How do banks respond to changes in capital requirements as a result of the stress tests? Does the disclosure of stress test results matter? To answer these questions, we study the impact of European stress tests on banks’ lending, their corresponding risk-taking, the ensuing effect on their...

We analyse the SRISK measure with respect to its usage as a benchmark for the ECB/EBA 2014 stress test. By regressing the ECB/EBA stress test impact and the SRISK stress impact on a set of factors that are commonly associated with bank credit losses and bank vulnerability, we find that the...

Liquidity has its systemic aspect that is frequently neglected in research and risk management applications. We build a model that focuses on systemic aspects of liquidity and its links with solvency conditions accounting for pertinent interactions between market participants in an agent-based...

This paper uses panel econometric techniques to estimate a macro-financial model for fee and commission income over total assets for a broad sample of euro area banks. Using the estimated parameters, it conducts a scenario analysis projecting the fee and commission income ratio over a three...

Severe financial turbulences are driven by high impact and low probability events that are the characteristic hallmarks of systemic financial stress. These unlikely adverse events arise from the extreme tail of a probability distribution and are therefore very poorly captured by traditional...

This paper develops Bayesian econometric methods for posterior inference in non-parametric mixed frequency VARs using additive regression trees. We argue that regression tree models are ideally suited for macroeconomic nowcasting in the face of extreme observations, for instance those produced...

This paper shows how to compute the h-step-ahead predictive likelihood for any subset of the observed variables in parametric discrete time series models estimated with Bayesian methods. The subset of variables may vary across forecast horizons and the problem thereby covers marginal and joint...

This paper describes an algorithm to compute the distribution of conditional forecasts, i.e. projections of a set of variables of interest on future paths of some other variables, in dynamic systems. The algorithm is based on Kalman filtering methods and is computationally viable for large...

Vector autoregressions (VARs) are flexible time series models that can capture complex dynamic interrelationships among macroeconomic variables. However, their dense parameterization leads to unstable inference and inaccurate out-of sample forecasts, particularly for models with many variables....