Robust Deep Hedging

27 Pages Posted: 30 Jun 2021 Last revised: 29 Nov 2021

See all articles by Eva Luetkebohmert

Eva Luetkebohmert

University of Freiburg, Institute for Economic Research; affiliation not provided to SSRN

Thorsten Schmidt

University of Freiburg

Julian Sester

University of Freiburg; Nanyang Technological University (NTU) - School of Physical and Mathematical Sciences

Date Written: November 29, 2021

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 particular in highly volatile periods.

Keywords: affine processes, Knightian uncertainty, Kolmogorov equation, deep learning, robust hedging

JEL Classification: C02, C45, G13

Suggested Citation

Luetkebohmert, Eva and Schmidt, Thorsten and Sester, Julian and Sester, Julian, Robust Deep Hedging (November 29, 2021). Available at SSRN: https://ssrn.com/abstract=3869616 or http://dx.doi.org/10.2139/ssrn.3869616

Eva Luetkebohmert

University of Freiburg, Institute for Economic Research ( email )

Rempartstr. 16
Freiburg, D-79098
Germany

affiliation not provided to SSRN

Thorsten Schmidt (Contact Author)

University of Freiburg ( email )

Fahnenbergplatz
Freiburg, D-79085
Germany

Julian Sester

University of Freiburg

Freiburg, D-79085
Germany

Nanyang Technological University (NTU) - School of Physical and Mathematical Sciences ( email )

S3 B2-A28 Nanyang Avenue
Singapore, 639798
Singapore

Do you want regular updates from SSRN on Twitter?

Paper statistics

Downloads
118
Abstract Views
748
rank
317,572
PlumX Metrics