Adversarial Deep Hedging: Learning to Hedge without Price Process Modeling

ArXiv ID: 2307.13217 “View on arXiv”

Authors: Unknown

Abstract

Deep hedging is a deep-learning-based framework for derivative hedging in incomplete markets. The advantage of deep hedging lies in its ability to handle various realistic market conditions, such as market frictions, which are challenging to address within the traditional mathematical finance framework. Since deep hedging relies on market simulation, the underlying asset price process model is crucial. However, existing literature on deep hedging often relies on traditional mathematical finance models, e.g., Brownian motion and stochastic volatility models, and discovering effective underlying asset models for deep hedging learning has been a challenge. In this study, we propose a new framework called adversarial deep hedging, inspired by adversarial learning. In this framework, a hedger and a generator, which respectively model the underlying asset process and the underlying asset process, are trained in an adversarial manner. The proposed method enables to learn a robust hedger without explicitly modeling the underlying asset process. Through numerical experiments, we demonstrate that our proposed method achieves competitive performance to models that assume explicit underlying asset processes across various real market data.

Keywords: Deep Hedging, Adversarial Learning, Derivative Hedging, Incomplete Markets, Market Frictions, Derivatives

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 6.5/10
• Quadrant: Holy Grail
• Why: The paper introduces advanced adversarial learning concepts applied to deep hedging, requiring a solid grasp of stochastic calculus and neural network training. It demonstrates robust empirical validation using real market data and multiple option types, though it lacks open-source code or exhaustive backtesting details.

  flowchart TD
    A["Research Goal: Hedge derivatives without modeling asset price process"] --> B["Input: Real Market Data"]
    B --> C["Methodology: Adversarial Deep Hedging"]
    C --> D["Computational Process: Adversarial Training<br>Hedger vs. Generator"]
    D --> E{"Outcome"}
    E --> F["Robust Hedger Learned"]
    E --> G["Implicit Asset Process Captured"]
    F & G --> H["Key Finding: Competitive performance<br>vs. traditional models"]