Journal of Credit Risk

The metaverse is a rapidly evolving concept centered on creating an ecosystem of interconnected virtual platforms, enabling its users to engage in real-world-like activities through digital avatars. In recent years, the metaverse's ability to create immersive experiences on these parallel virtual platforms has been increasingly appreciated by its users.

Cryptocurrency markets are often characterized by market manipulation, or at the very least, by a sharp distinction between large and sophisticated investors and small retail investors. While traditional assets often see a divergence in the success of institutional traders and retail traders, we find an even more pronounced difference regarding the holders of Ether (ETH), the second-largest cryptocurrency by volume.

With the rapid advance of text-mining technology, an increasing amount of valuable information is being used to address issues related to financial distress prediction. However, the utilization and interpretability of textual data in prediction models pose challenges for its practical application. This study proposes a practical text-processing approach to extract predictive information embedded in financial reports.

Empirical divergence-based distributionally robust optimization (DRO) offers a novel approach to managing credit risk in financial institutions by accounting for data uncertainty and model misspecification. This study examines two specific applications of DRO: loss forecasting for predicting the significant increase in credit risk (SICR) status of loans under the International Financial Reporting Standard 9 expected credit loss provisioning framework; and risk limit management of corporate loans.

The rapid growth of consumer credit services has heightened financial institutions' need for enhanced risk management capabilities, as they strive to satisfy individuals' various consumption preferences. Identifying personal credit risk is crucial in financial risk management, underscoring the importance of financial institutions developing a systematic and effective credit risk identification framework to mitigate the likelihood of credit defaults.

Skip to main content Tweet Facebook LinkedIn Save this article Send to Print this page The extent to which consumers carry balances on their credit cards strongly influences the incidence of default they exhibit.

Tweet Facebook LinkedIn Save this article Send to Print this page We investigate credit portfolio tranche pricing and highlight the sensitivity of credit portfolios to dependence. The Poisson binomial distribution is extended by introducing correlation and dependence. Gaussian and shifted gamma factor models are integrated with the Poisson binomial distribution framework. Monte Carlo simulations are used to demonstrate the improvement in accuracy given by our method.

Tweet Facebook LinkedIn Save this article Send to Print this page The proposed method addresses unresolved cases incorporated in the LGD estimation process. We use the survival approach which gives more accurate LGD estimates. Adding randomness in random survival forests provides a more robust solution.

End of drawer navigation content We examine how credit rating agencies and bond investors react to credit guarantees in China’s municipal corporate bond market. Bonds guaranteed by related or non-related parties are typically upgraded by credit rating agencies, and only non-related credit guarantees lead to decreased bond finance costs. The presence of credit guarantees also significantly increases guarantors’ financing costs, which are generally not considered by credit rating agencies.

Tweet Facebook LinkedIn Save this article Send to Print this page 774 research papers in the field of credit risk are analyzed. The authors identify significant work and outline the intellectual framework of credit risk research. The study aims to assist institutions and researchers to identify pertinent areas that require further research.