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