In single-obligor default risk modelling, using a background filtration in conjunction with a suitable embedding hypothesis (generally known as H-hypothesis or immersion property) has proven a very successful tool to separate the actual default event from the model for the default arrival...

This paper focuses on the key credit risk parameter–Loss Given Default (LGD). We describe its general properties and determinants with respect to seniority of debt, characteristics of debtors or macroeconomic conditions. Further, we illustrate how the LGD can be extracted from market...

In this paper we compare market prices of credit default swaps with model prices. We show that a simple reduced form model with a constant recovery rate outperforms the market practice of directly comparing bonds' credit spreads to default swap premiums. We find that the model works well for...

Using copula methods and simulation-based inference, the authors investigate the association between the performance of a stock index formed by European financial institutions and a basket of CDS contracts of the same sector. Their analysis focuses on (i) assessing the dependence structure of...

Currency and interest rate swaps are subject to a complex, two-sided default risk. Several theoretical papers have recently addressed the problem of pricing this swap credit risk. We implement a recent credit risk pricing model in an attempt to evaluate one of the main lines of research in...

Currency and interest rate swaps are subject to a complex, two-sided default risk. Although several theoretical papers have recently addressed the problem of pricing swap credit risk, the empirical literature is almost non-existent. This is the only study we know which uses actual transaction...

The standard measures of distress risk ignore the fact that firm defaults are correlated and that some defaults are more likely to occur in bad times. We use risk premium computed from corporate credit spreads to measure a firm’s exposure to systematic variation in default risk. Unlike...