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BackgroundEnvironmental, social, and governance (ESG) disclosure can influence a firm’s credit risk by improving transparency, strengthening risk management, and signalling stability to lenders and rating agencies. In South Africa, where information asymmetry, governance weaknesses, and macroeconomic volatility persist, understanding this relationship is important for promoting financial stability and advancing sustainable investment practices.AimThis study investigates how ESG disclosure affects three dimensions of credit risk: probability of default (PD), cost of debt (COD), and credit model scores (CMS). It also evaluates whether individual ESG pillars exert distinct effects, thereby identifying which sustainability dimensions are most relevant in the South African context.SettingThe analysis covers 78 non-financial Johannesburg Stock Exchange firms from 2017 to 2023.MethodThe study employs baseline ordinary least squares and fixed-effects models, and instrumental-variable two-stage least squares for PD and COD, and ordered probit models, with and without a conditional mixed-process framework, for CMS, allowing treatment of endogeneity.ResultsHigher ESG disclosure lowers PD and improves CMS but does not affect COD. Governance drives reductions in PD, while environmental and social pillars strengthen CMS, indicating that ESG components operate through different credit risk channels.ConclusionEnvironmental, social, and governance disclosure influences two credit risk measures, highlighting its relevance for credit evaluation in South Africa.ContributionThis study provides the first South African evidence on the ESG-credit risk relationship using different proxies and endogeneity-corrected models. It advances academic debates on ESG in emerging markets and offers practical insights for regulators, lenders, and investors integrating ESG factors into credit-risk evaluation.

Credit scoring, which is typically transformed into a classification problem, is a powerful tool to manage credit risk since it forecasts the probability of default (PD) of a loan application. However, there is a growing trend of integrating survival analysis into credit scoring to provide a dynamic prediction on PD over time and a clear explanation on censoring. A novel dynamic credit scoring model (i.e., SurvXGBoost) is proposed based on survival gradient boosting decision tree (GBDT) approach. Our proposal, which combines survival analysis and GBDT approach, is expected to enhance predictability relative to statistical survival models. The proposed method is compared with several common benchmark models on a real-world consumer loan dataset. The results of out-of-sample and out-of-time validation indicate that SurvXGBoost outperform the benchmarks in terms of predictability and misclassification cost. The incorporation of macroeconomic variables can further enhance performance of survival models. The proposed SurvXGBoost meanwhile maintains some interpretability since it provides information on feature importance. First published online 14 December 2020

The paper proposes a sequential Bayesian updating approach to estimate default probabilities on rating grade level for no- and low-default portfolios. Bayesian sequential updating enables default probabilities to be obtained also for those rating grades for which no defaults have been observed. The advantage of this approach is that it preserves the rank order of rating grades in the case of no defaults. Rank preservation is not ensured when using an identical prior distribution across all rating grades. We discuss Bayesian sequential updating for the beta–binomial model and a model incorporating the asymptotic single-risk factor model of the Basel Accord. Practical aspects such as incorporating information from external sources and the margin of conservatism are addressed.

We use a variety of organization-level datasets to examine the effectiveness and efficiency of the nations for the coronavirus epidemic. COVID-19 subsidies appear to have saved a significant number of jobs and maintained economic activity during the first wave of the epidemic, according to conclusions drawn from the experiences of EU member countries. General allocation rules may yield near-optimal outcomes in favor of allocation, as firms with high ecological footprints or zombie firms have lower access to government financing than more favorable, commercially owned, and export-inclination firms. Our assumptions show that the pandemic has a considerable negative impact on firm earnings and the percentage of illiquid and non-profitable businesses. Although they are statistically significant, government wage subsidies have a modest impact on corporate losses compared to the magnitude of the economic shock. Larger enterprises, which receive a lesser proportion of the aid, have more room to increase their trade liabilities or liabilities to linked entities. In contrast, according to our estimations, SMEs stand a greater danger of insolvency.

The inferred rate of default (IRD) was first introduced as an indicator of default risk computable from information publicly reported by the Bulgarian National Bank. We have provided a more detailed justification for the suggested methodology for forecasting the IRD on the bank-group- and bank-system-level based on macroeconomic factors. Furthermore, we supply additional empirical evidence in the time-series analysis. Additionally, we demonstrate that IRD provides a new perspective for comparing credit risk across bank groups. The estimation methods and model assumptions agree with current Bulgarian regulations and the IFRS 9 accounting standard. The suggested models could be used by practitioners in monthly forecasting the point-in-time probability of default in the context of accounting reporting and in monitoring and managing credit risk.

This study examines the impact of overall Environmental, Social, and Governance (ESG) performance and its pillars on the default probability of Australian-listed firms. Using a panel dataset spanning 2014 to 2022 and applying the Generalized Method of Moments (GMM) regression, we find that firms with higher ESG scores exhibit a significantly lower likelihood of default. Disaggregating the ESG components reveals that the Environmental and Social pillars have a negative association with default risk, suggesting a risk-mitigating effect. In contrast, the Governance pillar demonstrates a positive relationship with default probability, which may reflect potential greenwashing behavior or an excessive focus on formal governance mechanisms at the expense of operational and financial performance. Furthermore, the analysis identifies trade credit financing (TCF) as a partial mediator in the ESG–default risk nexus, indicating that firms with stronger ESG profiles rely less on external short-term financing, thereby reducing their default risk. These findings provide valuable insights for corporate management, investors, regulators, and policymakers seeking to enhance financial resilience through sustainable practices.

PurposeThe main purpose of this study is to evaluate the probability of default and examine the relationship between default risk and financial performance, with dynamic panel moderation of firm size.Design/methodology/approachThis study utilizes a total of 1,500 firm-year observations from 2013 to 2018 using dynamic panel data approach of generalized method of moments to test the relationship between default risk and financial performance with the moderation effect of the firm size.FindingsThis study establishes the findings that default risk significantly impacts the financial performance. The relationship between distance-to-default (DD) and financial performance is positive, which means the relationship of the independent and dependent variable is inverse. Moreover, this study finds that the firm size is a significant positive moderator between DD and financial performance.Practical implicationsThis study provides new and useful insight into the literature on the relationship between default risk and financial performance. The results of this study provide investors and businesses related to nonfinancial firms in the Pakistan Stock Exchange (PSX) with significant default risk's impact on performance. This study finds, on average, the default probability in KSE ALL indexed companies is 6.12%.Originality/valueThe evidence of the default risk and financial performance on samples of nonfinancial firms has been minimal; mainly, it has been limited to the banking sector. Moreover, the existing studies have only catered the direct effect of only. This study fills that gap and evaluates this relationship in nonfinancial firms. This study also helps in the evaluation of Merton model's performance in the nonfinancial firms.

This paper considers a hypothetical case in which a bank wants to build a routine climate stress test exercise on residential mortgage loans. The bank has regularly updated the probability of default (PD) and loss given default (LGD) on each residential mortgage loan under the internal-rating-based (IRB) approach of Basel II/III. Additionally, the bank estimates the stressed PD and stressed LGD associated with a predetermined extreme weather event. Using simulation techniques, this paper shows that the loss of the bank’s residential mortgage portfolio can reach a median of around 36% of the portfolio value. This remarkable loss comes from the effects of default correlation and property damage. Banks should pay more attention to such impacts of extreme weather events.

In this study, we extend research on stablecoin credit risk by introducing a novel rule-of-thumb approach to determine whether a stablecoin is “dead” or “alive” based on a simple price threshold. Using a comprehensive dataset of 98 stablecoins, we classify a coin as failed if its price falls below a predefined threshold (e.g., $0.80), validated through sensitivity analysis against established benchmarks such as CoinMarketCap delistings and Feder et al. (2018) methodology. We employ a wide range of panel binary models to forecast stablecoins’ probabilities of default (PDs), incorporating stablecoin-specific regressors. Our findings indicate that panel Cauchit models with fixed effects outperform other models across different definitions of stablecoin failure, while lagged average monthly market capitalization and lagged stablecoin volatility emerge as the most significant predictors—outweighing macroeconomic and policy-related variables. Random forest models complement our analysis, confirming the robustness of these key drivers. This approach not only enhances the predictive accuracy of stablecoin PDs but also provides a practical, interpretable framework for regulators and investors to assess stablecoin stability based on credit risk dynamics.

As the IFRS 9 accounting standard requires banks to recognise impairments based on a forward-looking expected loss concept, banks must estimate the quantitative relationship between default rates and macroeconomic indicators (GDP, unemployment, etc.). In such models, the stationarity of the (usually short) default rate time series is often the most critical issue. In this article, we provide practical advice for banking experts on how (under which circumstances) they can still use short default rate time series in OLS regressions even if those fail regular stationarity tests. We argue that if margin of conservativism is requested for the underlying default rate projections, then applying (seemingly) non-stationary default rate time series in OLS models might not necessarily be problematic.