Humboldt-Universität zu Berlin - High Dimensional Nonstationary Time Series

SFB649DP2014 066

TENET: Tail-Event driven NETwork risk

Wolfgang Karl Härdle
Weining Wang
Lining Yu

A system of risk factors necessarily involves systemic risk. The analysis of systemic risk is in the focus of recent econometric analysis and uses tail event and network based techniques. Here we bring tail event and network dynamics together into one context. In order to pursue such joint effects, we propose a semiparametric measure to estimate systemic interconnectedness across financial institutions based on tail-driven spillover effects in a high dimensional framework. The systemically important institutions are identified conditional on their interconnectedness structure. Methodologically, a variable selection technique in a time series setting is applied in the context of a single-index model for a generalized quantile regression framework. We could thus include more financial institutions into the analysis to measure their tail event interdependencies and, at the same time, being sensitive to non-linear relationships between them. Network analysis, its behaviour and dynamics, allows us to characterize the role of each industry group in the U. S. financial market 2007 - 2012. The proposed TENET - Tail Event driven NETwork technique allows us to rank the systemic risk contributions of publicly traded U.S. financial institutions.

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

JEL Classification:
G01, G18, G32, G38, C21, C51, C63