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...

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...

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...

We compare the out-of-sample accuracy of three methodologies—the time-varying hazard model of Shumway (2001), the static probit model used by Cole and Gunther (1998), and a static logistic regression model similar to Cole and White (2012)—in forecasting U.S. bank failures. When we limit all...

We introduce a generalization of the one-dimensional accelerated failure time model allowing the covariate effect to be any positive function of the covariate. This function and the baseline hazard rate are estimated nonparametrically via an iterative algorithm. In an application in non-life...

We propose a local adaptive multiplicative error model (MEM) accommodating timevarying parameters. MEM parameters are adaptively estimated based on a sequential testing procedure. A data-driven optimal length of local windows is selected, yielding adaptive forecasts at each point in time....

Predictions of the individual unemployment duration will allow to distribute target support while searching for a job more effectively. The paper uses survival models to predict the unemployment duration based on data from Russian employment centers in 2017-2021. The dataset includes...

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...

This paper introduces the Markov-Switching Multifractal Duration (MSMD) model by adapting the MSM stochastic volatility model of Calvet and Fisher (2004) to the duration setting. Although the MSMD process is exponential ß-mixing as we show in the paper, it is capable of generating highly...