In practice, multivariate dependencies of extreme risks are often only assessed in a pairwise way. We propose a novel test to detect when bivariate simplifications produce misleading results. This occurs when a significant portion of the multivariate dependence structure in the tails is of...

An accurate assessment of tail inequalities and tail asymmetries of financial returns is key for risk management and portfolio allocation. We propose a new test procedure for detecting the full extent of such structural differences in the dependence of bivariate extreme returns. We decompose the...

We introduce a new and general methodology for analyzing vector autoregressive models with time-varying coefficient matrices and conditionally heteroskedastic disturbances. Our proposed method is able to jointly treat a dynamic latent factor model for the autoregressive coefficient matrices and...

We introduce a new model for time-varying spatial dependence. The model extends the well-known static spatial lag model. All parameters can be estimated conveniently by maximum likelihood. We establish the theoretical properties of the model and show that the maximum likelihood estimator for the...

A new model for time-varying spatial dependencies is introduced. It forms an extension to the popular spatial lag model and can be estimated conveniently by maximum likelihood. The spatial dependence parameter is assumed to follow a generalized autoregressive score (GAS) process. The theoretical...

We introduce a new model for time-varying spatial dependence. The model extends the well-known static spatial lag model. All parameters can be estimated conveniently by maximum likelihood. We establish the theoretical properties of the model and show that the maximum likelihood estimator for the...

We propose the dynamic network effect (DNE) model for the study of high-dimensional multivariate time series data. Cross-sectional dependencies between units are captured via one or multiple observed networks and a low-dimensional vector of latent stochastic network effects. The...

We introduce a new and general methodology for analyzing vector autoregressive models with time-varying coefficient matrices and conditionally heteroskedastic disturbances. Our proposed method is able to jointly treat a dynamic latent factor model for the autoregressive coefficient matrices and...