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 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...

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...

This paper presents a framework for estimating losses in the residential real estate mortgage portfolios of German banks. We develop an EL model where LGD estimates are based on current collateral values and PD dynamics are estimated using a structural PVAR approach. We confirm empirically that...

We provide a simulation smoother to a exible state-space model with lagged states and lagged dependent variables. Qian (2014) has introduced this state-space model and proposes a fast Kalman filter with time-varying state dimension in the presence of missing observations in the data. In this...

Using a nonlinear Bayesian likelihood approach that fully accounts for the lower bound on nominal interest rates, we analyze US post-crisis macroeconomic dynamics and provide reference parameter estimates. We find that despite the attention received in the literature, neither the inclusion of...

This paper investigates the ability of several generalized Bayesian vector autoregressions to cope with the extreme COVID-19 observations and discusses their impact on prior calibration for inference and forecasting purposes. It shows that the preferred model interprets the pandemic episode as a...