Paper: arXiv 2504.06208

Authors: Pascal François, Geneviève Gauthier, Frédéric Godin, Carlos O. Pérez-Mendoza

Abstract

We propose a deep hedging framework for index option portfolios, grounded in a realistic market simulator that captures the joint dynamics of S&P 500 returns and the full implied volatility surface. Our approach integrates surface-informed decisions with multiple hedging instruments and explicitly accounts for transaction costs. The hedging strategy also considers the variance risk premium embedded in the hedging instruments, enabling more informed and adaptive risk management. Tested on a historical out-of-sample set of straddles from 2020 to 2023, our method consistently outperforms traditional delta-gamma hedging strategies across a range of market conditions.

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 8.5/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper provides a rigorous extension of deep hedging frameworks by integrating a realistic joint S&P 500 and implied volatility surface market simulator (JIVR) to hedge option portfolios with multi-asset instruments. The empirical validation is exceptionally strong, featuring historical out-of-sample backtesting (2020-2023) under realistic transaction costs, and includes a publicly accessible GitHub repository for full replication.

Research Flowchart

  flowchart TD
    A[Research Goal: Outperform Delta-Gamma Hedging for Index Options] --> B[Data Inputs: S&P 500 Returns, Implied Volatility Surface, 2020-2023 Straddles]
    B --> C[Market Simulator: Captures Joint Asset Dynamics & Variance Risk Premium]
    C --> D[Deep Hedging Framework: Multi-Instrument Decisions with Transaction Costs]
    D --> E[Historical Out-of-Sample Backtesting]
    E --> F[Outcome: Deep Hedging Consistently Outperforms Traditional Delta-Gamma Hedging]

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5 of 96 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.