Towards a fast and robust deep hedging approach

ArXiv ID: 2504.16436 “View on arXiv”

Authors: Fabienne Schmid, Daniel Oeltz

Abstract

We present a robust Deep Hedging framework for the pricing and hedging of option portfolios that significantly improves training efficiency and model robustness. In particular, we propose a neural model for training model embeddings which utilizes the paths of several advanced equity option models with stochastic volatility in order to learn the relationships that exist between hedging strategies. A key advantage of the proposed method is its ability to rapidly and reliably adapt to new market regimes through the recalibration of a low-dimensional embedding vector, rather than retraining the entire network. Moreover, we examine the observed Profit and Loss distributions on the parameter space of the models used to learn the embeddings. The results show that the proposed framework works well with data generated by complex models and can serve as a construction basis for an efficient and robust simulation tool for the systematic development of an entirely model-independent hedging strategy.

Keywords: Deep Hedging, Option Portfolios, Model Embeddings, Stochastic Volatility, Model-Independent Hedging, Options

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 6.0/10
• Quadrant: Holy Grail
• Why: The paper presents a sophisticated neural network architecture with multi-task learning and embedding layers for hedging, involving advanced stochastic calculus and optimization, while providing a framework tested on simulated market data with empirical PnL analysis.

  flowchart TD
    A["Research Goal: Create Fast & Robust Deep Hedging"] --> B["Methodology: Neural Model Embeddings"]
    B --> C{"Inputs: Paths from<br>Stochastic Volatility Models"}
    C --> D["Computational Process:<br>Train Embedding Space"]
    D --> E["Outcome: Low-Dim<br>Adaptive Embeddings"]
    E --> F["Analysis: PnL on<br>Parameter Space"]
    F --> G["Key Findings:<br>Efficient Model-Independent Hedging"]