We propose a framework for estimating network-driven time-varying systemic risk contributions that is applicable to a high-dimensional financial system. Tail risk dependencies and contributions are estimated based on a penalized two-stage fixed-effects quantile approach, which explicitly links...

We introduce a methodology for measuring default risk connectedness that is based on an out-of-sample variance decomposition of model forecast errors. The out-of-sample nature of the procedure leads to "realized" measures which, in practice, respond more quickly to crisis occurrences than those...

Financial systems have become increasingly complex. They are characterized by their large dimensional, strong cross-sectional dependence amounting to networks of unknown and time-varying form which appear as main drivers of risk of and within the system. Moreover, in turbulent market times,...