Paper: arXiv 2303.01651

Authors: Yuru Sun, Worapree Maneesoonthorn, Ruben Loaiza-Maya, Gael M. Martin

Abstract

This paper explores the implications of producing forecast distributions that are optimized according to scoring rules that are relevant to financial risk management. We assess the predictive performance of optimal forecasts from potentially misspecified models for i) value-at-risk and expected shortfall predictions; and ii) prediction of the VIX volatility index for use in hedging strategies involving VIX futures. Our empirical results show that calibrating the predictive distribution using a score that rewards the accurate prediction of extreme returns improves the VaR and ES predictions. Tail-focused predictive distributions are also shown to yield better outcomes in hedging strategies using VIX futures.

Complexity vs Empirical Score

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

Why this score: The paper provides a strong theoretical and empirical treatment of scoring rules customized for tail-risk management and VIX hedging. It effectively bridges loss-function-driven distribution forecasting with practical financial risk applications and empirical backtesting.

Research Flowchart

  flowchart TD
    A[Goal: Evaluate forecasts optimized with tail-focused scoring rules for risk management] --> B[Input Data: Financial Returns & VIX Futures]
    B --> C[Methodology: Calibrate predictive distributions using scoring rules targeting extreme events]
    C --> D[Computational Evaluation: Predict VaR, Expected Shortfall ES, and VIX Volatility]
    D --> E[Outcome 1: Improved VaR and ES predictions under model misspecification]
    D --> F[Outcome 2: Better hedging strategy performance using tail-focused VIX forecasts]