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 test whether a simple measure of corporate insolvency based on equity return volatility - and denoted as Distance to Insolvency (DI) - delivers better predictions of corporate default than the widely-used Expected Default Frequency (EDF) measure computed by Moody's. We look at the predictive...

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...

As of today there are a lot of well-known bankruptcy prediction models. Scientists have been paying much attention to the development of bankruptcy prediction models since 1970. However, most of them are unable to predict bankruptcy, thereby making it impossible for firms to prevent it today....

The paper investigates predictive ability of existing bankruptcy prediction models suitable for small business by using dates of accounting report of Russian's firms. Combination of financial ratios analysis with bankruptcy prediction models' testing made it possible to identify the models...

Sergey Aivazian was the head of my department at the Moscow School of Economics, but he was much more than that. He played an important role in my life, and he contributed to my studies devoted to copula modelling. This small memoir reports how this amazingly polite and smart scientist helped me...

This paper proposes a machine learning approach to estimate physical forward default intensities. Default probabilities are computed using artificial neural networks to estimate the intensities of the inhomogeneous Poisson processes governing default process. The major contribution to previous...

This paper proposes a set of models which can be used to estimate the market risk for a portfolio of crypto-currencies, and simultaneously to estimate also their credit risk using the Zero Price Probability (ZPP) model by Fantazzini et al (2008), which is a methodology to compute the...

We test whether a simple measure of corporate insolvency based on equity return volatility - and denoted as Distance to Insolvency (DI) - delivers better predictions of corporate default than the widely-used Expected Default Frequency (EDF) measure computed by Moody's. We look at the predictive...

This paper examined a set of over two thousand crypto-coins observed between 2015 and 2020 to estimate their credit risk by computing their probability of death. We employed different definitions of dead coins, ranging from academic literature to professional practice, alternative forecasting...