Explore the vast range of titles available.

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.

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.

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.

This study proposed a method for constructing rating tools using logistic regression and linear discriminant analysis to determine the risk profile of SME portfolios. The objective, firstly, is to evaluate the impact of the crisis due to the Covid-19 by readjusting the profile of each company by using the expert opinion and, secondly, to evaluate the efficiency of the measures taken by the Moroccan state to support the companies during the period of the pandemic. The analysis in this paper showed that the performance of the logistic regression and linear discriminant analysis models is almost equivalent based on the ROC curve. However, it was revealed that the logistic regression model minimizes the risk cost represented in this study by the expected loss. For the support measures adopted by the Moroccan government, the study showed that the failure rate (critical situation) of the firms benefiting from the support is largely lower than that of the non-beneficiaries.

Equilibrium pricing of credit default swaps (CDS) promotes efficient identification of credit risk in the market, which in turn leads to efficient allocation of resources. However, even when CDS have been priced in equilibrium, i.e., when premiums are equal to anticipated payments, the moral hazard incentives of CDS buyers increase with CDS transactions. Consequentially, it becomes an interesting research direction to study the impact of moral hazard incentives on the trading mechanism or pricing of derivatives (CDS). Most of the existing literature on the impact of moral hazard incentives in CDS pricing on derivatives trading mechanisms takes a macro perspective and focuses on the agreement risk effect. The literature exploring the analysis of the impact of moral hazard on the probability of agreement default from a micro perspective is not yet available. With this in mind, this paper focuses on the mechanisms by which “fraud”, an extreme manifestation of micro-moral hazard incentives, affects the probability of default. This paper introduces for the first time the concept of “claiming fraud” by credit protection buyers, which is different from the macro perspective of moral hazard incentives, and thus defines a specific extreme form of moral hazard incentives. Meanwhile, to address the intrinsic feature of the lack of economic explanatory power of the reduce-form model, this paper introduces a moral hazard incentive factor into the reduce-form model, and proposes a moral hazard state variable as a function of the asset value of the reference entity, which gives the reduce-form model strong economic explanatory power, and the default predictability is reduced by the description of the reduce-form model. In terms of the object of study, this paper considers the issue of moral hazard incentives in the presence of claiming fraud in two reference entities to further explore the impact of moral hazard incentives on default protection at the micro level in terms of cyclic default. Finally, based on the analysis of the results of the numerical simulation experiments, it is proposed that increasing the number of reference assets for CDS buyers will help to reduce the moral hazard incentives of the buyer, and thus the anticipated payments to the buyer, i.e., we attempt to endogenize the credit risk of an asset by allowing the asset holder to choose the probability of the asset going up or down, which helps to understand the phenomenon of moral hazard incentives in CDS trading.