Academic literature on the topic 'LGD (Loss Given Default)'
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Journal articles on the topic "LGD (Loss Given Default)"
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Van Dyk, Jenni, Jaun Lange, and Gary Van Vuuren. "The Impact Of Systemic Loss Given Default On Economic Capital." International Business & Economics Research Journal (IBER) 16, no. 2 (March 31, 2017): 87–100. http://dx.doi.org/10.19030/iber.v16i2.9884.
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Empirical studies have demonstrated that loan default probabilities (PD) and loss given defaults (LGD) are positively correlated because of a common, business cycle, dependency. Regulatory capital requirements demand that banks use downturn LGD estimates because the correlation between PD and LGD is not captured. Economic capital models are not bound by this constraint. We extend and implement a model which captures the PD and LGD correlation by exploring the link between defaults and recoveries from a systemic point of view. We investigate the impact of correlated defaults and resultant loss rates on a portfolio comprising default-sensitive financial instruments. We demonstrate that the systemic component of recovery risk (driven by macroeconomic conditions) exerts greater influence on loss estimation and fair risk pricing than its standalone component.
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Li, Shouwei, and Jianmin He. "Loss distribution of systemic defaults in different interbank networks." International Journal of Modern Physics C 27, no. 10 (August 29, 2016): 1650121. http://dx.doi.org/10.1142/s0129183116501217.
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We analyze the impact of the network structure, the default probability and the loss given default (LGD) on the loss distribution of systemic defaults in the interbank market, where network structures analyzed include random networks, small-world networks and scale-free networks. We find that the network structure has little effect on the shape of the loss distribution, whereas the opposite is true to the default probability; the LGD changes the shape of the loss distribution significantly when default probabilities are high; the maximum of the possible loss is sensitive to the network structure and the LGD.
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Metzler, Adam, and Alexandre Scott. "Importance Sampling in the Presence of PD-LGD Correlation." Risks 8, no. 1 (March 10, 2020): 25. http://dx.doi.org/10.3390/risks8010025.
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This paper seeks to identify computationally efficient importance sampling (IS) algorithms for estimating large deviation probabilities for the loss on a portfolio of loans. Related literature typically assumes that realised losses on defaulted loans can be predicted with certainty, i.e., that loss given default (LGD) is non-random. In practice, however, LGD is impossible to predict and tends to be positively correlated with the default rate and the latter phenomenon is typically referred to as PD-LGD correlation (here PD refers to probability of default, which is often used synonymously with default rate). There is a large literature on modelling stochastic LGD and PD-LGD correlation, but there is a dearth of literature on using importance sampling to estimate large deviation probabilities in those models. Numerical evidence indicates that the proposed algorithms are extremely effective at reducing the computational burden associated with obtaining accurate estimates of large deviation probabilities across a wide variety of PD-LGD correlation models that have been proposed in the literature.
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Joubert, Morne, Tanja Verster, and Helgard Raubenheimer. "Making use of survival analysis to indirectly model loss given default." ORiON 34, no. 2 (January 14, 2019): 107–32. http://dx.doi.org/10.5784/34-2-588.
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A direct or indirect modelling methodology can be used to predict Loss Given Default (LGD). When using the indirect LGD methodology, two components exist, namely, the loss severity component and the probability component. Commonly used models to predict the loss severity and the probability component are the haircut- and the logistic regression models, respectively. In this article, survival analysis was proposed as an improvement to the more traditional logistic regression method. The mean squared error, bias and variance for the two methodologies were compared and it was shown that the use of survival analysis enhanced the model's predictive power. The proposed LGD methodology (using survival analysis) was applied on two simulated datasets and two retail bank datasets, and according to the results obtained it outperformed the logistic regression LGD methodology. Additional benefits included that the new methodology could allow for censoring as well as predicting probabilities over varying outcome periods.
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Breed, Douw Gerbrand, Tanja Verster, Willem D. Schutte, and Naeem Siddiqi. "Developing an Impairment Loss Given Default Model Using Weighted Logistic Regression Illustrated on a Secured Retail Bank Portfolio." Risks 7, no. 4 (December 13, 2019): 123. http://dx.doi.org/10.3390/risks7040123.
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This paper proposes a new method to model loss given default (LGD) for IFRS 9 purposes. We develop two models for the purposes of this paper—LGD1 and LGD2. The LGD1 model is applied to the non-default (performing) accounts and its empirical value based on a specified reference period using a lookup table. We also segment this across the most important variables to obtain a more granular estimate. The LGD2 model is applied to defaulted accounts and we estimate the model by means of an exposure weighted logistic regression. This newly developed LGD model is tested on a secured retail portfolio from a bank. We compare this weighted logistic regression (WLR) (under the assumption of independence) with generalised estimating equations (GEEs) to test the effects of disregarding the dependence among the repeated observations per account. When disregarding this dependence in the application of WLR, the standard errors of the parameter estimates are underestimated. However, the practical effect of this implementation in terms of model accuracy is found to be negligible. The main advantage of the newly developed methodology is the simplicity of this well-known approach, namely logistic regression of binned variables, resulting in a scorecard format.
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Van Vuuren, Gary, Riaan De Jongh, and Tanja Verster. "The Impact Of PD-LGD Correlation On Expected Loss And Economic Capital." International Business & Economics Research Journal (IBER) 16, no. 3 (June 30, 2017): 157–70. http://dx.doi.org/10.19030/iber.v16i3.9975.
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The Basel regulatory credit risk rules for expected losses require banks use downturn loss given default (LGD) estimates because the correlation between the probability of default (PD) and LGD is not captured, even though this has been repeatedly demonstrated by empirical research. A model is examined which captures this correlation using empirically-observed default frequencies and simulated LGD and default data of a loan portfolio. The model is tested under various conditions dictated by input parameters. Having established an estimate of the impact on expected losses, it is speculated that the model be calibrated using banks' own loss data to compensate for the omission of correlation dependence. Because the model relies on observed default frequencies, it could be used to adapt in real time, forcing provisions to be dynamically allocated.
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Shi, Baofeng, Xue Zhao, Bi Wu, and Yizhe Dong. "Credit rating and microfinance lending decisions based on loss given default (LGD)." Finance Research Letters 30 (September 2019): 124–29. http://dx.doi.org/10.1016/j.frl.2019.03.033.
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Sanches, Guilherme Fernandes, and André Alves Portela Santos. "Validação da perda dado o descumprimento na abordagem IRB avançada." Brazilian Review of Finance 14, no. 2 (June 27, 2016): 299. http://dx.doi.org/10.12660/rbfin.v14n2.2016.60908.
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The goal of our paper is to contribute to the discussion about the most important aspects of the loss given default validation process, with special attention to the brazilian case, as the Central Bank of Brazil determines in Circular no 3.648/2013. The authors suggest the application of a few non-linear statistical measures to the study of dependence between default frequency and loss given default, like
Kendall ad Somers statistics and non-binary receiver operation characterisc (ROC). An estimation methodology for Downturn LGD is proposed, having as foundation a correlation adjustment derived
from expected loss and ordination of quantiles of the forecasted LGD distribution according to the dependence level for different credit portfolios.
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Schneider, Paul, Leopold Sögner, and Tanja Veža. "The Economic Role of Jumps and Recovery Rates in the Market for Corporate Default Risk." Journal of Financial and Quantitative Analysis 45, no. 6 (September 17, 2010): 1517–47. http://dx.doi.org/10.1017/s0022109010000554.
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AbstractUsing an extensive cross section of U.S. corporate credit default swaps (CDSs), 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.
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Orlando, Giuseppe, and Roberta Pelosi. "Non-Performing Loans for Italian Companies: When Time Matters. An Empirical Research on Estimating Probability to Default and Loss Given Default." International Journal of Financial Studies 8, no. 4 (November 9, 2020): 68. http://dx.doi.org/10.3390/ijfs8040068.
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Within bank activities, which is normally defined as the joint exercise of savings collection and credit supply, risk-taking is natural, as in many human activities. Among risks related to credit intermediation, credit risk assumes particular importance. It is most simply defined as the potential that a bank borrower or counterparty fails to fulfil correctly at maturity the pecuniary obligations assumed as principal and interest. Whenever this happens, a loan is non-performing. Among the main risk components, the Probability of Default (PD) and the Loss Given Default (LGD) have been the subject of greater interest for research. In this paper, logit model is used to predict both components. Financial ratios are used to estimate the PD. Time of recovery and presence of collateral are used as covariates of the LGD. Here, we confirm that the main driver of economic losses is the bureaucratically encumbered recovery system and the related legal environment. The long time required by Italian bureaucratic procedures, simply put, seems to lower dramatically the chance of recovery from defaulting counterparties.
Dissertations / Theses on the topic "LGD (Loss Given Default)"
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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.
Books on the topic "LGD (Loss Given Default)"
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Honal, Martin. Loss Given Default von Mobilien-Leasingverträgen. Wiesbaden: Gabler, 2009. http://dx.doi.org/10.1007/978-3-8349-9941-2.
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Min, Qi. Loss given default of high loan-to-value residential mortgages. Washington, DC: Office of the Comptroller of the Currency, 2007.
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Simon, Gleeson. Part II Commercial Banking, 10 The Internal Ratings-Based Approach. Oxford University Press, 2018. http://dx.doi.org/10.1093/law/9780198793410.003.0010.
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This chapter discusses the internal ratings-based approach (IRB). The IRB permits a bank to use its internal models to derive risk weights for particular exposures. There are two available bases for the IRB: foundation (F-IRB), which permits the bank to model Probability of Default (PD), but relies on regulatory standard figures to determine Loss Given Default (LGD) and Exposure at Default (EAD); and advanced (A-IRB), in which all three of these are modelled. The A-IRB IRB approach models PD, LGD, EAD, and M. Both IRB approaches model both expected loss (EL) and unexpected loss (UL), and IRB banks are expected to recognise the EL derived from their models in their capital calculations. Consequently, a bank using an IRB approach will generally have a different total capital level from that which it would have if it were an SA bank.
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Hartmann-Wendels, Thomas, and Martin Honal. Loss Given Default von Mobilien-Leasingverträgen. Gabler Verlag, 2009.
Find full textBook chapters on the topic "LGD (Loss Given Default)"
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Honal, Martin. "Berechnung und Schätzung des LGD von Leasingverträgen." In Loss Given Default von Mobilien-Leasingverträgen, 32–76. Wiesbaden: Gabler, 2009. http://dx.doi.org/10.1007/978-3-8349-9941-2_3.
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Honal, Martin. "Einfluss der gesamtwirtschaftlichen Entwicklung auf den LGD." In Loss Given Default von Mobilien-Leasingverträgen, 86–123. Wiesbaden: Gabler, 2009. http://dx.doi.org/10.1007/978-3-8349-9941-2_5.
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Honal, Martin. "Einflussfaktoren auf den LGD von PKW-Leasingverträgen." In Loss Given Default von Mobilien-Leasingverträgen, 124–57. Wiesbaden: Gabler, 2009. http://dx.doi.org/10.1007/978-3-8349-9941-2_6.
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Balthazar, Laurent. "Loss Given Default." In From Basel 1 to Basel 3, 188–99. London: Palgrave Macmillan UK, 2006. http://dx.doi.org/10.1057/9780230501171_13.
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Scandizzo, Sergio. "Loss Given Default Models." In The Validation of Risk Models, 78–92. London: Palgrave Macmillan UK, 2016. http://dx.doi.org/10.1057/9781137436962_6.
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Izzi, Luisa, Gianluca Oricchio, and Laura Vitale. "Loss Given Default Estimation." In Basel III Credit Rating Systems, 102–13. London: Palgrave Macmillan UK, 2012. http://dx.doi.org/10.1057/9780230361188_5.
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Keller, Helmut. "L: Lärmschutzverordnung – Loss given default." In Praxishandbuch Finanzwissen, 323–31. Wiesbaden: Springer Fachmedien Wiesbaden, 2013. http://dx.doi.org/10.1007/978-3-658-00750-8_24.
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Ruiz, Ignacio. "Default Probability, Loss Given Default, and Credit Portfolio Models." In XVA Desks — A New Era for Risk Management, 104–25. London: Palgrave Macmillan UK, 2015. http://dx.doi.org/10.1057/9781137448200_7.
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Honal, Martin. "Einleitung." In Loss Given Default von Mobilien-Leasingverträgen, 1–4. Wiesbaden: Gabler, 2009. http://dx.doi.org/10.1007/978-3-8349-9941-2_1.
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Honal, Martin. "Grundlagen des Leasinggeschäftes." In Loss Given Default von Mobilien-Leasingverträgen, 5–31. Wiesbaden: Gabler, 2009. http://dx.doi.org/10.1007/978-3-8349-9941-2_2.
Full textConference papers on the topic "LGD (Loss Given Default)"
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LIU, YANG. "IMPACT ASSESSMENT OF LOSS GIVEN DEFAULT (LGD) MODELS’ RISK ON REGULATORY CAPITAL: A BAYESIAN APPROACH." In RISK ANALYSIS 2018. Southampton UK: WIT Press, 2018. http://dx.doi.org/10.2495/risk180101.
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Mattarocci, Gianluca, Claudio Giannotti, and Xenia Scimone. "Loss Given Default for residential real estate banks: Evidence from the Euro area." In 24th Annual European Real Estate Society Conference. European Real Estate Society, 2017. http://dx.doi.org/10.15396/eres2017_130.
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Pachidis, Vassilios, Pericles Pilidis, Ioannis Templalexis, Theodosios Alexander, and Petros Kotsiopoulos. "Prediction of Engine Performance Under Compressor Inlet Flow Distortion Using Streamline Curvature." In ASME Turbo Expo 2006: Power for Land, Sea, and Air. ASMEDC, 2006. http://dx.doi.org/10.1115/gt2006-90806.
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Traditionally, engine performance has been simulated based on non-dimensional maps for compressors and turbines. Component characteristic maps assume by default a given state of inlet conditions which can not be easily altered in order to simulate two-dimensional or three-dimensional flow phenomena. Inlet flow distortion, for example, is usually simulated by applying empirical correction factors and modifiers to default component characteristics, alternatively, the parallel compressor theory may be applied. The accuracy of the above methods has been rather questionable since they are unable to capture in sufficient fidelity component-level, complex physical processes and analyze them in the context of the whole engine performance. The technique described in this paper integrates a zero-dimensional (non-dimensional) gas turbine modeling and performance simulation system and a two-dimensional, streamline curvature compressor software. The two-dimensional compressor software can fully define the characteristics of a compressor at several operating condition and is subsequently used in the zero-dimensional cycle analysis to provide a more accurate, physics-based estimate of compressor performance under clean and distorted inlet conditions, replacing the default compressor maps. The high-fidelity component communicates with the lower fidelity cycle via a fully automatic and iterative process for the determination of the correct operating point. This study discusses in detail the development, validation and integration of the two-dimensional, streamline curvature compressor software and presents the various loss models used in the code. It also discusses the relative changes in the performance of a two-stage, experimental compressor with different types of radial pressure distortion obtained by running the two-dimensional streamline curvature compressor software independently. Moreover, the performance of a notional engine model, utilizing the coupled, two-dimensional compressor, under distorted conditions is discussed in detail and compared against the engine performance under clean conditions.