This paper employs a novel multi-country dataset of corporate defaults to develop a model of
distress risk specific to emerging markets. The data suggest that global financial variables …

A factor model with sparsely correlated residuals is used to model short-term probabilities of
default and other corporate exits while permitting missing data, and serves as the basis for …

Abstract Sequential Monte Carlo (SMC) is a powerful technique originally developed for
particle filtering and Bayesian inference. As a generic optimizer for statistical and …

This paper studies the transmission of macroprudential policies across both financial and
non financial sectors of the economy. It first documents that tighter macroprudential …

As previous literature has documented cross-industry returns and tail risk predictability,
especially during a financial crisis, this research investigates the effects of industries' …

We document average abnormal stock returns of 14% for international insurance firms
designated as Global Systemically Important Insurers (G-SII). These gains are associated …

This study considers multiperiod bankruptcy prediction models, an aspect scarcely
considered in research despite its importance, since creditors must assess the risk of loans …

A number of papers document that recent machine learning models outperform traditional
corporate distress models in terms of accurately ranking firms by their riskiness. However, it …

This paper suggests a novel approach to assess corporate sector solvency risk. The
approach uses a Bottom-Up Default Analysis that projects probabilities of default of …

We consider discrete default intensity-based and logit-type reduced-form models for
conditional default probabilities for corporate loans where we develop simple closed-form …