We propose a semiparametric measure to estimate systemic interconnectedness across financial institutions based on tail-driven spill-over effects in a ultra-high dimensional framework. Methodologically, we employ a variable selection technique in a time series setting in the context of a single-index model for a generalized quantile regression framework. We can thus include more financial institutions into the analysis, to measure their interdependencies in tails and, at the same time, to take into account non-linear relationships between them. A empirical application on a set of 200 publicly traded U. S. nancial institutions provides useful rankings of systemic exposure and systemic contribution at various stages of financial crisis. Network analysis, its behaviour and dynamics, allows us to characterize a role of each sector in the financial crisis and yields a new perspective of the nancial markets at the U. S. financial market 2007 - 2012.

Keywords: Systemic Risk, Systemic Risk Network, Generalized Quantile, Quantile Single- Index Regression, Value at Risk, CoVaR, Lasso