This paper exploits a recent and granular data set for 1,500 German LSIs to conduct a residential mortgage stress testing exercise. To account for model uncertainty when modeling PD dynamics we use a benchmark-constrained Bayesian model averaging approach that combines standard BMA with a...

In this paper we study the impact of model uncertainty, which occurs when linking a stress scenario to default probabilities, on reduced-form credit risk stress testing. This type of uncertainty is omnipresent in most macroeconomic stress testing applications due to short time series for banks'...

We propose a novel time-varying parameters mixed-frequency dynamic factor model which is integrated into a dynamic model averaging framework for macroeconomic nowcasting. Our suggested model can efficiently deal with the nature of the real-time data flow as well as parameter uncertainty and...

We apply Bayesian Model Averaging and a frequentistic model space analysis to assess the pricing-determinants of credit default swaps (CDS). Our study focuses on the complete model space of plausible models covering most of the variables and specifications used elsewhere in the literature,...

A structural Bayesian vector autoregression model predicts that - when accompanied by a decline in consumer confidence - a one-percent decrease in house prices is associated with a contraction of economic activity by 0.2 to 1.2 percent after one year. Results point to important second-round...

This paper investigates how the ordering of variables affects properties of the time-varying covariance matrix in the Cholesky multivariate stochastic volatility model.It establishes that systematically different dynamic restrictions are imposed whenthe ratio of volatilities is time-varying....

This paper uses a time-varying vector autoregressive (VAR) model for the euro area to explore the changes in the interest rate pass-through to bank retail rates following conventional and unconventional monetary policy shocks. The median estimate of the impulse responses shows a considerably...

The early warning system literature on banking crises has often relied on linear classifiers such as the logit model, which are usually estimated with large datasets of multiple regions of countries. We construct an EWS based on an artificial neural network model with monthly data from the...

Using a unique data set on German banks' loans to the German real economy, we investigate banks' credit risk. This data set includes the volume of loans per bank and industry as well as the corresponding write-downs. Our empirical study for the period 2003-2011 yields the following results: (i)...

Using unique supervisory survey data on the impact of a hypothetical interest rate shock on German banks, we analyse price and quantity effects on banks' net interest margin components under different balance sheet assumptions. In the first year, the cross-sectional variation of banks' simulated...