Default probabilities in credit risk management: estimation, model calibration, and backtesting

This doctoral thesis is devoted to estimation and examination of default probabilities (PDs) within credit risk management and comprises three various studies. In the first study, we introduce a structural credit risk model based on stable non-Gaussian processes in order to overcome distributional drawbacks of the classical Merton model. Following the Moody’s KMV estimation methodology, we conduct an empirical comparison between the results obtained from the Merton model and the stable Paretian one. Our results suggest that PDs are generally underestimated by the Merton model and that the stable Lévy model is substantially more sensitive to the periods of financial crises. The second study is devoted to examination of the performance of static and multi-period credit-scoring models for determining PDs of financial institutions. Using an extensive sample of U.S. commercial banks provided by the FFIEC, we focus on evaluating the performance of the considered scoring techniques. We find that our models provide a high predictive accuracy in distinguishing between default and non-default financial institutions. Despite the difficulty of predicting defaults in the financial sector, the proposed models perform very well also in comparison to results on scoring techniques for the corporate sector. Finally, in the third study, we examine the relationship between distress risk and returns of U.S. renewable energy companies. Using the Expected Default Frequency (EDF) measure obtained from Moody’s KMV, we demonstrate that there is a positive cross-sectional relationship between returns and evidence for a distress risk premium in this sector. The positively priced distress premium is also confirmed by investigating returns corrected for common Fama and French and Carhart risk factors. We further show that raw and risk-adjusted returns of value-weighted portfolios that take a long position in the 20% most distressed stocks and a short position in the 20% safest stocks generally outperforms the S&P 500 index throughout our sample period.