Paper: arXiv 2411.09676

Authors: Tong Pu, Yunran Wei, Yiying Zhang

Abstract

We introduce a novel class of systemic risk measures, the Vulnerability Conditional risk measures, which try to capture the “tail risk” of a risky position in scenarios where one or more market participants is experiencing financial distress. Various theoretical properties of Vulnerability Conditional risk measures, along with a series of related contribution measures, have been considered in this paper. We further introduce the backtesting procedures of VCoES and MCoES. Through numerical examples, we validate our theoretical insights and further apply our newly proposed risk measures to the empirical analysis of cryptocurrencies, demonstrating their practical relevance and utility in capturing systemic risk.

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 6.5/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper provides a rigorous mathematical framework extending systemic risk measures to Expected Shortfall using copulas and stochastic orders. It combines formal theoretical derivations with backtesting methodologies and empirical applications to cryptocurrency markets.

Research Flowchart

  flowchart TD
    A[Research Goal: Capture Tail Risk & Systemic Risk via Vulnerability Conditional Risk Measures] --> B[Data Inputs: Cryptocurrency Market Financial Data]
    B --> C[Methodology: Derive VCoES & MCoES Risk Measures]
    C --> D[Computational Processes: Mathematical Analysis & Backtesting Procedures]
    D --> E[Key Outcomes: Validated Theoretical Insights & Practical Utility in Crypto Systemic Risk]

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