We argue that the true transition-to-default dynamic in banks' credit portfolios can only be fully described with a multiple-spell discrete-time hazard model. This paper develops such a model for default prediction. The model permits the use of all data available to the bank or to the bank...

We develop distress prediction models for non-financial small and medium sized enterprises (SMEs) using a dataset from eight European countries over the period 2000-2009. We examine idiosyncratic and systematic covariates and find that macro conditions and bankruptcy codes add predictive power...

This study aims to shed light on the debate concerning the choice between discrete-time and continuous-time hazard models in making bankruptcy or any binary prediction using interval censored data. Building on the theoretical suggestions from various disciplines, we empirically compare widely...

This dissertation is concerned with the forecasting performance of time series models for the price movements of high-frequency transaction data on the Frankfurt Stock Exchange. The availability of high quality data of this kind at an affordable cost makes it possible to investigate the...

In credit default prediction models, the need to deal with time-varying covariates often arises. For instance, in the context of corporate default prediction a typical approach is to estimate a hazard model by regressing the hazard rate on time-varying covariates like balance sheet or stock...