TENET: Tail-Event driven NETwork risk

Wolfgang Karl Härdle, Weining Wang and Lining Yu

Journal of Econometrics, 2016, vol. 192, issue 2, 499-513

Abstract: CoVaR is a measure for systemic risk of the networked financial system conditional on institutions being under distress. The analysis of systemic risk is the focus of recent econometric analyses and uses tail event and network based techniques. Here, in this paper we bring tail event and network dynamics together into one context. In order to pursue such joint efforts, 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 to 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, be sensitive to non-linear relationships between them. Network analysis, its behaviour and dynamics, allows us to characterize the role of each financial industry group in 2007–2012: the depositories received and transmitted more risk among other groups, the insurers were less affected by the financial crisis. The proposed TENET - Tail Event driven NETwork technique allows us to rank the Systemic Risk Receivers and Systemic Risk Emitters in the US financial market.

Keywords: Systemic risk; Systemic risk network; Generalized quantile; Quantile single-index regression; Value at risk; CoVaR; Lasso (search for similar items in EconPapers)
JEL-codes: C21 C51 C63 G01 G18 G32 G38 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (192)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0304407616300161
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:192:y:2016:i:2:p:499-513

DOI: 10.1016/j.jeconom.2016.02.013

Access Statistics for this article

Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson

More articles in Journal of Econometrics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().