Adversarial Deep Hedging: Learning to Hedge without Price Process Modeling
Adversarial deep hedging uses adversarial learning to optimize hedging strategies without explicit price process modeling.
What it examines
This paper introduces adversarial deep hedging, a novel framework for derivative hedging in incomplete markets. It leverages adversarial learning to train a hedger and a generator without explicitly modeling the underlying asset price process, aiming to achieve robust hedging strategies under realistic market conditions.
What it concludes
The study concludes that adversarial deep hedging is effective for robust derivative hedging without explicit price modeling. Potential applications include financial risk management and derivative trading. Future research should focus on improving learning stability and computational efficiency.
Evidence objects
The study concludes that adversarial deep hedging is effective for robust derivative hedging without explicit price modeling. Potential applications include financial risk management and derivative trading. Future research should focus on improving learning stability and computational efficiency.
key_findings bullet 1 · key_findings · validation V0
Raw abstract and provenance
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 cru… ▽ More 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. △ Less
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