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Inference on dynamic systemic risk measures

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  • Francq, Christian
  • Zakoïan, Jean-Michel

Abstract

Systemic risk measures (SRM) quantify the risk of a system induced by the possible distress of any of its components. Applications in economics and finance are numerous. We define a general dynamic framework for the risk factors, allowing us to obtain explicit expressions of the corresponding dynamic SRM. We deduce an easy-to-implement statistical approach which, based on semi-parametric assumptions, reduces to estimating univariate location-scale models and to computing (static) quantiles of the residuals. We derive a sound asymptotic theory (including confidence intervals, tests, validity of a residual bootstrap) for major SRM, namely the Conditional VaR (CoVaR) and Delta-CoVaR. Our theoretical results are illustrated via Monte-Carlo experiments and real financial and macroeconomic time series.

Suggested Citation

  • Francq, Christian & Zakoïan, Jean-Michel, 2025. "Inference on dynamic systemic risk measures," Journal of Econometrics, Elsevier, vol. 247(C).
    • Handle: RePEc:eee:econom:v:247:y:2025:i:c:s0304407624002872
    DOI: 10.1016/j.jeconom.2024.105936
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    More about this item

    Keywords

    CoVaR; Delta-CoVaR; Marginal expected shortfall; Multivariate risks; Residual bootstrap;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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