Journal of Credit Risk
ISSN:
1744-6619 (print)
1755-9723 (online)
Editor-in-chief: Linda Allen and Jens Hilscher
Volume 19, Number 1 (March 2023)
Editor's Letter
Iftekhar Hasan
Gabelli School of Business, Fordham University
Alev Yildirim
Queens College School of Business, City University of New York
It is our pleasure to introduce this special issue of The Journal of Credit Risk on “Risk, Distress, Default and Sustainability”, which includes some of the outstanding papers presented at the 2021 International Risk Management Conference. In this issue we introduce new research on topics that identify the relationships between environmental risk and credit risk, managers’ corporate risk-taking behavior and stock volatility, alternative models of financial distress predictions and of probability of default estimations, and the effect of the spillover of private sector risks and sustainability risks on sovereign credit risk.
The issue’s first paper, “Climate-policy-relevant sectors and credit risk” by Marcin Borsuk, assesses the effects of climate-related financial risks by exploring credit risk channels from a bank perspective. In particular, this study provides evidence that banks exposed to climate-policy-relevant sectors are more sensitive to changes in environmental risk drivers and are thus subject to a higher credit risk than those exposed to sectors with lower carbon emissions.
The second paper in the issue, “Managerial connections and corporate risk-taking: evidence from the Great Recession” by N. K. Chidambaran and Stefano Manfredonia, empirically analyzes the relationship between managers’ connections, corporate risk-taking and corporate performance during the Great Recession of 2007–9 as a natural experiment. Chidambaran and Manfredonia show that corporate equity volatility increased substantially for firms operating in sectors severely affected by this crisis. This effect was greater for firms with well-connected managers. These firms adopted riskier corporate policies, yet managers’ connections also helped these firms to recover to their precrisis levels of performance more quickly.
Our third paper, “Dynamic class-imbalanced financial distress prediction based on case-based reasoning integrated with time weighting and resampling” by Jie Sun, Mingyang Sun, Mengru Zhao and Yingying Du, proposes a new risk management tool: the dynamic class-imbalanced case-based reasoning (CBR) financial distress prediction (FDP) model. This not only gives a satisfying performance by effectively treating the problems of financial distress concept drift and class imbalance but also allows good interpretability by corporate managers and stakeholders in real-world applications. Using real-world data from Chinese listed companies, Sun et alshow that the proposed dynamic class-imbalanced CBR FDP model outperforms both static and dynamic CBR FDP models without resampling or time weighting.
The issue’s fourth paper, “Calibration alternatives to logistic regression and their potential for transferring the statistical dispersion of discriminatory power into uncertainties in probabilities of default” by Jan Henrik Wosnitza, introduces two new single-parameter families of differentiable functions as alternatives to linear logistic regression in estimating probabilities of default, the key component of credit risk analysis in banks’ lending activity. These models are based on the maximum entropy principle, and therefore they rely on a minimum number of assumptions. Wosnitza finds that one of the new single-parameter families outperforms the linear logistic regression on a real world data set.
The final paper in the issue, “Sovereign credit risk modeling using machine learning: a novel approach to sovereign credit risk incorporating private sector and sustainability risks”, sees Arsh Anand, Bart Baesens and Rosanne Vanpée exploring the evolution of the drivers of sovereign credit risk and stresses the importance of understanding additional risk drivers in sovereign credit risk assessment. In particular, Anand et al assess the effect of spillover from private sector risks into sovereign debt risk as well as the impact of rising sustainability risks on sovereign credit risk and their underlying relationship. When modeling sovereign credit risk, they find that both factors have significant sustainability risks that are financially material to sovereign creditworthiness.
We believe that both academics and practitioners will find the papers in this special issue of The Journal of Credit Risk to be of great interest. We hope you enjoy reading them.
Papers in this issue
Climate-policy-relevant sectors and credit risk
This paper explores the relationship between banks exposed to climate-policy-relevant sectors and credit risk, finding that banks exposed to higher carbon emitting sectors are subject to greater credit risk than those exposed to less carbon emitting…
Managerial connections and corporate risk-taking: evidence from the Great Recession
Using the the 2007-9 Great Recession as an example, the authors investigate the relationship between managers’ connections, corporate risk-taking and corporate performance during a period of crisis.
Dynamic class-imbalanced financial distress prediction based on case-based reasoning integrated with time weighting and resampling
The authors put forward a dynamic class-imbalanced CBR FDP model which is shown, using data from Chinese listed companies, to outperform static and dynamic CBR FDP models without resampling or time weighting.
Calibration alternatives to logistic regression and their potential for transferring the statistical dispersion of discriminatory power into uncertainties in probabilities of default
This paper compares four calibration approaches to linear logistic regression in credit risk estimation and proposes two new single-parameter families of differentiable functions as candidates for this regression.
Sovereign credit risk modeling using machine learning: a novel approach to sovereign credit risk incorporating private sector and sustainability risks
The authors investigate the effect of spillover effects from private sector risks on sovereign debt risk and the impact of rising sustainability risks on sovereign credit risk using the XGBoost classification algorithm and model interpretability…