Events / Upcoming
Abstract: The robustness of credit portfolio models is of great interest for financial institutions and regulators, since misspecified models translate into insufficient capital buffers and a crisis-prone financial system. In this talk, I will present a method to enhance credit portfolio models based on the model of Merton by incorporating contagion effects. While, in most models, the risks related to financial interconnectedness are neglected, we use Bayesian network methods to uncover the direct and indirect relationships between credits while maintaining the convenient representation of factor models. A range of techniques to learn the structure and parameters of financial networks from real credit default swaps data are studied and evaluated. Our approach is demonstrated in detail in a stylized portfolio, and the impact on standard risk metrics is estimated.
Bio: Ioannis Anagnostou is a Postdoctoral Researcher within the Computational Science Lab of University of Amsterdam, carrying out research on data-driven methods for risk management and financial stability. He is also a Quantitative Analyst at ING Bank where he is developing and implementing models for more accurate risk measurement and management. Dr. Anagnostou received his PhD from the University of Amsterdam having worked as a Marie Curie Early Stage Researcher at ING as part of the EU funded project BigDataFinance 2015-2019. Before joining ING, he worked for the Royal Bank of Scotland as a Senior Analyst within the Risk Analytics and Models team, where he focused on the development of credit risk grading models, Economic Capital modelling, and Stress Testing. Dr. Anagnostou holds a double MSc in Financial Mathematics with Distinction from the University of Edinburgh and Heriot-Watt University, and a BSc & MSc in Applied Mathematics from the National Technical University of Athens. During his MSc in Edinburgh, he researched contagious defaults at Scottish Widows Investment Partnership, now part of Standard Life Aberdeen. Prior to that, in Athens, he wrote his thesis on option pricing under exponential Levy models, and interned with Athens Stock Exchange and the Hellenic-American Education Foundation.
We propose a novel framework for credit risk modeling, where default or failure information together with rating or expert information are jointly incorporated in the model. These sources of information are modeled as response variables in a multivariate ordinal regression model estimated by a composite likelihood procedure. The proposed framework provides probabilities of default conditional on the rating information observed at the beginning of a predetermined period and is able to account for missing failure or credit rating information. Our approach is the first that consistently combines failure prediction models, where default indicators are used as responses, with so called “shadow rating models”, where the responses are estimates of default probabilities usually derived from the leading credit rating agencies. In our empirical analysis we apply the proposed framework to a data set of US firms over the period from 1985 to 2014. Different sets of financial ratios constructed from financial statements and market information are selected as bankruptcy predictors in line with standard literature in failure prediction modeling. We find that the joint model of failures and credit ratings outperforms state-of-the-art failure prediction models and shadow rating approaches in terms of prediction accuracy and discriminatory power.
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