Correction to: Probability of default estimation in credit risk using a nonparametric approach

Credit Risk and Risk Neutral Default Probabilities: Information About Rating Migrations and Defaults

Probability of default (PD) estimation is essential to the calculation of expected credit loss under the Basel III framework and the International Financial Reporting Standard 9. Gross domestic product (GDP) growth has been adopted as a key determinant in PD estimation models. However, PD models with a GDP covariate may not perform well under aberrant (i.e., outlier) conditions such as the COVID-19 pandemic. This study explored the robustness of a PD model with a GDP determinant (the test model) in comparison with that of a PD model with a credit default swap index (CDX) determinant (the alternative model). The test model had a significantly greater ratio of increase in Akaike information criterion than the alternative model in comparisons of the fit performance of models including 2020 data with that of models excluding 2020 data (i.e., that do not cover the COVID-19 pandemic). Furthermore, the Cook’s distance of the 2020 data of the test model was significantly greater than that of the alternative model. Therefore, the test model exhibited a serious robustness issue in outlier scenarios, such as the COVID-19 pandemic, whereas the alternative model was more robust. This finding opens the prospect for the CDX to potentially serve as an alternative to GDP in PD estimation models.

We use a multiple-factor credit risk model to provide new estimates of default probabilities in a sample of Italian Small and Medium-sized Enterprises. Results show that, on average, SMEs are riskier than large businesses within the retail segment. It is possible to distinguish different segments inside the SMEs’ population based on geographical location, sector of activity and juridical status.

Latin American economy has achieved rapid economic growth since 2003 after undergoing currency crisis and economic turmoil from the early 1930s through until the early 1960s. As a result of this growth, consumer finance market expanded due to external demand in country X in Latin America. In addition, because part of the poor class has changed into the middle class, financial services have also spread to those who could not formulate loans in the past. On the other hand, there are many debtors who do not understand the contents of the loan contract, and the debt default due to excessive debt is increasing. Therefore, in order for Latin American financial institutions, manufacturers and retailers to operate stably, it is necessary to measure the credit risk of the debtor who formed the loan and to grasp the factors that affect the default. The purpose of this research is to construct a credit risk model based on 14,000 auto loan data in country X in Latin America and to estimate debtor’s default probability. The binomial logit model was adopted as a usage model in this research. Estimate default probabilities for debtors who defaulted within one year using the model and grasp default situations in Latin America. From the analysis results, it became clear that the presence or absence of marriage greatly influences the default. It is thought that the debtor who got married and had a family became easier to default because expenditure is larger than the debtor who does not have a family. In addition, when there are missing values in the income and down payment items, the debtor got the result that it is easy to default. Since information on auto loans is often written by the debtor himself, it was suggested that debtors who do not fill in information correctly have a stronger possibility of default.

The objective of paper is to assess the efficiency of financial model to capture increasing volatilities across asset class markets of the three investment banks. For which data will be collect to forecast the credit risk, and to know how well our standard tools forecast volatility, particularly during the turmoil that extend throughout the globe. Volatility prediction is a critical task in asset valuation and risk management for investors and financial intermediaries. The paper will focus on Value-at-Risk (VaR) which is a standard model that has been forecasted using both non-parametric and parametric approaches and then Backtesting procedure had been applied to achieve the both outcome. One is to detect the underlying credit risk which is associated with the market as well as portfolio risk, and other is to perceive model which provide more accurate forecasting.

Predicting loss given default in leasing: A closer look at models and variable selection

The good, the bad and the impaired: A credit risk model of the Irish mortgage market

A reinforced urn process modeling of recovery rates and recovery times

This paper applies a novel time series-based additive nonparametric quantile regression technique to stress test the credit risk of conventional and participation banks in Turkey. We particularly examine the effects of the exchange rate, unemployment rate, the policy interest rate, and the public debt-to-GDP ratio on the non-performing loan ratio (NPL) of Turkish banks at distinct quantile levels. We find that their effects are heterogeneous and significantly vary across different types of banks and quantiles. Overall, participation banks are more vulnerable to negative scenarios associated with the exchange rate, unemployment rate, and public debt ratio than conventional banks.

Peer-to-peer (P2P) lending offers an alternative way to access credit. Unlike established lending institutions with proven credit risk management practices, P2P platforms rely on numerous independent variables to evaluate loan applicants’ creditworthiness. This study aims to estimate default probabilities using a mixture-of-experts neural network in P2P lending. The approach involves coupling unsupervised clustering to capture essential data properties with a classification algorithm based on the mixture-of-experts structure. This classic design enhances model capacity without significant computational overhead. The model was tested using P2P data from Lending Club, comparing it to other methods like Logistic Regression, AdaBoost, Gradient Boosting, Decision Tree, Support Vector Machine, and Random Forest. The hybrid model demonstrated superior performance, with a Mean Squared Error reduction of at least 25%.

<abstract><p>In the realm of machine learning, where data-driven insights guide decision-making, addressing the challenges posed by class imbalance in datasets has emerged as a crucial concern. The effectiveness of classification algorithms hinges not only on their intrinsic capabilities but also on their adaptability to uneven class distributions, a common issue encountered across diverse domains. This study delves into the intricate interplay between varying class imbalance levels and the performance of ten distinct classification models, unravelling the critical impact of this imbalance on the landscape of predictive analytics. Results showed that random forest (RF) and decision tree (DT) models outperformed others, exhibiting robustness to class imbalance. Logistic regression (LR), stochastic gradient descent classifier (SGDC) and naïve Bayes (NB) models struggled with imbalanced datasets. Adaptive boosting (ADA), gradient boosting (GB), extreme gradient boosting (XGB), light gradient boosting machine (LGBM), and k-nearest neighbour (kNN) models improved with balanced data. Adaptive synthetic sampling (ADASYN) yielded more reliable predictions than the under-sampling (UNDER) technique. This study provides insights for practitioners and researchers dealing with imbalanced datasets, guiding model selection and data balancing techniques. RF and DT models demonstrate superior performance, while LR, SGDC and NB models have limitations. By leveraging the strengths of RF and DT models and addressing class imbalance, classification performance in imbalanced datasets can be enhanced. This study enriches credit risk modelling literature by revealing how class imbalance impacts default probability estimation. The research deepens our understanding of class imbalance's critical role in predictive analytics. Serving as a roadmap for practitioners and researchers dealing with imbalanced data, the findings guide model selection and data balancing strategies, enhancing classification performance despite class imbalance.</p></abstract>

Recent advances in the theory of credit risk allow the use of standard term structure machinery for default risk modeling and estimation. The empirical literature in this area often interprets the drift adjustments of the default intensity's diffusion state variables as the only default risk premium. We show that this interpretation implies a restriction on the form of possible default risk premia, which can be justified through exact and approximate notions of default The equivalence between the empirical and martingale default intensities that follows from diversifiable default risk greatly facilitates the pricing and management of credit risk. We emphasize that this is not an equivalence in distribution, and illustrate its importance using credit spread dynamics estimated in Duffee (1999). We also argue that the assumption of diversifiability is implicitly used in certain existing models of mortgage-backed securities.

This study explores banks’ internal credit risk estimates and the associated banksourced transition matrixes. We empirically test the widely used Markovian property and time homogeneity assumptions at an unprecedented scale. Our unique data set consists of internal probability of default estimates from 12 global banks that employ an advanced internal ratings-based approach, covering monthly observations on 20,000 corporates over the period 2015–18. The results indicate that internal credit risk estimates do not satisfy the two assumptions, showing evidence of both path dependency and time heterogeneity even within the period of economic expansion. Banks tend to revert their rating actions, which contradicts previous findings based on data from credit rating agencies. This has significant practical implications through bank-sourced credit transition matrixes, which are becoming increasingly important as regulators begin to use more detailed credit risk data sets (eg, analytical credit data sets by the European Central Bank) with potential applications in stress testing.