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  3. Dynamic Financial Tail Risk Networks: A Backtesting-based Conditional Expected Shortfall Approach.
  1. Home
  3. Dynamic Financial Tail Risk Networks: A Backtesting-based Conditional Expected Shortfall Approach.

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

Donghao Zhang1, Xiaodong Yan2, Feng Shen2,3

  • 1Chinese Financial Research Institute, Southwestern University of Finance and Economics, Chengdu, PR China.

Plos One
|June 24, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a novel Factor-Copula methodology for dynamic tail risk networks, improving upon existing models. The new approach significantly reduces risk mispricing, offering better insights into financial institution contagion.

Related Experiment Videos

Area of Science:

  • Quantitative Finance
  • Financial Risk Management
  • Network Analysis

Background:

  • Traditional quantile regression and copula models have limitations in capturing high-dimensional dynamic tail risk.
  • Accurate measurement of tail risk and contagion is crucial for financial stability.

Purpose of the Study:

  • To develop a Factor-Copula methodology for constructing high-dimensional dynamic tail risk networks.
  • To overcome the limitations of existing models in assessing financial network risk.
  • To analyze risk spillovers and contagion among Chinese financial institutions.

Main Methods:

  • Development of a Factor-Copula methodology based on conditional expected shortfall (CoES).
  • Backtesting of the CoES model using cumulative joint violations and conditional coverage tests.
  • Dynamic analysis of tail risk networks for Chinese listed financial institutions.

    • Main Results:

    • The proposed factor-copula-CoES model significantly reduces rejection rates in conditional backtests compared to benchmark models.
    • Dynamic analysis reveals that network topology characteristics align with real market risk events.
    • Quantitative analyses demonstrate distinct impacts of institutional and market factors on risk spillovers and contagion.

    Conclusions:

    • The Factor-Copula-CoES methodology provides a more robust framework for dynamic tail risk network construction.
    • The model enhances the accuracy of risk assessment and provides valuable insights into financial contagion.
    • Understanding the drivers of risk spillovers is essential for mitigating systemic risk in financial markets.