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Credit risk

About: Credit risk is a research topic. Over the lifetime, 18595 publications have been published within this topic receiving 382866 citations.


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Journal ArticleDOI
TL;DR: In this paper, the authors present a new approach to incorporate dynamic default dependency in intensity-based default risk models, which uses an arbitrary default dependency structure which is specified by the Copula of the times of default, this is combined with individual intensitybased models for the defaults of the obligors without loss of the calibration of the individual default-intensity models.
Abstract: In this paper we present a new approach to incorporate dynamic default dependency in intensity-based default risk models. The model uses an arbitrary default dependency structure which is specified by the Copula of the times of default, this is combined with individual intensity-based models for the defaults of the obligors without loss of the calibration of the individual default-intensity models. The dynamics of the survival probabilities and credit spreads of individual obligors are derived and it is shown that in situations with positive dependence, the default of one obligor causes the credit spreads of the other obligors to jump upwards, as it is experienced empirically in situations with credit contagion. For the Clayton copula these jumps are proportional to the pre-default intensity. If information about other obligors is excluded, the model reduces to a standard intensity model for a single obligor, thus greatly facilitating its calibration. To illustrate the results they are also presented for Archimedean copulae in general, and Gumbel and Clayton copulae in particular. Furthermore it is shown how the default correlation can be calibrated to a Gaussian dependency structure of CreditMetrics-type.

363 citations

Journal ArticleDOI
TL;DR: A first-passage-time model is developed, providing an analytical formula for calculating default correlations that is easily implemented and conveniently used for a variety of financial applications and provides a theoretical justification for several empirical regularities in the credit risk literature.
Abstract: Evaluating default correlations or the probabilities of default by more than one firm is an important task in credit analysis, derivatives pricing, and risk management. However, default correlations cannot be measured directly, multiple-default modeling is technically difficult, and most existing credit models cannot be applied to analyze multiple defaults. This article develops a first-passage-time model, providing an analytical formula for calculating default correlations that is easily implemented and conveniently used for a variety of financial applications. The model also provides a theoretical justification for several empirical regularities in the credit risk literature. Article published by Oxford University Press on behalf of the Society for Financial Studies in its journal, The Review of Financial Studies.

362 citations

Journal ArticleDOI
TL;DR: This paper provided the first empirical analysis of credit contagion via direct counterparty effects, finding that bankruptcy announcements cause negative abnormal equity returns and increases in CDS spreads for creditors, and that creditors with large exposures are more likely to suffer from financial distress later.
Abstract: Standard credit risk models cannot explain the observed clustering of default, sometimes described as "credit contagion." This paper provides the first empirical analysis of credit contagion via direct counterparty effects. We find that bankruptcy announcements cause negative abnormal equity returns and increases in CDS spreads for creditors. In addition, creditors with large exposures are more likely to suffer from financial distress later. This suggests that counterparty risk is a potential additional channel of credit contagion. Indeed, the fear of counterparty defaults among financial institutions explains the sudden worsening of the credit crisis after the Lehman bankruptcy in September 2008.

361 citations

Journal ArticleDOI
TL;DR: In this paper, the authors consider the problem of coordination failure in corporate bankruptcy and show that without common knowledge of fundamentals, the incidence of failure is uniquely determined provided that private information is precise enough.

360 citations

Journal ArticleDOI
TL;DR: A new fuzzy support vector machine to discriminate good creditors from bad ones is proposed, reformulate this kind of two-group classification problem into a quadratic programming problem and expects it to have more generalization ability while preserving the merit of insensitive to outliers.
Abstract: Due to recent financial crises and regulatory concerns, financial intermediaries' credit risk assessment is an area of renewed interest in both the academic world and the business community. In this paper, we propose a new fuzzy support vector machine to discriminate good creditors from bad ones. Because in credit scoring areas we usually cannot label one customer as absolutely good who is sure to repay in time, or absolutely bad who will default certainly, our new fuzzy support vector machine treats every sample as both positive and negative classes, but with different memberships. By this way we expect the new fuzzy support vector machine to have more generalization ability, while preserving the merit of insensitive to outliers, as the fuzzy support vector machine (SVM) proposed in previous papers. We reformulate this kind of two-group classification problem into a quadratic programming problem. Empirical tests on three public datasets show that it can have better discriminatory power than the standard support vector machine and the fuzzy support vector machine if appropriate kernel and membership generation method are chosen.

358 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20251
2023343
2022729
2021799
2020915
2019921