Ahmet Karagozoglu

Dean’s Research Award

2000-01-01

Hofstra University

Oscar Lasdon Best Dissertation Award

1999-12-31

Awarded by the Baruch College of the City University of New York

Resolution of corporate financial distress: an empirical analysis of processes and outcomes

2012

In this article, Jacobs,Karagozoglu, and Naples Layish focus on determining which types of firms are able to successfully remain independent entities through the resolution of their financial distress. The authors empirically investigate the determinants of the process utilized to resolve financial distress (private work-out versus public bankruptcy filing) and also the outcome (liquidation versus reorganization).After developing various qualitative-dependent variable models, they estimate and compare several accounting and economic variables measured at the time of default that they expect can influence the resolution process and outcome.Results reveal the ordered logistic regression specification achieves the best balance between in-sample fit, consistency with financial theory, and out-of-sample classification accuracy, as compared to more elaborate techniques, such as local regression or neural networks.The authors find the public resolution process to be associated with larger firms that have less tangibility, a greater proportion of secured debt in their capital structures, or higher risk measures. The private resolution process is likelier for firms that have more total leverage, greater measures of liquidity, a higher proportion of subordinated debt in their capital structures, or reside in a debtorfriendly bankruptcy court district.Regarding the resolution outcome, the authors find that firms that are more likely to be liquidated than reorganized have greater liquidity, more secured debt, lower cumulative abnormal returns on equity, higher loss given default, a less favorable auditor's opinion, or they will default in a better part of the credit cycle. Finally, firms more likely to be reorganized have greater leverage,more intangible assets, or a prepackaged bankruptcy. They conclude that their model is useful for risk managers and investors who are in the market for distressed or defaulted debt.

View more

Stress testing and model validation: application of the Bayesian approach to a credit risk portfolio

2015-01-01

Following the recent global financial crisis, regulators have recognized the importance of stress testing, in part due to the impact of model risk, and have implemented supervisory requirements in both the revised Basel framework and the Comprehensive Capital Analysis and Review (CCAR) program. We contribute to the literature by developing a Bayesian-based credit risk stress-testing methodology, which can be implemented by small-to-medium-sized banks, as well as presenting empirical results using data from the recent CCAR implementations. Through the application of a Bayesian model, we can formally incorporate exogenous scenarios and also quantify the uncertainty in model output that results from stochastic model inputs. We contribute to the model validation literature by comparing the proportional model risk buffer measure of the severely adverse cumulative nine-quarter loss estimate – a common way to estimate, being a measure of statistical uncertainty generated by a model – obtained from our empirical implementation of the Bayesian to the frequentist model. We find it to be 40% higher in the former than in the latter. As for the model validation exercise, the Bayesian model outperforms the frequentist model statistically significantly, according to the cumulative percentage error metric, by 2% (1.5%) over the entire sample (downturn period).

View more