Tail event driven networks of SIFIs

Chen, C. Y.-H. , Härdle, W. K. and Okhrin, Y. (2019) Tail event driven networks of SIFIs. Journal of Econometrics, 208(1), pp. 282-298. (doi: 10.1016/j.jeconom.2018.09.016)

Full text not currently available from Enlighten.


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 panel quantile autoregression involving network effects that are quantified through a time-varying adjacency matrix. To reflect the risk content in stress situations the construction of the adjacency matrix is suggested to include tail events. More precisely we employ the conditional expected shortfall as risk profile. Based on the similarity of the risk profiles we create a positive and a negative network factor, which capture the effects of risk contagion and risk diversification respectively. The developed joint spacings variance ratio test supports the suggested methodology. The TENQR technique is evaluated using the SIFIs (systemically important financial institutions) identified by the Financial Stability Board (FSB). The risk decomposition of the resulting network identifies the systemic importance of SIFIs and thus provides measures for the required level of additional loss absorbency. It is discovered that the positive network effect, as a function of the tail probability level, becomes more profound in stress situations and varies in its impact to SIFIs located in different geographic regions.

Item Type:Articles
Additional Information:Financial support from the German Research Foundation (DFG) via Collaborative Research Center 649 “Economic Risk” and International Research Training Group 1792 “High Dimensional Nonstationary Time Series”, Humboldt-Universität zu Berlin, is gratefully acknowledged.
Glasgow Author(s) Enlighten ID:Chen, Professor Cathy Yi-Hsuan
Authors: Chen, C. Y.-H., Härdle, W. K., and Okhrin, Y.
College/School:College of Social Sciences > Adam Smith Business School > Accounting and Finance
Journal Name:Journal of Econometrics
ISSN (Online):1872-6895
Published Online:12 October 2018

University Staff: Request a correction | Enlighten Editors: Update this record