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Sequential Monitoring for Changes in Dynamic Semiparametric Risk Models

Author

Listed:
  • Lajos Horváth

    (University of Utah)

  • Emese Lazar

    (UOR - University of Reading)

  • Zhenya Liu

    (Métis Lab EM Normandie - EM Normandie - École de Management de Normandie = EM Normandie Business School)

  • Shixuan Wang

    (UOR - University of Reading)

  • Xiaohan Xue

    (University of Bath [Bath])

Abstract

We propose a sequential monitoring scheme to detect changes in dynamic semiparametric risk models that capture Value–at–Risk (VaR) and Expected Shortfall (ES) jointly. The monitoring scheme is based on a gradient–based detector and a boundary function, and a change is detected when the detector crosses the boundary function. We derive the asymptotic limit of the stopping time of detection under the null hypothesis of no change. Monte Carlo simulations show that the proposed test has reasonable size control under the null hypothesis and high power under alternative hypotheses of various change point scenarios in finite samples. Empirical applications based on the S&P 500 index and the GBP/EUR exchange rate illustrate that our proposed test is able to detect change points in real–time.

Suggested Citation

  • Lajos Horváth & Emese Lazar & Zhenya Liu & Shixuan Wang & Xiaohan Xue, 2025. "Sequential Monitoring for Changes in Dynamic Semiparametric Risk Models," Post-Print hal-05290588, HAL.
    • Handle: RePEc:hal:journl:hal-05290588
    DOI: 10.1080/07350015.2025.2540071
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