Dynamic financial tail risk networks: A backtesting-based conditional expected shortfall approach

Donghao Zhang, Xiaodong Yan and Feng Shen

PLOS ONE, 2026, vol. 21, issue 6, 1-1

Abstract: This paper develops a Factor-Copula methodology for constructing high-dimensional dynamic tail risk networks based on the conditional expected shortfall (CoES) in order to overcome the limitations of traditional quantile regression and copula models. We backtest the CoES using cumulative joint violations and conditional coverage tests. The proposed factor‑copula‑CoES model reduces the rejection rates in 10‑th order conditional backtests by 56.37%, 47.72%, and 1.96% relative to the static‑copula‑CoES, time‑varying‑copula‑CoES, and factor‑copula‑CoVaR models, respectively, with all reductions being statistically significant. The dynamic analysis of the tail risk network of Chinese listed financial institutions indicates that the network topology characteristics align with real market risk events. Finally, the quantitative analyses show different effects of institutional and market factors on network-measured risk spillovers and contagion among Chinese financial institutions.

Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0351966 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 51966&type=printable (application/pdf)

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:plo:pone00:0351966

DOI: 10.1371/journal.pone.0351966

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().