Forecasting Cross-Sections of Frailty-Correlated Default

Abstract

We propose a novel econometric model for estimating and forecasting cross-sections of time-varying conditional default probabilities. The model captures the systematic variation in corporate default counts across e.g. rating and industry groups by using dynamic factors from a large panel of selected macroeconomic and financial data as well as common unobserved risk factors. All factors are statistically and economically significant and together capture a large part of the time-variation in observed default rates. In this framework we improve the out-of-sample forecasting accuracy associated with conditional default probabilities by about 10-35% in terms of Mean Absolute Error, particularly in years of default stress.

Keywords: Non-Gaussian Panel Data, Common Factors, Unobserved Components, Forecasting Conditional Default Probabilities

JEL Classification: C33, G21