Journal articles: '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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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.

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Hunt, Clive, and Ross Taplin. "Aggregation of Incidence and Intensity Risk Variables to Achieve Reconciliation." Risks 7, no. 4 (October 25, 2019): 107. http://dx.doi.org/10.3390/risks7040107.

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The aggregation of individual risks into total risk using a weighting variable multiplied by two ratio variables representing incidence and intensity is an important task for risk professionals. For example, expected loss (EL) of a loan is the product of exposure at default (EAD), probability of default (PD), and loss given default (LGD) of the loan. Simple weighted (by EAD) means of PD and LGD are intuitive summaries however they do not satisfy a reconciliation property whereby their product with the total EAD equals the sum of the individual expected losses. This makes their interpretation problematic, especially when trying to ascertain whether changes in EAD, PD, or LGD are responsible for a change in EL. We propose means for PD and LGD that have the property of reconciling at the aggregate level. Properties of the new means are explored, including how changes in EL can be attributed to changes in EAD, PD, and LGD. Other applications such as insurance where the incidence ratio is utilization rate (UR) and the intensity ratio is an average benefit (AB) are discussed and the generalization to products of more than two ratio variables provided.

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Ardiansyah, Misnen, Aris Munandar, Ahmad Syahrul Fauzi, and Ulufun Naimah. "Default Risk on Islamic Banking in Indonesia." Global Review of Islamic Economics and Business 2, no. 2 (September 7, 2015): 110. http://dx.doi.org/10.14421/grieb.2014.022-03.

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Stability of financial institutions is a crucial issue amid the economic crisis that hit the US and Europe. Islamic banking in Indonesia as financial institutions are also required to have good stability in order to maintain the stability of the national economy. The aim of this research is to determine the stability of Islamic banking in Indonesia, and understand the factors that affect the stability. Stability of Islamic banking will be measured using Merton model to estimate the Probability Default (PD). Panel data regression was used to estimate the factors that affect the stability of Islamic Banking. The object of this research is 10 Islamic banking in Indonesia that meet the specified criteria. From the analysis of the Merton model, the research found that the stability of Islamic banking in Indonesia is not good enough. This can be seen from the value of the probability default on Islamic banking which still above 0.5. However, based on the trend, the probability default of Islamic banking has decreased from year to year. Some of the variables that influence the stability of Islamic banking is asset and BI rate (SBI).

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Cheng, Dan, and Pasquale Cirillo. "An Urn-Based Nonparametric Modeling of the Dependence between PD and LGD with an Application to Mortgages." Risks 7, no. 3 (July 7, 2019): 76. http://dx.doi.org/10.3390/risks7030076.

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We propose an alternative approach to the modeling of the positive dependence between the probability of default and the loss given default in a portfolio of exposures, using a bivariate urn process. The model combines the power of Bayesian nonparametrics and statistical learning, allowing for the elicitation and the exploitation of experts’ judgements, and for the constant update of this information over time, every time new data are available. A real-world application on mortgages is described using the Single Family Loan-Level Dataset by Freddie Mac.

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Delgado-Vaquero, David, José Morales-Díaz, and Constancio Zamora-Ramírez. "IFRS 9 Expected Loss: A Model Proposal for Estimating the Probability of Default for non-rated companies." Revista de Contabilidad 23, no. 2 (July 1, 2020): 180–96. http://dx.doi.org/10.6018/rcsar.370951.

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Bajo el modelo de provisiones por riesgo de crédito de la NIIF 9, las empresas deben estimar una Probabilidad de Default o quiebra (PD) para todos los activos financieros (y otros elementos) no valorados a valor razonable con cambios en la cuenta de resultados. Existen varias metodologías para estimar dicha PD utilizando información histórica o de mercado. No obstante, en algunos casos las empresas no disponen de información histórica o de mercado acerca de una contraparte. Para estos casos proponemos un modelo denominado Financial Ratios Scoring (FRS), a través del cual la entidad puede obtener un rating interno de la contraparte como primer paso para estimar la PD. El modelo se diferencia de otros modelos recientes en varios aspectos como, por ejemplo, el tamaño de la base de datos o el hecho de que se enfoca en empresas sin rating. Se basa en dar una puntuación a la contraparte en función de sus ratios financieros clave. La puntuación sitúa a la empresa en un percentil dentro de una distribución del sector previamente construida utilizando empresas con rating oficial u ofrecido por vendors. Hemos analizado la fiabilidad del modelo calculando el rating interno para empresas con rating oficial y hemos comparado el rating interno con el oficial, obteniendo resultados positivos. Under the IFRS 9 impairment model, entities must estimate the PD (Probability of Default) for all financial assets (and other elements) not measured at fair value through profit or loss. There are several methodologies for estimating this PD from market or historical information. However, in some cases entities do not possess market or historical information concerning a counterparty. For such cases, we propose a model called Financial Ratios Scoring (FRS), by means of which an entity can obtain a “shadow rating” for a counterparty as a first step in estimating the PD. The model differentiates from other recent models in several aspects, such as the size of the database and the fact that it is focused on non-rated companies, for example. It is based on scoring the counterparty according to its key financial ratios. The score will place the counterparty on a percentile within a previously constructed sector distribution using companies with a credit rating published by rating agencies or financial vendors. We have tested the model reliability by calculating the internal credit rating of several companies (which have an official/quoted credit rating), and by comparing the rating obtained with the official one, and obtained positive results.

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Pollák, Zoltán, and Dávid Popper. "Stress tests in Hungarian banking after 2008." Acta Oeconomica 71, no. 3 (September 21, 2021): 451–63. http://dx.doi.org/10.1556/032.2021.00022.

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Abstract The 2008 crisis highlighted the importance of using stress tests in banking practice. The role of these stress tests is to identify and precisely estimate the effect of possible future changes in market conditions on capital adequacy and profitability. This paper seeks to show a possible methodology to calculate the stressed point-in-time probability of default (PD) parameter. The presented approach contains a linear autoregressive distributed lag model to determine the connection between the logit of default rates and the relevant macroeconomic factors, and uses migration matrices to calculate PDs from the forecasted default rates. The authors illustrate the applications of this methodology using the Hungarian real credit portfolio data.

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Volarević, Hrvoje, and Mario Varović. "Internal model for IFRS 9 - Expected credit losses calculation." Ekonomski pregled 69, no. 3 (June 21, 2018): 269–97. http://dx.doi.org/10.32910/ep.69.3.4.

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This article explores and analyzes the implementation problem of International Financial Reporting Standard 9 (IFRS 9) which is in use from 1 January 2018. IFRS 9 is most relevant for financial institutions, but also for all business subjects with a significant share of financial assets in their Balance sheet. The main objective of this article is the implementation of new impairment model for financial instruments, which is measurable through Expected Credit Losses (ECL). The use of this model is in correlation with a credit risk of the company for which it is necessary to determine basic variables of the model: Exposure at Default (EAD), Loss Given Default (LGD) and Probability of Default (PD). Basel legislation could be used for LGD calculation while PD calculation is based on specific methodology with two different solutions. In the first option, PD is taken as an external data from reliable rating agencies. When there is no external rating, an internal model for PD calculation has to be created. In order to develop an internal model, authors of this article propose application of multi-criteria decision-making model based on Analytic Hierarchy Process (AHP) method. Input data in the model are based on information from financial statements while MS Excel is used for calculation of such multi-criteria problem. Results of internal model are mathematically related with PD values for each analyzed company. Simple implementation of this internal model is an advantage compared to other much more complicated models.

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Kreienkamp, Tim, and Andrey Kateshov. "Credit Risk Modeling: Combining Classification And Regression Algorithms to Predict Expected Loss." Journal of Corporate Finance Research / Корпоративные Финансы | ISSN: 2073-0438 8, no. 4 (December 9, 2014): 4–10. http://dx.doi.org/10.17323/j.jcfr.2073-0438.8.4.2014.4-10.

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Credit risk assessment is of paramount importance in the financial industry. Machine learning techniques have been used successfully over the last two decades to predict the probability of loan default (PD). This way, credit decisions can be automated and risk can be reduced significantly. In the more recent parts, intensified regulatory requirements led to the need to include another parameter – loss given default (LGD), the share of the loan which cannot be recovered in case of loan default – in risk models. We aim to build a unified credit risk model by estimating both parameters jointly to estimate expected loss. A large, highdimensional, real world dataset is used to benchmark several combinations of classification, regression and feature selection algorithms. The results indicate that non-linear techniques work especially well to model expected loss.

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Bonini, Stefano, and Giuliana Caivano. "Artificial Intelligence: the Application of Machine Learning and Predictive Analytics in Credit Risk." Risk Management Magazine 16, no. 1 (April 30, 2021): 19–29. http://dx.doi.org/10.47473/2020rmm0081.

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In the last years Machine Learning (and the Artificial Intelligence), is experiencing a new rush thanks to the growth of volume and kind of data, the presence of tools / software with higher computational power and cheaper data storage size (e.g. cloud). In Credit Risk Management, the PD (Probability of Default) estimation has attracted lots of research interests in the past literature and recent studies have shown that advanced Artificial Intelligence (AI) methods achieved better performance than traditional statistical methods tied to simplified Machine Learning techniques. The study empirically investigates the results of applying different advanced machine learning techniques in estimation and calibration of Probability of Default. The study has been done on big data sample with more than 800,000 Retail customers of a panel European Banks under ECB Supervision, with 10 years of historical information (2006 - 2016) and 300 variables to be analyzed for each customer. The study shows that neural network produces a higher population riskiness ranking accuracy, with 71% of Accuracy Ratio. However, the authors’ idea is that classification tree is more interpretable from an economic and credit point of view. In terms of model calibration, cluster analysis produces rating classes more stable and with a predicted risk probability aligned with the observed default rate.

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Oygur, Tunc, and Gazanfer Unal. "Traces of the Multifractal Nature of the Financial Crises in Turkey: Co-Movement of the Hölder Exponents and Large-Scale Forecast." Fluctuation and Noise Letters 19, no. 03 (May 11, 2019): 2050029. http://dx.doi.org/10.1142/s0219477520500297.

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This paper investigates the multifractal behavior of the probability of default (PD) of real sector firms and Turkey sovereign credit default swap (CDS). Moreover, we emphasize the co-movements of Hölder exponents during the financial crisis periods. For this reason, first, it is necessary to figure out the default probabilities of real sector firms. The default probability is evaluated weekly by the methodology of Moody’s Analytics, which is a commonly used approach, in which the market value of a firm is a call option written on its total assets. Multifractal detrended fluctuation analysis (MF-DFA), multifractal detrended cross-correlation analysis (MF-DCCA) and multifractal detrended moving average cross-correlation analysis (MF-X-DMA) techniques are applied to identify the multifractal behavior of the large-scale fluctuations of PDs and CDSs. In this way, we can evaluate the local Hurst exponents. Besides, the oscillation method is employed to estimate the pointwise and local Hölder exponents. In the period between January 2001 and March 2018, the structure of dynamic co-movements of Hölder exponents is determined by applying wavelet coherency methodology and the relations in crisis period are revealed. The selected period covers the crises with structural differences: Turkey banking crisis, the US sub-prime mortgage crisis and the European sovereign debt crisis that occurred in 2001, 2008 and 2009, respectively. Besides, during the periods of financial crises, among the local Hölder exponents, severely correlated large scales show multifractal features, and hence vector fractionally autoregressive integrated moving average (VFARIMA) forecasting provides better results than scalar models.

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Perrotta, Adamaria, and Georgios Bliatsios. "Why segmentation matters: a Machine Learning approach for predicting loan defaults in the Peer-to-Peer (P2P) Financial Ecosystem." Risk Management Magazine 16, no. 2 (August 18, 2021): 35–49. http://dx.doi.org/10.47473/2020rmm0089.

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Peer-to-Peer (P2P) lending is an online lending process allowing individuals to obtain or concede loans without the interference of traditional financial intermediaries. It has grown quickly the last years, with some platforms reaching billions of dollars of loans in principal in a short amount of time. Since each loan is associated with the probability of loss due to a borrower's failure, this paper addresses the borrower's default prediction problem in the P2P financial ecosystem. The main assumption, which makes this study different from the available literature, is that borrowers sharing the same homeownership status display similar risk profile, thus a model per segment should be developed. We estimate the Probability of Default (PD) of a borrower by using Logistic Regression (LR) coupled with Weight of Evidence encoding. The features set is identified via the Sequential Feature Selection (SFS). We compare the forward against the backward SFS, in terms of the Area Under the Curve (AUC), and we choose the one that maximizes this statistic. Finally, we compare the results of the chosen LR approach against two other popular Machine Learning (ML) techniques: the k Nearest Neighbors (k-NN) and the Random Forest (RF).

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Featherstone, Allen M., Christine A. Wilson, and Lance M. Zollinger. "Factors affecting risk-rating migration." Agricultural Finance Review 77, no. 1 (May 2, 2017): 181–95. http://dx.doi.org/10.1108/afr-05-2016-0044.

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Purpose The purpose of this paper is to examine empirical customer account data from 2006 through 2012 to review the probability of default (PD) rating methodology implemented by a FCS association for production agricultural accounts. This analysis provides insight into the migration of accounts across the association’s currently established PD rating categories with negative migration being a precursor to potential loan default. Design/methodology/approach The data set contained 17,943 observations from the years 2006 to 2012 and consisted of various fields of data including balance sheet date, earnings statement date, and PD rating as of the statement date. The methods include analysis on the dynamics of the PD ratings and component ratios. OLS regression was used to analyze the data to see how the current period PD rating and component ratios affected the PD rating one year, three years, and five years out. OLS regression examined the statistical significance of the PD ratings and ratio components for this analysis. The dependent variable, Future PD Rating, represents the assigned PD rating for the observed farm either one, three, or five years into the future. It is expected that the initial PD rating in any given year would have a positive relationship, and be statistically significant in estimating future PD ratings. The independent variables are the current PD rating and the various component ratios of the inverse current ratio (CR), the debt to asset ratio (D/A), the gross profit to total liabilities ratio, the inverse debt coverage ratio, working capital to gross profit, and funded debt to EBITDA. Findings Results indicate that financial ratio information gathered today can do a good job forecasting PD ratings up to three years in the future. CR information does not forecast five years into the future very well. Thus, there is an important need to update financial information on a regular basis. The results indicate that the D/A information is very important in predicting risk ratings. As the production agriculture sector has experienced difficult financial conditions during 2014 and 2015, agricultural finance institutions need to obtain up-to-date financial information from their clientele to effectively assess the risk of and manage their financial portfolio. Originality/value Several previous works have examined and established models to assess risk in agricultural lending. This research adds to this body of work by examining the migration of an account’s risk-rating class over time using actual lender account data.

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Johnson, Andrew M., Michael D. Boehlje, and Michael A. Gunderson. "Agricultural credit risk and the macroeconomy." Agricultural Finance Review 77, no. 1 (May 2, 2017): 164–80. http://dx.doi.org/10.1108/afr-06-2016-0057.

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Purpose The purpose of this paper is to explore the linkage between agricultural sector and macroeconomic factors with farm financial health. It considers whether agricultural lenders can more accurately anticipate changes in the credit quality of their portfolios by considering broad economic indicators outside the agriculture sector. Design/methodology/approach This paper examines firm, sector, and macroeconomic drivers of probability of default (PD) migrations from a sample of 153 grain farms of actual lender data from Farm Credit Mid-America’s portfolio. A series of ordered logit models are developed. Findings Farm-level and sector-level variables have the most significant impact on PD migrations. Equity to asset ratios, working capital to gross farm income ratios, and gross corn income per acre are found to be the most significant drivers of PD migrations. Macroeconomic variables are shown to unreliably forecast PD migrations, suggesting that agricultural lenders should emphasize firm and sector variables over macroeconomic factors in credit risk models. Originality/value This paper builds the literature on agricultural credit risk by testing a broader set of sector and macroeconomic variables than previous articles. Also, prior articles measured the direction but not magnitude of PD migrations; the ordered model in the analysis measures both.

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Васильева, Альфия. "Approaches to developing an internal model for assessing the long-term probability of default for corporate borrowers in the "retail" segment»." Journal of Corporate Finance Research / Корпоративные Финансы | ISSN: 2073-0438 14, no. 1 (June 20, 2020): 91–114. http://dx.doi.org/10.17323/j.jcfr.2073-0438.14.1.2020.91-114.

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Аннотация Данная работа является следующим этапом исследовательской работы авторов в рамках разработки подходов к моделированию кредитного риска, с учетом требований МСФО 9, для российских банков. Данный стандарт внедрен был внедрен во всем мире с 1 января 2018 года (в том числе и на российском банковском рынке) в соответствии правилами которого требуется уточнение действующих моделей оценки кредитного риска. В основу МСФО (IFRS) 9 положен подход «ожидаемых кредитных потерь» (ECL/ОКП). Новая бизнес-модель кардинально меняет подход к формированию резервов, в том числе благодаря учету влияния макроэкономических показателей на их величину. Целью настоящей статьи является построение модели оценки вероятности дефолта корпоративных заемщиков сегмента «Торговля» за весь срок действия активов, в соответствии с требованиями МСФО 9. Разработка модели в рамках настоящей работы проведена на данных реального Банка[1], поэтому результаты и методы, применяемые в рамках настоящей работы могут быть использованы как коммерческим банкам, так и регулирующими органами в рамках реализации проектов по внедрению МСФО (IFRS) 9. Практическая ценность данной работы также определяет ее научную новизну, так как данная работа представляет собой одно из первых исследований в области долгосрочной вероятности дефолта на реальных данных российских корпоративных клиентов коммерческих банков. В рамках настоящей работы вероятность дефолта в течение срока жизни финансового инструмента (life-time PD/ lt PD) производится на основе параметрической модели, при этом в рамках настоящей задачи были исследованы два класса распределений (двухпараметрическое распределение Вейбулла и модифицированное распределение Вейбулла). Результаты разработки модели представлены в настоящем отчете. [1] В силу конфиденциальности информации авторы не раскрывают название банка, данные по портфелю которого были использованы, а также наименования его клиентов

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De Jongh, Riaan, Tanja Verster, Elzabe Reynolds, Morne Joubert, and Helgard Raubenheimer. "A Critical Review Of The Basel Margin Of Conservatism Requirement In A Retail Credit Context." International Business & Economics Research Journal (IBER) 16, no. 4 (October 2, 2017): 257–74. http://dx.doi.org/10.19030/iber.v16i4.10041.

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The Basel II accord (2006) includes guidelines to financial institutions for the estimation of regulatory capital (RC) for retail credit risk. Under the advanced Internal Ratings Based (IRB) approach, the formula suggested for calculating RC is based on the Asymptotic Risk Factor (ASRF) model, which assumes that a borrower will default if the value of its assets were to fall below the value of its debts. The primary inputs needed in this formula are estimates of probability of default (PD), loss given default (LGD) and exposure at default (EAD). Banks for whom usage of the advanced IRB approach have been approved usually obtain these estimates from complex models developed in-house. Basel II recognises that estimates of PDs, LGDs, and EADs are likely to involve unpredictable errors, and then states that, in order to avoid over-optimism, a bank must add to its estimates a margin of conservatism (MoC) that is related to the likely range of errors. Basel II also requires several other measures of conservatism that have to be incorporated. These conservatism requirements lead to confusion among banks and regulators as to what exactly is required as far as a margin of conservatism is concerned. In this paper, we discuss the ASRF model and its shortcomings, as well as Basel II conservatism requirements. We study the MoC concept and review possible approaches for its implementation. Our overall objective is to highlight certain issues regarding shortcomings inherent to a pervasively used model to bank practitioners and regulators and to potentially offer a less confusing interpretation of the MoC concept.

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Gruszczyński, Marek. "On Unbalanced Sampling in Bankruptcy Prediction." International Journal of Financial Studies 7, no. 2 (June 5, 2019): 28. http://dx.doi.org/10.3390/ijfs7020028.

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The paper discusses methodological topics of bankruptcy prediction modelling—unbalanced sampling, sample bias, and unbiased predictions of bankruptcy. Bankruptcy models are typically estimated with the use of non-random samples, which creates sample choice biases. We consider two types of unbalanced samples: (a) when bankrupt and non-bankrupt companies enter the sample in unequal numbers; and (b) when sample composition allows for different ratios of bankrupt and non-bankrupt companies than those in the population. An imbalance of type (b), being more general, is examined in several sections of the paper. We offer an extended view of the relationship between the biased and unbiased estimated probabilities of bankruptcy—probability of default (PD). A common error in applications is neglecting the possibility of calibrating the PD obtained from a bankruptcy model to the unbiased PD that is population adjusted. We show that Skogsviks’ formula of 2013 coincides with prior correction known for the logit model. This, together with solutions for other binomial models, serves as practical advice for obtaining the calibration of unbiased PDs from popular bankruptcy models. In the final section, we explore sample bias effects on classification.

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Ledwon, Andreas, and Clemens Jäger. "Cox Proportional Hazards Regression Analysis to assess Default Risk of German-listed Companies with Industry Grouping." ACRN Journal of Finance and Risk Perspectives 9, no. 1 (2020): 57–77. http://dx.doi.org/10.35944/jofrp.2020.9.1.005.

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This study evaluates three corporate failure prediction models using latest available data on corporate insolvencies for non-financial constitutes represented in CDAX. We estimate semiparametric Cox proportional hazards models considering Andersen-Gill counting process (AG-CP) to explore the importance of accounting and financial ratios as well as industry effects that are useful in detecting potential insolvencies. The contribution of this paper is twofold. First, the literature on corporate default prediction is manifold and predominantly focused on U.S. data. Thus, academic contribution based on German-listed companies is limited. To our best knowledge, we are the first to conduct thorough comparative out-of-sample Cox regression models considering AG-CP based on a unique dataset for non-financial constitutes subject to the German insolvency statute (“InsO”). Relying on a parsimonious accounting-based approach inspired by Altman (1968) and Ohlson (1980) is merely adequate. Shumway (2001) and Campbell et al. (2008) variable selection delivers the best discriminatory power and calibration results. In particular, a combination of pure accounting ratios augmented with market-driven information in Model (2) indicates superior accuracy rates in top deciles. However, in-sample empirical results underpin the importance towards market-based indicators, as all accounting ratios enter statistically insignificant. Secondly, we test to what extend industry variables improve discriminatory power and forecasting accuracy of fitted models. Contrary to the findings of Chava & Jarrow (2004), our research implies that industry grouping adds marginal predictive power and no overall improvement in accuracy rates when market variables are already included in the probability of default (PD) model.

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Rosaria Della Peruta, Maria, Francesco Campanella, and Manlio Del Giudice. "Knowledge sharing and exchange of information within bank and firm networks: the role of the intangibles on the access to credit." Journal of Knowledge Management 18, no. 5 (September 2, 2014): 1036–51. http://dx.doi.org/10.1108/jkm-06-2014-0255.

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Purpose – The purpose of this paper is to theoretically develop the idea that the intangible value of the collaboration between firms and the banking system can influence the probability of default (PD) on the part of firms and, therefore, their rating. The authors also propose that collaboration between banks and firms has a positive effect not only on the access to credit but also on the innovation activities and on the intervention of foreign capital in the ownership of Italian businesses. Design/methodology/approach – As pointed out by the literature on smaller businesses finance, investments widely rely on credit availability. Tests using data on a sample investigation involving 5,587 firms, operating in 17 manufacturing sectors in Italy, support the majority of the proposed ideas. Findings – The empirical investigation shows that only some aspects of the collaboration between enterprises and banks influence the PD, the investments in R&D and the internationalisation of ownership of the enterprises. In particular, the three stated variables are positively influenced both by the intensity of the credit relationship and by the level of information exchange with the credit system. Research limitations/implications – Further development of this research, as more empirical data become available, should allow explaining why the level of information exchange with the credit system has the greatest influence on the dependent variables analyzed. Originality/value – This paper aims to extend the current understanding on how the local banking system is developed and is able to increase access to credit after gathering all the information about firms asking for funds.

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Osherson, Daniel N., Joshua Stern, Ormond Wilkie, Michael Stob, and Edward E. Smith. "Default Probability." Cognitive Science 15, no. 2 (April 1991): 251–69. http://dx.doi.org/10.1207/s15516709cog1502_3.

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Li, Weiping. "Probability of Default and Default Correlations." Journal of Risk and Financial Management 9, no. 3 (July 5, 2016): 7. http://dx.doi.org/10.3390/jrfm9030007.

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Dzidzevičiūtė, Laima. "ESTIMATION OF DEFAULT PROBABILITY FOR LOW DEFAULT PORTFOLIOS." Ekonomika 91, no. 1 (January 1, 2012): 132–56. http://dx.doi.org/10.15388/ekon.2012.0.902.

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This article presents several approaches to estimating the probabilities of default for low default portfolios, their advantages and disadvantages, and provides exemplary calculations using data of one external credit register of Lithuania. The results show that three approaches seem to be most appropriate: those of K. Pluto and D. Tasche (2005) without correlation, and those of N. M. Kiefer (2006) and A. Forrest (2005) without correlation. The first one could be easily implemented by banks; however, if the ordinal ranking of obligors is incorrect, then the monotony of probabilities of default is not ensured. The same problem exists with the second approach. The A. Forrest (2005) approach without correlation ensures the monotony of default probabilities and allows estimating conservative PDs; however, it requires programming skills, otherwise iterative recalculation will be very time-consuming.

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Misankova, Maria, Erika Spuchľakova, and Katarina Frajtova –. Michalikova. "Determination of Default Probability by Loss Given Default." Procedia Economics and Finance 26 (2015): 411–17. http://dx.doi.org/10.1016/s2212-5671(15)00815-1.

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Luo, Lawrence. "Bootstrapping default probability curves." Journal of Credit Risk 1, no. 4 (2005): 169–79. http://dx.doi.org/10.21314/jcr.2005.028.

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Blümke, Oliver. "Estimating the probability of default for no‐default and low‐default portfolios." Journal of the Royal Statistical Society: Series C (Applied Statistics) 69, no. 1 (October 8, 2019): 89–107. http://dx.doi.org/10.1111/rssc.12381.

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Hund, John. "Default Probability Dynamics in Structural Models." Journal of Fixed Income 13, no. 2 (September 30, 2003): 67–79. http://dx.doi.org/10.3905/jfi.2003.319354.

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Capozza, Dennis R., Dick Kazarian, and Thomas A. Thomson. "The Conditional Probability of Mortgage Default." Real Estate Economics 26, no. 3 (September 1998): 259–89. http://dx.doi.org/10.1111/1540-6229.00750.

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Matsumoto, Koichi. "Implied Default Probability and Credit Derivatives." Asia-Pacific Financial Markets 10, no. 2-3 (September 2003): 129–49. http://dx.doi.org/10.1007/s10690-005-6007-z.

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Kreinin, Alexander, and Ahmed Nagi. "Calibration of the default probability model." European Journal of Operational Research 185, no. 3 (March 2008): 1462–76. http://dx.doi.org/10.1016/j.ejor.2004.11.029.

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Putcha, Deepti, Robert S. Ross, Alice Cronin-Golomb, Amy C. Janes, and Chantal E. Stern. "Salience and Default Mode Network Coupling Predicts Cognition in Aging and Parkinson’s Disease." Journal of the International Neuropsychological Society 22, no. 2 (February 2016): 205–15. http://dx.doi.org/10.1017/s1355617715000892.

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AbstractObjectives:Cognitive impairment is common in Parkinson’s disease (PD). Three neurocognitive networks support efficient cognition: the salience network, the default mode network, and the central executive network. The salience network is thought to switch between activating and deactivating the default mode and central executive networks. Anti-correlated interactions between the salience and default mode networks in particular are necessary for efficient cognition. Our previous work demonstrated altered functional coupling between the neurocognitive networks in non-demented individuals with PD compared to age-matched control participants. Here, we aim to identify associations between cognition and functional coupling between these neurocognitive networks in the same group of participants.Methods:We investigated the extent to which intrinsic functional coupling among these neurocognitive networks is related to cognitive performance across three neuropsychological domains: executive functioning, psychomotor speed, and verbal memory. Twenty-four non-demented individuals with mild to moderate PD and 20 control participants were scanned at rest and evaluated on three neuropsychological domains.Results:PD participants were impaired on tests from all three domains compared to control participants. Our imaging results demonstrated that successful cognition across healthy aging and Parkinson’s disease participants was related to anti-correlated coupling between the salience and default mode networks. Individuals with poorer performance scores across groups demonstrated more positive salience network/default-mode network coupling.Conclusions:Successful cognition relies on healthy coupling between the salience and default mode networks, which may become dysfunctional in PD. These results can help inform non-pharmacological interventions (repetitive transcranial magnetic stimulation) targeting these specific networks before they become vulnerable in early stages of Parkinson’s disease. (JINS, 2016,22, 205–215)

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Yilmaz, Bilgi, A. Alper Hekimoglu, and A. Sevtap Selcuk-Kestel. "Default and prepayment options pricing and default probability valuation under VG model." Journal of Computational and Applied Mathematics 399 (January 2022): 113724. http://dx.doi.org/10.1016/j.cam.2021.113724.

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Yang, Bill Huajian. "Estimating long-run probability of default, asset correlation and portfolio-level probability of default using Vasicek models." Journal of Risk Model Validation 7, no. 4 (December 2013): 3–19. http://dx.doi.org/10.21314/jrmv.2013.112.

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Luo, Xiangyun, and Miao Luo. "Analysis on financing structure and default probability of listed companies." E3S Web of Conferences 292 (2021): 02032. http://dx.doi.org/10.1051/e3sconf/202129202032.

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This paper studies the relationship between the financing structure and the probability of default of A-share listed companies from 2001 to 2020. The purpose is to prevent the occurrence of default and ensure the healthy development of various industries. It is found that the higher the proportion of external financing is, the higher the probability of default is. The impact of debt financing on default risk is higher than equity financing. In addition, this paper tests the mediating effect of cash flow risk, and the effect of financing structure on debt default probability is heterogeneous among regions and enterprises. These findings show that enterprises must control their financing structure, optimize the allocation of resources, prevent cash flow risk, reduce the probability of debt default, so as to make various industries flourish and optimize the industrial structure.

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Jung, Hosung, and Hyun Hak Kim. "Default Probability by Employment Status in South Korea." Asian Economic Papers 19, no. 3 (October 2020): 62–84. http://dx.doi.org/10.1162/asep_a_00786.

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This paper analyzes the factors behind the loan defaults of borrowers by their employment status (whether they are self-employed) using the Korea Consumer Credit Panel data held by the Bank of Korea, and estimates the probability of default and the fragility of the financial sector. This is done using the concept of exposure at default for individuals. Using individual data on loans and delinquencies, we divide the spreads of personal loans into spreads determined by loan characteristics and spreads based on credit ratings to examine how these factors determine the probability of default among self-employed and non-self-employed borrowers. We find that the marginal effect of the spread based by the characteristic of loans and the borrowers’ credit ratings on the probability of default is greater to the self-employed borrowers, as compared to the non-self-employed borrowers. Especially, the effect of the spread by credit rating is stronger than the other. When we measure the vulnerability of financial institutions in terms of households’ probability of default, the institutions’ exposure to default of the self-employed affects to institutions’ vulnerability more than those of the non-self-employed. Therefore, more attention should be paid to the connectedness of financial institutions to household debt, and, in particular, the financial status of the self-employed.

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민춘식 and 김성민. "Macroeconomic Vables Impact on Expected Default Probability." Korean Journal of Financial Engineering 8, no. 3 (September 2009): 171–97. http://dx.doi.org/10.35527/kfedoi.2009.8.3.008.

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Divino, Jose Angelo, and Líneke Clementino Sleegers Rocha. "Probability of default in collateralized credit operations." North American Journal of Economics and Finance 25 (August 2013): 276–92. http://dx.doi.org/10.1016/j.najef.2012.06.015.

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Lee, Nicholas Rueilin, Jung-Fang Liu, and Wei-Yu Lin. "Default probability anomalies in the momentum strategies." Applied Economics Letters 21, no. 17 (June 2, 2014): 1206–9. http://dx.doi.org/10.1080/13504851.2014.920463.

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Schultz, Emma L., David T. Tan, and Kathleen D. Walsh. "Corporate governance and the probability of default." Accounting & Finance 57 (June 4, 2015): 235–53. http://dx.doi.org/10.1111/acfi.12147.

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Abid, Amira, Fathi Abid, and Bilel Kaffel. "CDS-based implied probability of default estimation." Journal of Risk Finance 21, no. 4 (July 21, 2020): 399–422. http://dx.doi.org/10.1108/jrf-05-2019-0079.

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Purpose This study aims to shed more light on the relationship between probability of default, investment horizons and rating classes to make decision-making processes more efficient. Design/methodology/approach Based on credit default swaps (CDS) spreads, a methodology is implemented to determine the implied default probability and the implied rating, and then to estimate the term structure of the market-implied default probability and the transition matrix of implied rating. The term structure estimation in discrete time is conducted with the Nelson and Siegel model and in continuous time with the Vasicek model. The assessment of the transition matrix is performed using the homogeneous Markov model. Findings The results show that the CDS-based implied ratings are lower than those based on Thomson Reuters approach, which can partially be explained by the fact that the real-world probabilities are smaller than those founded on a risk-neutral framework. Moreover, investment and sub-investment grade companies exhibit different risk profiles with respect of the investment horizons. Originality/value The originality of this study consists in determining the implied rating based on CDS spreads and to detect the difference between implied market rating and the Thomson Reuters StarMine rating. The results can be used to analyze credit risk assessments and examine issues related to the Thomson Reuters StarMine credit risk model.

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Dalla Valle, Luciana, Maria Elena De Giuli, Claudia Tarantola, and Claudio Manelli. "Default probability estimation via pair copula constructions." European Journal of Operational Research 249, no. 1 (February 2016): 298–311. http://dx.doi.org/10.1016/j.ejor.2015.08.026.

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Karminsky, Alexander M., and Alexander Kostrov. "The probability of default in Russian banking." Eurasian Economic Review 4, no. 1 (June 2014): 81–98. http://dx.doi.org/10.1007/s40822-014-0005-2.

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

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