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 propose the realized systemic risk beta as a measure for financial companies' contribution to systemic risk given network interdependence between firms' tail risk exposures. Conditional on statistically pre-identified network spillover effects and market as well as balance sheet information,...

We propose a new unified approach to identifying and estimating spatio-temporal dependence structures in large panels. The model accommodates global cross-sectional dependence due to global dynamic factors as well as local cross-sectional dependence, which may arise from local network...

We introduce a new dynamic clustering method for multivariate panel data char- acterized by time-variation in cluster locations and shapes, cluster compositions, and, possibly, the number of clusters. To avoid overly frequent cluster switching (flickering), we extend standard cross-sectional...

This thesis develops new methods to assess two types of financial risk. Market risk is defined as the risk of losing money due to drops in the values of asset portfolios. Systemic risk refers to the breakdown risk for the financial system induced by the distress of individual companies. During...

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 a dynamic clustering model for studying time-varying group structures in multivariate panel data. The model is dynamic in three ways: First, the cluster means and covariance matrices are time-varying to track gradual changes in cluster characteristics over time. Second, the units of...

We analyse whether financial integration between countries leads to converging or diverging business cycles using a dynamic spatial model. Our model allows for contemporaneous spillovers of shocks to GDP growth between countries that are financially integrated and delivers a scalar measure of...