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

PLOS ONE

卷:21

Research Article

Donghao Zhang¹  Xiaodong Yan²  Feng Shen^2,3 

[1] School of Finance, Southwestern University of Finance and Economics, Chengdu, PR China

[2] Engineering Research Center of Intelligent Finance, Southwestern University of Finance and Economics, Chengdu, PR China

[3] Chinese Financial Research Institute, Southwestern University of Finance and Economics, Chengdu, PR China

关键词: Finance;    Money supply and banking;    Network analysis;    Financial markets;    Stock markets;    Local governments;    Insurance;    Pandemics;   

DOI  :  10.1371/journal.pone.0351966

 received in 2025-06-28, accepted in 2026-06-03,  发布年份 2026

来源: PLOS

【 摘 要 】

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.

【 授权许可】

CC BY   
Zhang et al 2026

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