A Structural Approach for Predicting Default Correlation

Abstract

Default correlation is a critical concept in risk management for fixed income investment, bank management, and insurance industry, working capital management, among many. We extend the Leland-Toft term structure model into a two-firm environment and predict the default correlation between two firms by directly simulating the calibrate model based on the observed equity data (1990-2010) for various ratings. Using our empirical default correlation estimation as the benchmark, our investigation sheds more light on the structural approach in predicting default correlation. The results show that our approach outperforms the previous Zhou’s model, thereby our approach is not only theoretical improvement, but also has an empirical advantage.