TENET: Tail-Event driven NETwork risk

By Wolfgang Karl Härdle, Weining Wang, Lining Yu. 2016

427 citations recorded by the source corpus.

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.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/0bf855832cc8c307af2835e90caff8f54c3f2716

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Weining Wang
2. Wolfgang Karl Härdle
3. Rangan Gupta
4. M. Balcılar
5. Han Lin Shang
6. Victor Troster

In Finance

1. Weining Wang
2. Wolfgang Karl Härdle
3. Hangsheng Wang
4. Xuening Zhu
5. Han Lin Shang

Citations in the corpus

• Network quantile autoregression (2019, background): “, 2013) and financial risk management (Gaglianone et al., 2011; Härdle et al., 2016).”
• NETS: Network estimation for time series (2019, neutral): “First, it is related to the econometric literature on networks, which includes research by Billio et al. (2012), Diebold and Yılmaz (2014, 2015), Hautsch et al. (2015), Anufriev and Panchenko (2015), Härdle et al. (2016), and Hagströmer and Menkveld (2016).”