Firm-level default models are important for bottomup modeling of the default risk of corporate debt portfolios. However, models in the literature typically have several strict assumptions which may yield biased results, notably a linear effect of covariates on the log-hazard scale, no...

Firm-level default models are important for bottomup modeling of the default risk of corporate debt portfolios. However, models in the literature typically have several strict assumptions which may yield biased results, notably a linear effect of covariates on the log-hazard scale, no...

In banking practice, rating transition matrices have become the standard approach of deriving multi-year probabilities of default (PDs) from one-year PDs, the latter normally being available from Basel ratings. Rating transition matrices have gained in importance with the newly adopted IFRS 9...

Under the IFRS 9 impairment model, entities must estimate the PD (Probability of Default) for all financial assets (and other elements) not measured at fair value through profit or loss. There are several methodologies for estimating this PD from market or historical information. However, in...

We examine the relationship between corporate governance and default risk for a sample of firms cited in the Securities and Exchange Commission's (SEC's) Accounting and Auditing Enforcement Releases (AAERs). Using hazard analysis of actual default incidence and OLS regressions of a continuous...

Accurate probability-of-distress models are central to regulators, firms, and individuals who need to evaluate the default risk of a loan portfolio. A number of papers document that recent machine learning models outperform traditional corporate distress models in terms of accurately ranking...

In recent years hazard models, using both market and accounting information, have become state of the art in predicting firm bankruptcies. However, a comprehensive test comparing their performance against the traditional accounting-based approach or the contingent claims approach is missing in...

Using a large sample of business groups from more than one hundred countries around the world, we show that group information matters for parent and subsidiary default prediction. Group firms may support each other when in financial distress. Potential group support represents an off-balance...

Accurate probability-of-distress models are central to regulators, firms, and individuals who need to evaluate the default risk of a loan portfolio. A number of papers document that recent machine learning models outperform traditional corporate distress models in terms of accurately ranking...

We examine how equity-market frictions that restrict pessimistic trading, such as short-sale constraints, affect assessments of default risk. We find that these frictions decrease the usefulness of equity-market variables for identifying defaulting firms but increase their usefulness for...