Internal Ratings Based approach requirements.A modelização do risco de crédito de empréstimos a empresas sem emissões cotadas em mercados financeiros é limitada, apesar do peso elevado deste segmento nas carteiras de crédito dos bancos. O objectivo deste estudo é contribuir para este ramo de literatura ao aplicar técnicas de medição dos dois principais parâmetros de risco de crédito a uma amostra aleatória extraída da base de dados de um banco europeu. A dissertação é composta por dois capítulos, o primeiro trata a modelização da probabilidade de incumprimento (PD), e o segundo a modelização da perda em caso de incumprimento (LGD). O primeiro capítulo começa por apresentar e comparar alternativas para a medição do credit score dos clientes, incluindo modelo de equações sectoriais múltiplas e outro com amostra ponderada. Em seguida é abordada a problemática de agrupar scores individuais em classes de risco com PDs associadas. Para tal, duas alternativas são propostas, a primeira usa técnicas de clustering, enquanto que a segunda baseia-se no mapeamento entre as classificações internas e uma escala de referência externa. No final do primeiro capítulo, e usando as estimativas de PD anteriormente calculadas, determinam-se os requisitos de capital regulamentar à luz do novo acordo de capital de Basileia, em contraste com os requisitos previstos no acordo actual. No segundo capítulo comparam-se duas alternativas para a modelização do LGD. Os modelos são estimados sob uma amostra aleatória de 7 anos, considerando-se como variáveis explicativas características dos empréstimos, garantias e clientes. Ambas as alternativas têm em consideração o facto da variável dependente ser uma fracção e de ter uma distribuição não normal. A primeira alternativa é baseada na transformação Beta da variável dependente, enquanto que a segunda é baseada em Generalized Linear Models.
ISCTE
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Seidler, Jakub. "Credit Risk in the Macroprudential Framework: Three Essays." Doctoral thesis, 2012. http://www.nusl.cz/ntk/nusl-305913.
Full textAbstract:
Charles University in Prague Faculty of Social Sciences Institute of Economic Studies Credit Risk in the Macroprudential Framework: Three Essays DISSERTATION Author: PhDr. Jakub Seidler Supervisor: prof. Ing. Oldřich Dědek, CSc Academic Year: 2011/2012 Abstract This thesis focuses on proper credit risk identification with respect to macroprudential policies, which should mitigate systemic risk accumulation and contribute to higher financial stability of the financial sector. The first essay deals with a key credit risk parameter - Loss Given Default (LGD). We illustrate how the LGD can be estimated with the help of an adjusted Mertonian structural approach. We present a derivation of the formula for expected LGD and show its sensitivity analysis with respect to other company structural parameters. Finally, we estimate the five-year expected LGDs for companies listed on Prague Stock Exchange and find that the average LGD for the analyzed sample is around 20-50%. The second essay examines the issue of how to determine whether the observed level of private sector credit is excessive in the context of the "countercyclical capital buffer", a macroprudential tool proposed in the new regulatory framework of Basel III by the Basel Committee on Banking Supervision. An empirical analysis of selected Central and...