We use machine learning methods for modeling multi-period corporate default probabilities and obtain higher prediction accuracy compared to linear models with the differences being larger for longer prediction horizons. Overall, tree-boosting has the highest prediction accuracy. In addition, we...

Investigating linkages between credit and equity markets, we consider daily aggregate U.S. CDS spreads as well as well-chosen equity market and implied volatility indexes over ten years. We describe such robust (to spurious correlation) relationship with the quantile cointegrating regression...

Models for assessing the probability of default play an important role in the risk management systems of commercial banks, as they allow assessing the creditworthiness of various counterparties and transactions. Many Russian banks are trying to switch to an advanced approach based on internal...