The interdependence, dynamics and riskiness of financial institutions are the key features
frequently tackled in financial econometrics. We propose a Tail Event driven Network
Quantile Regression (TENQR) model which addresses these three aspects. More precisely,
our framework captures the risk propagation and dynamics in terms of a quantile (or expectile)
autoregression involving network effects quantified through an adjacency matrix.
To reflect the nature and risk content of systemic risk, the construction of the adjacency
matrix is suggested to include tail event covariates. The model is evaluated using the SIFIs
(systemically important financial institutions) identified by the Financial Stability Board
(FSB) as main players in the global financial system. The risk decomposition analysis of
it identifies the systemic importance of SIFIs and thus provides measures for the required
level of additional loss absorbency. It is discovered that the network effect, as a function
of the tail probability, becomes more profound in stress situations and brings the various
impacts to the SIFIs located in different geographic regions.
systemic risk; network analysis; network autoregression; tail event