Measuring systemic risk in the global banking sector: A cross-quantilogram network approach
Syed Jawad Hussain Shahzad and
Tomáš Výrost ()
Economic Modelling, 2022, vol. 109, issue C
We propose a new systemic risk index based on the interdependence of extreme downside movements of stock returns using the cross-quantilogram and network analysis approach. While quantile dependence allows for sensitivity in times of market downturn, the topological network properties allow for capturing the interconnectedness of the banking system and identification of the specific contribution of each individual bank. Using this design, the proposed systemic risk index is not only easy to calculate and interpret but identifies the banking system's significant transmitters and receivers of extreme downside risk. For the empirical evaluation of the proposed risk index, we use a sample of 83 large banks during the 2003–2020 period, spanning multiple recent crises affecting the banking market. The proposed index is found to be robust in comparison to major alternative systemic risk measures.
Keywords: Systemic risk; Downturn interdependence; Network; Cross-quantilograms; Global banks; COVID-19 pandemic (search for similar items in EconPapers)
JEL-codes: G01 G21 (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2) Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only
Journal Article: Measuring systemic risk in the global banking sector: A cross-quantilogram network approach (2022)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:eee:ecmode:v:109:y:2022:i:c:s0264999322000219
Access Statistics for this article
Economic Modelling is currently edited by S. Hall and P. Pauly
More articles in Economic Modelling from Elsevier
Bibliographic data for series maintained by Catherine Liu ().