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Deep Hedging with Options Using the Implied Volatility Surface
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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.
Forward citations
Cited by 3 Pith papers
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Robust Control under Stationary Ambiguity
Policies trained under stationary latent ambiguity, implemented by refreshing the latent parameter, preserve robustness to regime shifts better than policies trained under a fixed latent draw.
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Robust Hedging Valuation Adjustment for Deep Hedging Policies under Market Frictions
A common-stress reserve framework for deep hedgers shows classical trading bands usually beat learned policies, with sparse learned execution winning only under a strict low-liquidity budget.
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Is Deep Hedging Reinforcement Learning?
Deep hedging is a Monte Carlo, actor-only, pathwise-gradient policy-gradient method, and therefore falls under the standard reinforcement learning umbrella.
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