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Robust deep hedging

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  • Eva Lütkebohmert
  • Thorsten Schmidt
  • Julian Sester

Abstract

We study pricing and hedging under parameter uncertainty for a class of Markov processes which we call generalized affine processes and which includes the Black–Scholes model as well as the constant elasticity of variance (CEV) model as special cases. Based on a general dynamic programming principle, we are able to link the associated nonlinear expectation to a variational form of the Kolmogorov equation which opens the door for fast numerical pricing in the robust framework. The main novelty of the paper is that we propose a deep hedging approach which efficiently solves the hedging problem under parameter uncertainty. We numerically evaluate this method on simulated and real data and show that the robust deep hedging outperforms existing hedging approaches in highly volatile periods.

Suggested Citation

  • Eva Lütkebohmert & Thorsten Schmidt & Julian Sester, 2022. "Robust deep hedging," Quantitative Finance, Taylor & Francis Journals, vol. 22(8), pages 1465-1480, August.
    • Handle: RePEc:taf:quantf:v:22:y:2022:i:8:p:1465-1480
    DOI: 10.1080/14697688.2022.2056073
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    Cited by:

    1. Ariel Neufeld & Julian Sester & Mario Šikić, 2023. "Markov decision processes under model uncertainty," Mathematical Finance, Wiley Blackwell, vol. 33(3), pages 618-665, July.

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