Paper: arXiv 2604.08765

Authors: 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.

Complexity vs Empirical Score

  • Math Complexity: 5.5/10
  • Empirical Rigor: 7.5/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper introduces a novel, practical framework for ETF tail-risk monitoring that incorporates real-time data quality checks and uncertainty scoring. The methodology is empirically well-validated through a rolling walk-forward backtest, demonstrating strong performance under stressed market conditions.

Research Flowchart

  flowchart TD
    A[Research Goal: Reliable Next-Day ETF Tail-Risk Surveillance] --> B[Data Inputs: Daily ETF Panel, VIX & Yield-Curve Features]
    B --> C[Service-Time Quality Checks & Degradation Simulation]
    C --> D[Lower-Tail Prediction & Uncertainty Scoring]
    D --> E[Risk-Aware Adjustment of Tail-Risk Estimates]
    E --> F[Rolling Walk-Forward Evaluation]
    F --> G[Key Outcomes: Improved Tail-Risk Monitoring in Stressed Markets & High Reliability Under Data Degradation]

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0 of 28 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.