A financial distress of company should be able anticipated smartly by its management to rerun the business without having any loss due to business failure. Thus, we need a model which could provide an early signal to company the probability of financial distress so that remedial efforts can be...

Using a local adaptive Forward Intensities Approach (FIA) we investigate multiperiod corporate defaults and other delisting schemes. The proposed approach is fully datadriven and is based on local adaptive estimation and the selection of optimal estimation windows. Time-dependent model...

This paper examines the performance of two commonly applied bankruptcy prediction models, the accounting ratio-based Altman Z-Score model, and the structural Distance to Default model which currently underlies Morningstar's Financial Health Grade for public companies (Morningstar 2008)....

Probability of default prediction is one of the important tasks of rating agencies as well as of banks and other financial companies to measure the default risk of their counterparties. Knowing predictors that significantly contribute to default prediction provides a better insight into...

While there is an increasing interest in crypto-assets, the credit risk of these exchanges is still relatively unexplored. To fill this gap, we consider a unique data set on 144 exchanges active from the first quarter of 2018 to the first quarter of 2021. We analyze the determinants of the...

We use a vector error correction model to study the long-term relationship between aggregate expected default frequency and the macroeconomic development, i.e. CPI, industry production and short-term interest rate. The model is used to forecast the median expected default frequency of the...