Stressed distance to default and default risk

Nan Guo and LIngfei Li

Abstract

Distance to default (DTD) is a strong predictor of default risk. In this paper we propose a stressed version of DTD (the “stressed DTD”) to measure time-varying corporate default risk in the event that a systematic stress scenario occurs.We show that the stressed DTD is a better predictor of corporate defaults during the 2007–9 global financial crisis compared with the regular DTD, the Campbell–Hilscher–Szilagyi probability of default measure and the systematic default risk measure (“failure beta”). By controlling for raw default probability and failure beta, we show that the stressed DTD can explain variations in both credit default swap spreads and credit ratings. Our results provide both evidence that investors require compensation for exposure to stressed default risk and a rationale for considering credit stability under stress in ratings.