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Solving The Dynamic Volatility Fitting Problem: A Deep Reinforcement Learning Approach

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  • Emmanuel Gnabeyeu
  • Omar Karkar
  • Imad Idboufous

Abstract

The volatility fitting is one of the core problems in the equity derivatives business. Through a set of deterministic rules, the degrees of freedom in the implied volatility surface encoding (parametrization, density, diffusion) are defined. Whilst very effective, this approach widespread in the industry is not natively tailored to learn from shifts in market regimes and discover unsuspected optimal behaviors. In this paper, we change the classical paradigm and apply the latest advances in Deep Reinforcement Learning(DRL) to solve the fitting problem. In particular, we show that variants of Deep Deterministic Policy Gradient (DDPG) and Soft Actor Critic (SAC) can achieve at least as good as standard fitting algorithms. Furthermore, we explain why the reinforcement learning framework is appropriate to handle complex objective functions and is natively adapted for online learning.

Suggested Citation

  • Emmanuel Gnabeyeu & Omar Karkar & Imad Idboufous, 2024. "Solving The Dynamic Volatility Fitting Problem: A Deep Reinforcement Learning Approach," Papers 2410.11789, arXiv.org.
    • Handle: RePEc:arx:papers:2410.11789
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    References listed on IDEAS

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    6. Jay Cao & Jacky Chen & John Hull & Zissis Poulos, 2021. "Deep Hedging of Derivatives Using Reinforcement Learning," Papers 2103.16409, arXiv.org.
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