The paper uses the Self-Organizing Map for mapping the state of financial stability and visualizing the sources of systemic risks as well as for predicting systemic financial crises. The Self-Organizing Financial Stability Map (SOFSM) enables a two-dimensional representation of a...

The paper uses the Self-Organizing Map for mapping the state of financial stability and visualizing the sources of systemic risks on a two-dimensional plane as well as for predicting systemic financial crises. The Self-Organizing Financial Stability Map (SOFSM) enables a two-dimensional...

This paper introduces a new loss function and Usefulness measure for evaluating early warning systems (EWSs) that incorporate policymakers' preferences between issuing false alarms and missing crises, as well as individual observations. The novelty derives from three enhancements: i) accounting...

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...

This paper discusses the role of risk communication in macroprudential oversight and of visualization in risk communication. Beyond the soar in availability and precision of data, the transition from firm-centric to system-wide supervision imposes obvious data needs. Moreover, broad and...

​This paper operationalizes early theoretical contributions of Hyman Minsky and applies these in the context of economic sectors and nations. Following the view of boom-bust asset cycles, depicted by the endogenous build-up of risks and their abrupt unraveling, Minsky highlighted the...

This paper introduces the ratio of debt to cash ow (D/CF) of nations and their economic sectors to macroprudential analysis, particularly as an indicator of systemic risk and vulnerabilities. While leverage is oftentimes linked to the vulnerability of a nation, the stock of total debt and the ow...