Relevant bibliographies by topics / PD (Probability of default)

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  2. Dissertations / Theses
  3. Books
  4. Book chapters
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Academic literature on the topic 'PD (Probability of default)'

Author: Grafiati
Published: 4 June 2021
Last updated: 13 February 2022

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Journal articles on the topic "PD (Probability of default)"

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Burova, Anna, Henry Penikas, and Svetlana Popova. "Probability of Default Model to Estimate Ex Ante Credit Risk." Russian Journal of Money and Finance 80, no. 3 (September 2021): 49–72. http://dx.doi.org/10.31477/rjmf.202103.49.

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A genuine measure of ex ante credit risk links borrower’s financial position with the odds of default. Comprehension of a borrower’s financial position is proxied by the derivatives of its filled financial statements, i.e., financial ratios. We identify statistically significant relationships between shortlisted financial ratios and subsequent default events and develop a probability of default (PD) model that assesses the likelihood of a borrower going into delinquency at a one-year horizon. We compare the PD model constructed against alternative measures of ex ante credit risk that are widely used in related literature on bank risk taking, i.e., credit quality groups (prudential reserve ratios) assigned to creditors by banks and the credit spreads in interest rates. We find that the PD model predicts default events more accurately at a horizon of one year compared to prudential reserve rates. We conclude that the measure of ex ante credit risk developed is feasible for estimating risk-taking behaviour by banks and analysing shifts in portfolio composition.
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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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Filusch, Tobias. "Risk assessment for financial accounting: modeling probability of default." Journal of Risk Finance 22, no. 1 (October 28, 2020): 1–15. http://dx.doi.org/10.1108/jrf-02-2020-0033.

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Purpose This paper aims to introduce and tests models for point-in-time probability of default (PD) term structures as required by international accounting standards. Corresponding accounting standards prescribe that expected credit losses (ECLs) be recognized for the impairment of financial instruments, for which the probability of default strongly embodies the included default risk. This paper fills the research gap resulting from a lack of models that expand upon existing risk management techniques, link PD term structures of different risk classes and are compliant with accounting standards, e.g. offering the flexibility for business cycle-related variations. Design/methodology/approach The author modifies the non-homogeneous continuous-time Markov chain model (NHCTMCM) by Bluhm and Overbeck (2007a, 2007b) and introduces the generalized through-the-cycle model (GTTCM), which generalizes the homogeneous Markov chain approach to a point-in-time model. As part of the overall ECL estimation, an empirical study using Standard and Poor’s (S&P) transition data compares the performance of these models using the mean squared error. Findings The models can reflect observed PD term structures associated with different time periods. The modified NHCTMCM performs best at the expense of higher complexity and only its cumulative PD term structures can be transferred to valid ECL-relevant unconditional PD term structures. For direct calibration to these unconditional PD term structures, the GTTCM is only slightly worse. Moreover, it requires only half of the number of parameters that its competitor does. Both models are useful additions to the implementation of accounting regulations. Research limitations/implications The tests are only carried out for 15-year samples within a 35-year span of available S&P transition data. Furthermore, a point-in-time forecast of the PD term structure requires a link to the business cycle, which seems difficult to find, but is in principle necessary corresponding to the accounting requirements. Practical implications Research findings are useful for practitioners, who apply and develop the ECL models of financial accounting. Originality/value The innovative models expand upon the existing methodologies for assessing financial risks, motivated by the practical requirements of new financial accounting standards.
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Kuznichenko, Yana, Mariia V. Dykha, Natalia Pavlova, Serhiy Frolov, and Olha Hryhorash. "Defining the probability of bank debtors’ default using financial solvency assessment models." Banks and Bank Systems 13, no. 2 (May 25, 2018): 1–11. http://dx.doi.org/10.21511/bbs.13(2).2018.01.

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Due implementation of debtors’ financial solvency assessment models by Ukrainian banks with the aim of calculating the probability of their default (PD) is the next step towards the integration of Ukrainian banking system into global banking community, convergence of methodical approaches to assessing the credit risk with standards of international practice, possibility of using IRB-approach (an approach based on internal ratings) for calculating the regulatory requirements to capital adequacy.The analysis of approaches to bank credit portfolio segmentation according to types of debtors and debtors’ financial solvency assessment models, depending on the performed segmentation and accumulated bank statistical data, from the point of view of its suitability for Ukrainian banks, will enable the banks to choose the most suitable ones for implementation taking into account nature and complexity of operations performed.Such approaches will be more adapted to minimum capital requirements, simultaneously agreeing with national supervisory priorities.
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Khromova, Ella. "Dynamic Mapping of Probability of Default and Credit Ratings of Russian Banks." Journal of Corporate Finance Research / Корпоративные Финансы | ISSN: 2073-0438 14, no. 4 (December 1, 2020): 31–46. http://dx.doi.org/10.17323/j.jcfr.2073-0438.14.4.2020.31-46.

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Investors are interested in a quantitative measure of banks’ credit risk. This paper maps the credit ratings of Russian banks to default probabilities for different time horizons by constructing an empirical dynamic calibration scale. As such, we construct a dynamic scale of credit risk calibration to the probability of default (PD).Our study is based on a random sample of 395 Russian banks (86 of which defaulted) for the period of 2007-2017. The scale proposed by this paper has three features which distinguish it from existing scales: dynamic nature (quarterly probability of default estimates), compatibility with all rating agencies (base scale credit ratings), and a focus on Russian banks.Our results indicate that banks with high ratings are more stable just after the rating assignment, while a speculative bank’s probability of default decreases over time. Hence, we conclude that investors should account for not only the current rating grade of a bank, but also how long ago it was assigned. As a result, a rising capital strategy was formulated: the better a bank’s credit rating, the shorter the investment horizon should be and the closer the date of investment should be to the rating assignment date in order to minimise credit risk.The scientific novelty of this paper arises from the process of calibration of a rating grade to dynamic PD in order to evaluate the optimal time horizon of investments into a bank in each rating class. In practical terms, investors may use this scale not only to obtain a desired credit rating, but also to identify quantitative measure of credit risk, which will help to plan investment strategies and to calculate expected losses.
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Gupta, Vandana. "An Empirical Analysis of Default Prediction Models: Evidence from lndian Listed Companies." Journal of Prediction Markets 8, no. 3 (January 8, 2015): 1–23. http://dx.doi.org/10.5750/jpm.v8i3.946.

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This paper attempts to evaluate the predictive ability of three default prediction models: the market-based KMV model, the Z-score model using discriminant analysis (DA), and the logit model; and identifies the key default drivers. The research extends prior empirical work by modeling and testing the impact of financial ratios, macro-economic factors, corporate governance and firm-specific variables in predicting default. For the market-based model, the author has extended the works of KMV in developing a suitable algorithm for determining probability of default (PD). While for the KMV model, the continuous observations of PD are used as the dependent variable, for the accounting-based models, ratings assigned are the proxy for default (those rated ’D’ are defaulted and rated ‘AAA’ and ‘A’ are solvent). The research findings largely support the hypothesis that solvency, profitability and liquidity ratios do impact the default risk, but adding other covariates improves the predictive ability of the models. Through this study, the author recommends that accounting –based models and market based models are conceptually different. While market-based models are forward looking and inclusion of market data makes the default risk quantifiable; to make the PD more exhaustive, it is important to factor in the information provided in the financial statements. The conclusions drawn are that the disclosures in financial statements can help predict default risk as financial distress risk is likely to evolve over time and will be reflected in financial statements beyond accounting ratios. Moreover this will also help divulge “creative accounting” practices by corporates.
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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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Hu, Kuang-Hua, Shih-Kuei Lin, Yung-Kang Ching, and Ming-Chin Hung. "Goodness-of-Fit of Logistic Regression of the Default Rate on GDP Growth Rate and on CDX Indices." Mathematics 9, no. 16 (August 13, 2021): 1930. http://dx.doi.org/10.3390/math9161930.

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Under the Basel II and Basel III agreements, the probability of default (PD) is a key parameter used in calculating expected credit loss (ECL), which is typically defined as: PD × Loss Given Default × Exposure at Default. In practice or in regulatory requirements, gross domestic product (GDP) has been adopted in the PD estimation model. Due to the problem of excessive fluctuation and highly volatile ECL estimation, models that produce satisfactory PD and thus ECL estimations in the context of existing risk management techniques are lacking. In this study, we explore the usage of the credit default swap index (CDX), a market’s expectation of future PD, as a predictor of the default rate (DR). By comparing the goodness-of-fit of logistic regression, several conclusions are drawn. Firstly, in general, GDP has considerable explanatory power for the default rate which is consistent with current models in practice. Secondly, although both GDP and CDX fit the DR well for rating B class, CDX has a significantly better fit of DR for ratings [A, Baa, Ba]. Thirdly, compared with low-rated companies, the relationship between the DR and GDP is relatively weak for rating A. This phenomenon implies that, in addition to using macroeconomic variables and firm-specific explanatory variables in the PD estimation model, high-rated companies exhibit a greater need to use market supplemental information, such as CDX, to capture the changes in the DR.
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Suárez, Rebeca Peláez, Ricardo Cao Abad, and Juan M. Vilar Fernández. "A Doubly Smoothed PD Estimator in Credit Risk." Proceedings 54, no. 1 (September 1, 2020): 55. http://dx.doi.org/10.3390/proceedings2020054055.

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In this work a doubly smoothed probability of default (PD) estimator is proposed based on a smoothed version of the survival Beran’s estimator. The asymptotic properties of both the smoothed survival and PD estimators are proved and their behaviour is analyzed by simulation. The results allow us to conclude that the time variable smoothing reduce the error committed in the PD estimation.
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Kim, Myung Jig, Sung Hwan Shin, and Hong Sun Song. "Estimating Credit Rating and Transition Matrix of Savings Bank Industry Based upon IRB-Approach." Journal of Derivatives and Quantitative Studies 13, no. 2 (November 30, 2005): 61–85. http://dx.doi.org/10.1108/jdqs-02-2005-b0003.

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This paper proposes a method that estimates credit ratings by mapping empirical probability of default (PD) and standardized historical financial ratios. Unlike standard approaches such as the parametric logit model. discriminant analysis. neural network. and survival function model. the proposed approach has an advantage of offering a multiple credit rating categories. as opposed to either default or not default. of obligors. It would provide an useful information to practitioners because the probability of default for each credit rating category is a critical input under New Basel Capital Accord. Emoirical results based upon the historical PD and financial ratios of Korean savings bank industry from 2000 and 2003 suggest that the industry’s average credit rating belong to a speculative grade. that is BB and below. In addition, the computed transition matrix indicates that volatility of transition matrix fluctuates substantially each year and the orobability of staying in the same rating category at the end of the year tended to be much smaller than the average reported by the rating agencies for the overall Korean companies. The proposed method can easily be applied to industries other than savings bank industry.
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Dissertations / Theses on the topic "PD (Probability of default)"

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Santos, Bárbara Leitão. "Practical approach for probability of default estimation under IFRS 9." Master's thesis, Instituto Superior de Economia e Gestão, 2018. http://hdl.handle.net/10400.5/17350.

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Mestrado em Mathematical Finance
This report is part of the conclusion of the Master degree in Mathematical Finance, as a result of a 6-month internship at EY, in Financial Services - Advisory. Due to a recent financial crisis, credit entities had to deal with uncertainty, being credit risk one of the main concerns. Risk management in this type of entities is crucial to assure financial stability, and therefore, there is always a constant need of improvement. During the financial crisis of 2008, risk management failed its man purpose since risk models reveal insufficient to capture the risk deterioration on exposures and fail to estimate credit losses under a change in the economic cycle. Therefore, IFRS 9 becomes the new standard imposed by IASB, in order to replace IAS 39. This internship was a vector to expand my knowledge concerning impairment models and the new regulatory framework of the International Accounting Standard Board based on IFRS 9 Financial instruments, by studying a general approach on a specific perspective of a Portuguese bank. This report focuses on collective impairment, regarding the choices and validation of the model for the risk parameter PD used by the bank institution in analysis under this new standard.
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Kauffmann, Luiz Henrique Outi. "Uma abordagem Forward-Looking para estimar a PD segundo IFRS9." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/55/55137/tde-06112018-182558/.

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Este trabalho tem por objetivo discutir as metodologias de estimação da PD utilizadas na indústria financeira. Além disso, contextualizar a aplicação do trabalho ao IFRS9 e seu direcionamento para o tema de Risco de Crédito. Historicamente os grandes bancos múltiplos utilizam variadas metodologias econométricas para modelar a Probabilidade de Descumprimento (PD),um dos métodos mais tradicionais é a regressão logística, entretanto com a necessidade do cálculo da Perda Esperada de Crédito através do IFRS9, se torna necessário mudar o paradigma de estimação para uma abordagem forward-looking, isto está sendo interpretado por muitas instituições e consultorias como a inclusão de fatores e variáveis projetadas dentro do processo de estimação, ou seja, não serão utilizados apenas os dados históricos para prever o descumprimento ou inadimplência. Dentro deste contexto será proposto uma abordagem que une a estimação da Probabilidade de Descumprimento com a inclusão de um fator foward-looking.
This paper aims to discuss the methodologies used to estimate the Probability Of Default used in the financial industry. In addition, contextualize the application of the work to IFRS9 requirements and its targeting to the Credit Risk theme. Historically large multi-banks use a variety of econometric methodologies to model the Probability of Default, one of the more traditional methods is logistic regression. However, with the need to calculate the expected credit loss through IFRS9, it becomes necessary to change the estimation paradigm to a forwardlooking approach, this is being interpreted by many institutions and consultancies companies as the inclusion of factors and variables projected within the estimation process, that is, not only historical data are used to predict the default. Within this context will be proposed an approach that joins the estimation of Probability of Default with the inclusion of a forward-looking factor.
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Pereira, Bernardo Vieira Gonçalves. "Estudo sobre evolução do balanço de um banco em situação de stress económico : modelo macroeconómico de PD." Master's thesis, Instituto Superior de Economia e Gestão, 2019. http://hdl.handle.net/10400.5/19770.

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Mestrado em Econometria Aplicada e Previsão
Este relatório de estágio enquadra-se no âmbito do Trabalho Final de Mestrado para o Mestrado de Econometria Aplicada e Previsão do Instituto Superior de Economia e Gestão (ISEG). O estágio decorreu na Deloitte Consultores, S.A., num dos escritórios de Lisboa, com o intuito de me preparar para a realidade do mercado de trabalho através de um processo de integração na empresa durante a realização das atividades propostas. Neste relatório pretende-se expor um retrato daquilo que foram os meus primeiros seis meses de trabalho na Deloitte, através da descrição aprofundada do tema de trabalho, procurando sempre relacioná-lo com os conceitos adquiridos no decorrer do mestrado. A Deloitte faz parte das Big 4, grupo de empresas que domina o setor de serviços profissionais, sendo de realçar o seu grau de exigência e profissionalismo nos projetos a que se compromete. Na Deloitte, as minhas funções passaram pela análise das taxas de incumprimento e de migrações entre classe de risco de uma carteira de clientes empresa de um banco, tendo estabelecido um modelo econométrico que permite a previsão da evolução dessas taxas com base em variáveis macroeconómicas. Este tipo de modelos é aplicado na obtenção da perda esperada usada, por exemplo, no cálculo de provisões por imparidade e em testes de stress.
This report consists of my thesis for the Master in Applied Econometrics and Forecasting from ISEG, Lisbon School of Econmics. My internship took place in Deloitte Consultores, S.A., in one of the Lisbon offices, with the intent of not only adapting myself to the labor market reality but also to get acquainted with the company practices and costumes while performing my planned activities. In this report it is exposed my first six months of work for this company, through a detailed description of what was done, while always attempting to correlate it with the knowledge acquired thoughout my master degree. Deloitte is part of the Big 4, a group of consulting companies that rule the great majority of the market, and it is known for its high work quality and demand. In Deloitte, my job was to analyse default ratios and migration matrices, resultant from an undisclosed financial institution's portfolio, producing a macroeconomic regression model that would allow for the forecast of this default probability. These kind of models, to obtain estimates of the Expected Loss, are used, for example, in the computation of impairment provisions and stress tests.
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Čabrada, Jiří. "Kreditní rizika z pohledu Basel II." Master's thesis, Vysoká škola ekonomická v Praze, 2007. http://www.nusl.cz/ntk/nusl-5575.

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The thesis "Credit risk from Basel II point of view" deals with new capital concept with main focus on the credit risk. The particular emphasis is laid on the chief issue of Basel II concept i.e. internal models. The thesis quite in detail describes the usage of basel parameters - LGD particularly - in various day-to-day business processes of credit institutions. An individual part of the thesis is devoted to credit risk mitigants and their impacts on the amount of capital requirements. The analysis carried out precedent Basel II implementation indicated the launching of Basel II should imply risk weighted assests to credit risk decline. This documents the last chapter.
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Zhang, Jie. "Modelling examples of loss given default and probability of default." Thesis, University of Southampton, 2011. https://eprints.soton.ac.uk/172581/.

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The Basel II accord regulates risk and capital management requirements to ensure that a bank holds enough capital proportional to the exposed risk of its lending practices. Under the advanced internal ratings based (IRB) approach, Basel II allows banks to develop their own empirical models based on historical data for probability of default (PD), loss given default (LGD) and exposure at default (EAD). This thesis looks at some examples of modelling LGD and PD. One part of this thesis investigates modelling LGD for unsecured personal loans. LGD is estimated through estimating Recovery Rate (RR, RR=1-LGD). Firstly, the research examines whether it is better to estimate RR or Recovery Amounts. Linear regression and survival analysis models are built and compared when modelling RR and Recovery Amount, so as to predict LGD. Secondly, mixture distribution models are developed based on linear regression and survival analysis approaches. A comparison between single distribution models and mixture distribution models is made and their advantages and disadvantages are discussed. Thirdly, it is examined whether short-term recovery information is helpful in modelling final RR. It is found that early payment patterns and short-term RR after default are very significant variables in final RR prediction models. Thus, two-stage models are built. In the stage-one model short-term Recoveries are predicted, and then the predicted short-term Recoveries are used in the final RR prediction models. Fourthly, macroeconomic variables are added in both the short-term Recoveries models and final RR models, and the influences of macroeconomic environment on estimating RR are looked at. The other part of this thesis looks at PD modelling. One area where there is little literature of PD modelling is in invoice discounting, where a bank lends a company a proportion of the amount it has invoiced its customers in exchange for receiving the cash flow from these invoices. Default here means that the invoicing company defaults, at which point the bank cannot collect on the invoices. Like other small firms, the economic conditions affect the default risk of invoicing companies. The aim of this research is to develop estimates of default that incorporate the details of the firm, the current and past position concerning the invoices, and also economic variables.
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Zollinger, Lance M. "Probability of default rating methodology review." Thesis, Kansas State University, 2014. http://hdl.handle.net/2097/18811.

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Master of Agribusiness
Department of Agricultural Economics
Allen M. Featherstone
Institutions of the Farm Credit System (FCS) focus on risk-based lending in accordance with regulatory direction. The rating of risk also assists retail staff in loan approval, risk-based pricing, and allowance decisions. FCS institutions have developed models to analyze financial and related customer information in determining qualitative and quantitative risk measures. The objective of this thesis is to examine empirical account data from 2006-2012 to review the probability of default (PD) rating methodology within the overall risk rating system implemented by a Farm Credit System association. This analysis provides insight into the effectiveness of this methodology in predicting the migration of accounts across the association’s currently-established PD ratings where negative migration may be an apparent precursor to actual loan default. The analysis indicates that average PD ratings hold relatively consistent over the years, though the distribution of the majority of PD ratings shifted to higher quality by two rating categories over the time period. Various regressions run in the analysis indicate that the debt to asset ratio is most consistently statistically significant in estimating future PD ratings. The current ratio appears to be superior to working capital to gross profit as a liquidity measure in predicting PD rating migration. Funded debt to EBITDA is more effective in predicting PD rating movement as a measure of earnings to debt than gross profit to total liabilities, although the change of these ratios over time appear to be weaker indicators of the change in PD rating potentially due to the variable nature of annual earnings of production agriculture operations due to commodity price volatility. The debt coverage ratio is important as it relates to future PD migration, though the same variability in commodity price volatility suggests the need implement multi-year averaging for calculation of earnings-based ratios. These ratios were important in predicting the PD rating of observations one year into the future for production agriculture operations. To further test the predictive ability of the PD ratings, similar regression analyses were completed comparing current year rating and ratios to future PD ratings beyond one year, specifically for three and five years. Results from these regression models indicate that current year PD rating and ratios are less effective in predicting future PD ratings beyond one year. Furthermore, because of the variation in regression results between the analyses completed for one, three and five years into the future, it is important to regularly capture ratio and rating information, at least annually.
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Lan, Yi. "Survival Probability and Intensity Derived from Credit Default Swaps." Digital WPI, 2012. https://digitalcommons.wpi.edu/etd-theses/82.

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This project discusses the intensity and survival probability derived from Credit Default Swaps (CDS). We utilize two models, the reduced intensity model and the Shift Square Root Diffusion (SSRD) model. In the reduced intensity model, we assume a deterministic intensity and implement a computer simulation to derive the survival probability and intensity from the CDS market quotes of the company. In the SSRD model, the interest rate and intensity are both stochastic and correlated. We discuss the impaction of correlation on the interest rate and intensity. We also conduct a Monte Carlo simulation to determine the dynamics of stochastic interest rate and intensity.
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Caetano, João Manuel Nunes. "Predictive models of probability of default : an empirical application." Master's thesis, Instituto Superior de Economia e Gestão, 2014. http://hdl.handle.net/10400.5/7704.

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Mestrado em Finanças
Este estudo tem como objetivo realizar uma pesquisa dos modelos de previsão do incumprimento a empresas listadas em bolsa. Foram abordadas as metodologias do modelo de Merton (1974), modelo Contabilístico e Híbrido. Testou-se uma amostra de 172 empresas presentes no mercado Americano dos setores do Consumo, Distribuição, Produção e Telecomunicações nas quais 82 entram em incumprimento. Para cada metodologia, a capacidade preditiva foi testada através dos erros Tipo I e II. Os resultados sugerem que o modelo Híbrido, i.e., a combinação de modelos de mercado e análise contabilística, confere maior poder de precisão na classificação de incumprimento, ao invés de cada modelo individualmente.
This study intends to conduct a survey of Probability of Default models to listed companies. The methodologies of Merton (1974) model, Accounting model and Hybrid were addressed. We tested a sample of 172 American companies in the sectors of Consumer Products, Distribution, Manufacturing and Telecommunications in which 82 entered into default. For each methodology, the predictive ability was tested with Type I and II errors. The results suggests that the Hybrid model, i.e. a combination of market models and accounting analysis, have a better performance in the classification of credit default than each model individually.
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Azeredo, Daniela Rita Charrua Cabral de. "Structural models to estimate financial institution´s default probability." Master's thesis, Instituto Superior de Economia e Gestão, 2014. http://hdl.handle.net/10400.5/7898.

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Mestrado em Finanças
Neste estudo procurámos, no âmbito do Modelo de Merton (1973), determinar a Distância ao Incumprimento (DD) para uma amostra de bancos Ibéricos. Através da especificação de três diferentes Barreiras de Imcumprimento (DB), foi possivel obter diferentes resultados, sublinhando a importância da DB para output do modelo. Durante a crise, o risco de liquidez foi atenuado pelas políticas de cedência de liquidez levadas a cabo pelo BCE. As definições usadas para db1 e db2, diferem na forma como são tratados os emprestimos do BCE, permitindo implementar um procedimento assente no cálculo da DD para quantificar a redução no risco dos bancos induzida por estas medidas. Os nossos resultados demonstram que as políticas do BCE reduziram o risco de incumprimento dos bancos que constituem a amostra.
This paper is intended to model the default probabilities for selected Iberian Financial Institutions through the application of Merton's Model (1973) framework. Through the use of three different Default Barrier (db) definitions, we were able to obtain very different outputs, stressing how crucial db definition is to the structural model output. Throughout this crisis, liquidity risk was, in some dimension, offset by the ECB funding policies. db1 and db2 definitions, differing only on the way Central Bank loans were treated, were convenient to test non-standard applications of the model. In our study we introduce and test a procedure anchored on Distance to Distress calculation, to quantify the reduction in risk induced by ECB measures, finding that ECB actions effectively reduced bank's default risk.
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Kornfeld, Sarah. "Predicting Default Probability in Credit Risk using Machine Learning Algorithms." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-275656.

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This thesis has explored the field of internally developed models for measuring the probability of default (PD) in credit risk. As regulators put restrictions on modelling practices and inhibit the advance of risk measurement, the fields of data science and machine learning are advancing. The tradeoff between stricter regulation on internally developed models and the advancement of data analytics was investigated by comparing model performance of the benchmark method Logistic Regression for estimating PD with the machine learning methods Decision Trees, Random Forest, Gradient Boosting and Artificial Neural Networks (ANN). The data was supplied by SEB and contained 45 variables and 24 635 samples. As the machine learning techniques become increasingly complex to favour enhanced performance, it is often at the expense of the interpretability of the model. An exploratory analysis was therefore made with the objective of measuring variable importance in the machine learning techniques. The findings from the exploratory analysis will be compared to the results from benchmark methods that exist for measuring variable importance. The results of this study shows that logistic regression outperformed the machine learning techniques based on the model performance measure AUC with a score of 0.906. The findings from the exploratory analysis did increase the interpretability of the machine learning techniques and were validated by the results from the benchmark methods.
Denna uppsats har undersökt internt utvecklade modeller för att estimera sannolikheten för utebliven betalning (PD) inom kreditrisk. Samtidigt som nya regelverk sätter restriktioner på metoder för modellering av kreditrisk och i viss mån hämmar utvecklingen av riskmätning, utvecklas samtidigt mer avancerade metoder inom maskinlärning för riskmätning. Således har avvägningen mellan strängare regelverk av internt utvecklade modeller och framsteg i dataanalys undersökts genom jämförelse av modellprestanda för referens metoden logistisk regression för uppskattning av PD med maskininlärningsteknikerna beslutsträd, Random Forest, Gradient Boosting och artificiella neurala nätverk (ANN). Dataunderlaget kommer från SEB och består utav 45 variabler och 24 635 observationer. När maskininlärningsteknikerna blir mer komplexa för att gynna förbättrad prestanda är det ofta på bekostnad av modellens tolkbarhet. En undersökande analys gjordes därför med målet att mäta förklarningsvariablers betydelse i maskininlärningsteknikerna. Resultaten från den undersökande analysen kommer att jämföras med resultat från etablerade metoder som mäter variabelsignifikans. Resultatet av studien visar att den logistiska regressionen presterade bättre än maskininlärningsteknikerna baserat på prestandamåttet AUC som mätte 0.906. Resultatet from den undersökande analysen för förklarningsvariablers betydelse ökade tolkbarheten för maskininlärningsteknikerna. Resultatet blev även validerat med utkomsten av de etablerade metoderna för att mäta variabelsignifikans.
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Books on the topic "PD (Probability of default)"

1

Kiefer, Nicholas M. The probability approach to default probabilities. Washington, DC: Office of the Comptroller of the Currency, 2007.

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Moro, Virilo. Sovereign bond default risk: An estimation of Brady bonds default probability with risk aversion. [s.l.]: typescript, 1997.

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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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Balkan, Erol M. Country risk, probability of default and optimal lending. 1988.

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Datz, Giselle. Sovereign Debt Default. Oxford University Press, 2017. http://dx.doi.org/10.1093/acrefore/9780190846626.013.299.

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Sovereign borrowing and debt default have long been a part of a nation’s existence. Sovereign debt defaults (that is, the suspension of interest or principal payment on due debt) were common from the sixteenth century, when Edward III declared a default after military defeat in 1340, to the nineteenth century, when Latin American countries defaulted on some of their debts. Early loans were made in the form of repayable taxes until the system evolved to allow for sovereign loans, transparent enough that secondary markets for these debts were soon developed. A government may default on its debt due to unwillingness or inability to pay. In both cases, default is a difficult political decision whose real costs remain somewhat ambiguous from a theoretical standpoint. The costs of default are often contingent on the type of debt restructuring deal reached between the debtor and the creditor. The scholarly literature on sovereign debt crises is substantial, particularly with respect to the economic, legal, and political costs of default. More recent theoretical work has focused on the trend toward increased domestic debt, which is expected to help reduce the probability of a debt crisis. However, domestically issued sovereign debt can lead to other types of risk. While relying on domestic institutional investors in local economies can help smooth cycles of liquidity shortages, over-reliance on those investors (particularly pension funds) can undermine the solvency of domestic banks and social security arrangements.
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Chorafas, Daniel N., and Prof. Dr Dimitris N. Chorfas. Managing Credit Risk Vol. 1 - Analysing, Rating and Pricing the Probability of Default. Euromoney Institutional Investor PLC, 2000.

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Book chapters on the topic "PD (Probability of default)"

1

Scandizzo, Sergio. "Probability of Default Models." In The Validation of Risk Models, 59–77. London: Palgrave Macmillan UK, 2016. http://dx.doi.org/10.1057/9781137436962_5.

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El Karoui, N., M. Jeanblanc, Y. Jiao, and B. Zargari. "Conditional Default Probability and Density." In Inspired by Finance, 201–19. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-02069-3_9.

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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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Coletti, Giulianella, and Romano Scozzafava. "Coherent Conditional Probability and Default Reasoning." In Probabilistic Logic in a Coherent Setting, 241–55. Dordrecht: Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-010-0474-9_20.

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Franke, Jürgen, Wolfgang Karl Härdle, and Christian Matthias Hafner. "Nonparametric Estimators for the Probability of Default." In Statistics of Financial Markets, 535–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16521-4_21.

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Bonini, Stefano, and Giuliana Caivano. "Probability of Default: A Modern Calibration Approach." In Mathematical and Statistical Methods for Actuarial Sciences and Finance, 41–44. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05014-0_9.

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Franke, Jürgen, Wolfgang Härdle, and Christian M. Hafner. "Nonparametric Estimators for the Probability of Default." In Universitext, 383–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-10026-4_20.

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Franke, Jürgen, Wolfgang Karl Härdle, and Christian Matthias Hafner. "Nonparametric Estimators for the Probability of Default." In Universitext, 511–18. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13751-9_21.

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Broske, Mary S., and Haim Levy. "The Stochastic Dominance Estimation of Default Probability." In Studies in the Economics of Uncertainty, 91–112. New York, NY: Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4613-8922-4_6.

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Franke, Jürgen, Wolfgang Karl Härdle, and Christian Matthias Hafner. "Non-parametric Estimators for the Probability of Default." In Universitext, 491–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54539-9_21.

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Conference papers on the topic "PD (Probability of default)"

1

Schroeder, Philipp, and Gabriel Wittum. "Calculation of default probability (PD) solving Merton Model PDEs on sparse grids." In Distributed Processing (IPDPS). IEEE, 2009. http://dx.doi.org/10.1109/ipdps.2009.5161149.

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Zhang, Ying, Lin Chen, and Zongfang Zhou. "Research on Default Probability of Emerging Technology Firms." In 2008 Fourth International Conference on Networked Computing and Advanced Information Management (NCM). IEEE, 2008. http://dx.doi.org/10.1109/ncm.2008.66.

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Coenen, Lize, Ahmed K. A. Abdullah, and Tias Guns. "Probability of default estimation, with a reject option." In 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 2020. http://dx.doi.org/10.1109/dsaa49011.2020.00058.

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Hui Zhou, Yi Wang, Wei Wang, Tao Li, and Hong Yang. "Prediction of default probability of clients' electricity charge arrears." In 2008 IEEE International Conference on Service Operations and Logistics, and Informatics. IEEE, 2008. http://dx.doi.org/10.1109/soli.2008.4682972.

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Kristof, Tamas, and Miklos Virag. "Lifetime Probability Of Default Modeling For Hungarian Corporate Debt Instruments." In 31st Conference on Modelling and Simulation. ECMS, 2017. http://dx.doi.org/10.7148/2017-0041.

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Maruddani, Di Asih I., Dedi Rosadi, Gunardi, and Abdurakhman. "Default probability of multiperiods coupon bond based on classical approach." In 2015 International Conference on Research and Education in Mathematics (ICREM7). IEEE, 2015. http://dx.doi.org/10.1109/icrem.2015.7357068.

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Yusof, N. M., and M. M. Jaffar. "The analysis of KMV-Merton model in forecasting default probability." In 2012 IEEE Symposium on Humanities, Science and Engineering Research (SHUSER). IEEE, 2012. http://dx.doi.org/10.1109/shuser.2012.6269010.

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Luo, Jian-Hua, and Han-Yun Lei. "Empirical Study of Corporation Credit Default Probability Based on Logit Model." In 2008 4th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM). IEEE, 2008. http://dx.doi.org/10.1109/wicom.2008.2276.

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Li, Yingqiu, Wei He, and Xiaobing Yan. "Default Probability of Listed Companies Based on the Generalized Error Distribution." In 2010 International Conference on Multimedia Technology (ICMT). IEEE, 2010. http://dx.doi.org/10.1109/icmult.2010.5631369.

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Hocman, Frantisek. "PROBABILITY OF UNPROCLAIMED DEFAULT AND ITS INGERENTION IN SOVEREIGN COUNTRY RISK." In SGEM 2014 Scientific SubConference on POLITICAL SCIENCES, LAW, FINANCE, ECONOMICS AND TOURISM. Stef92 Technology, 2014. http://dx.doi.org/10.5593/sgemsocial2014/b22/s6.075.

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Reports on the topic "PD (Probability of default)"

1

Boruchowicz, Cynthia, Florencia López Bóo, Benjamin Roseth, and Luis Tejerina. Default Options: A Powerful Behavioral Tool to Increase COVID-19 Contact Tracing App Acceptance in Latin America? Inter-American Development Bank, December 2020. http://dx.doi.org/10.18235/0002983.

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Being able to follow the chain of contagion of COVID-19 is important to help save lives and control the epidemic without sustained costly lockdowns. This is especially relevant in Latin America, where economic contractions have already been the largest in the regions history. Given the high rates of transmission of COVID-19, relying only in manual contact tracing might be infeasible. Acceptability and uptake of contact tracing apps with exposure notifications is key for the implementation the “test, trace and treat” triad. In the first study of its kind in Latin America, we find that for a nationally representative sample of 10 countries, an opt-out regime with automatic installation significantly increases the probability of acceptance of such apps in almost 22 p.p. compared to an opt-in regime with voluntary installation. This triples the size and is of opposite sign of the effect found in Europe and the United States. We see that an opt-out regime is more effective in increasing acceptability in South America compared to Central America and Mexico; for those who claim not to trust the national government; and for those who do not use their smartphones for financial transactions. The severity of the pandemic at the place of residence does not seem to affect the effectiveness of the opt-out regime versus an opt-in one, but feeling personally at risk does increase the willingness to accept contact tracing apps with exposure notifications in general. These results can shed light on the use of default options in public health in the context of a pandemic in Latin America.
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Gomez-Gonzalez, Jose E., Oscar Valencia, and Gustavo Sánchez. Sudden Stops, Sovereign Risk, and Fiscal Rules. Inter-American Development Bank, March 2021. http://dx.doi.org/10.18235/0003146.

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This paper studies the effect of implementing fiscal rules on sovereign default risk and on the probability of large capital ow reversals for a large sample of countries including both developed and emerging market economies. Results indicate that fiscal rules are beneficial for macroeconomic stability, as they significantly reduce both sovereign risk perception and the probability of a sudden stop in countries that implement them. These results, which are robust to various empirical specifications, have important policy implications specially for countries that have relaxed their fiscal rules in response to the Covid-19 pandemic.
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Clausen, Jay, Michael Musty, Anna Wagner, Susan Frankenstein, and Jason Dorvee. Modeling of a multi-month thermal IR study. Engineer Research and Development Center (U.S.), July 2021. http://dx.doi.org/10.21079/11681/41060.

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Inconsistent and unacceptable probability of detection (PD) and false alarm rates (FAR) due to varying environmental conditions hamper buried object detection. A 4-month study evaluated the environmental parameters impacting standoff thermal infra-red(IR) detection of buried objects. Field observations were integrated into a model depicting the temporal and spatial thermal changes through a 1-week period utilizing a 15-minute time-step interval. The model illustrates the surface thermal observations obtained with a thermal IR camera contemporaneously with a 3-d presentation of subsurface soil temperatures obtained with 156 buried thermocouples. Precipitation events and subsequent soil moisture responses synchronized to the temperature data are also included in the model simulation. The simulation shows the temperature response of buried objects due to changes in incoming solar radiation, air/surface soil temperature changes, latent heat exchange between the objects and surrounding soil, and impacts due to precipitation/changes in soil moisture. Differences are noted between the thermal response of plastic and metal objects as well as depth of burial below the ground surface. Nearly identical environmental conditions on different days did not always elicit the same spatial thermal response.
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