Model-free price bounds under dynamic option trading

Neufeld, Ariel; Sester, Julian

doi:10.48550/arxiv.2101.01024

Cited by 1 publication

(1 citation statement)

References 30 publications

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“…More recent contributions are Cohen and Tegnér (2017), Barnett et al (2020), Aksamit et al (2020), Cheridito et al (2017), Akthari et al (2020). In the context of option pricing and efficient hedging, Bouchard et al (2015), Hou and Ob lój (2018) developed approaches respecting an ambiguity set of possible underlying probability measures and Acciaio et al (2016), Beiglböck et al (2013), Cox and Ob lój (2011), Dolinsky and Soner (2014), Lütkebohmert and Sester (2019), Neufeld and Sester (2021b) introduced approaches to entirely model-free option pricing and to model-free super-replication.…”

Section: Introductionmentioning

confidence: 99%

Robust deep hedging

Lütkebohmert¹,

Schmidt²,

Sester³

2021

Preprint

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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 particular in highly volatile periods.

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