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Reliability-Aware ETF Tail-Risk Monitoring

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Listed:
  • Tenghan Zhong
  • Keyuan Wu

Abstract

Daily ETF risk monitoring can become unreliable when market data quality degrades, market conditions shift, or predictive performance becomes unstable. This paper develops a reliability-aware risk monitoring service for next-day tail-risk surveillance. The proposed framework combines service-time quality checks, lower-tail prediction, uncertainty scoring, and risk-aware adjustment of the tail-risk estimate. We evaluate the system on a daily panel of multiple ETFs augmented with VIX and yield-curve information under a rolling walk-forward design. Empirically, the framework improves tail-risk monitoring, especially during stressed periods, while remaining reliable under simulated input degradation.

Suggested Citation

  • Tenghan Zhong & Keyuan Wu, 2026. "Reliability-Aware ETF Tail-Risk Monitoring," Papers 2604.08765, arXiv.org, revised Apr 2026.
  • Handle: RePEc:arx:papers:2604.08765
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    File URL: http://arxiv.org/pdf/2604.08765
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    References listed on IDEAS

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    2. Georgios Fatouros & Georgios Makridis & Dimitrios Kotios & John Soldatos & Michael Filippakis & Dimosthenis Kyriazis, 2023. "DeepVaR: a framework for portfolio risk assessment leveraging probabilistic deep neural networks," Digital Finance, Springer, vol. 5(1), pages 29-56, March.
    3. Vito Ciciretti & Monomita Nandy & Alberto Pallotta & Suman Lodh & P. K. Senyo & Jekaterina Kartasova, 2025. "An early-warning risk signals framework to capture systematic risk in financial markets," Quantitative Finance, Taylor & Francis Journals, vol. 25(5), pages 757-771, May.
    4. Trung H. Le, 2024. "Forecasting VaR and ES in emerging markets: The role of time‐varying higher moments," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 402-414, March.
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