We propose the CoJPoD, a novel framework explicitly linking the cross-sectional and cyclical dimensions of systemic risk. In this framework, banking sector distress in the form of the joint probability of default of financial intermediaries (reflecting contagion from both direct and indirect...

Credit risk models used in quantitative risk management treat credit risk analysis conceptually like a single person decision problem. From this perspective an exogenous source of risk drives the fundamental parameters of credit risk: probability of default, exposure at default and the recovery...

Utilising a unique data set with annual accounts from around 37,000 Danish non-financial firms spanning almost one and a half decade, we offer microeconometric evidence on bankfirm relationships and the survival of firms during the financial crisis 2008-9. Within the framework of...

The paper develops an early-warning model for predicting vulnerabilities leading to distress in European banks using both bank and country-level data. As outright bank failures have been rare in Europe, the paper introduces a novel dataset that complements bankruptcies and defaults with state...

Modelling the link between the global macro-financial factors and firms’ default probabilities constitutes an elementary part of financial sector stress-testing frameworks. Using the Global Vector Autoregressive(GVAR) model and constructing a linking satellite equation for the firm-level...

We propose the CoJPoD, a novel framework explicitly linking the cross-sectional and cyclical dimensions of systemic risk. In this framework, banking sector distress in the form of the joint probability of default of financial intermediaries (reflecting contagion from both direct and indirect...

In this study we combine clustering techniques with a moving window algorithm in order to filter financial market data outliers. We apply the algorithm to a set of financial market data which consists of 25 series selected from a larger dataset using a cluster analysis technique taking into...

In terms of regulatory and economic capital, credit risk is the most significant risk faced by banks. We implement a credit risk model - based on publicly available information - with the aim of developing a tool to monitor credit risk in a sample of large and complex banking groups (LCBGs) in...

In terms of regulatory and economic capital, credit risk is the most significant risk faced by banks. We implement a credit risk model - based on publicly available information - with the aim of developing a tool to monitor credit risk in a sample of large and complex banking groups (LCBGs) in...

In this study we combine clustering techniques with a moving window algorithm in order to filter financial market data outliers. We apply the algorithm to a set of financial market data which consists of 25 series selected from a larger dataset using a cluster analysis technique taking into...