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...

We propose the dynamic network effect (DNE) model for the study of high-dimensional multivariate time series data. Cross-sectional dependencies between units are captured via one or multiple observed networks and a low-dimensional vector of latent stochastic network effects. The...

Various economic theories are available to explain the existence of credit and default cycles. There remains empirical ambiguity, however, as to whether or these cycles coincide. Recent papers_new suggest by their empirical research set-up that they do, or at least that defaults and credit...

We model 1981-2002 annual US default frequencies for a panel of firms in different rating and age classes. The data is decomposed into a systematic and firm-specific risk component, where the systematic component reflects the general economic conditions and default climate. We have to cope with...

We study the relation between the credit cycle and macro-economic fundamentals in an intensity-based framework. Using rating transition and default data of U.S. corporates from Standard and Poor’s over the period 1980-2005 we directly estimate the credit cycle from the micro rating data. We...

Empirical studies showed that many types of network traffic exhibit long-range dependence (LRD),i.e., burstiness on a wide variety of time-scales. Given that traffic streams are indeed endowed withLRD properties, a next question is: what is their impact on network performance? To assess...

We model 1927-1997 U.S. business failure rates using a time series approach based on unobserved components. Clear evidence is found of cyclical behavior in default rates. The cycle has a period of around 10 years. We also detect longer term movements in default probabilities and default...