Deep Hedging under Rough Volatility

25 Pages Posted: 18 Feb 2021

See all articles by Blanka Horvath

Blanka Horvath

ETH Zürich - Department of Mathematics

Josef Teichmann

ETH Zurich; Swiss Finance Institute

Zan Zuric

Imperial College London - Department of Mathematics

Date Written: February 2, 2021

Abstract

We investigate the performance of the Deep Hedging framework under training paths beyond the (finite dimensional) Markovian setup. In particular we analyse the hedging performance of the original architecture under rough volatility models with view to existing theoretical results for those. Furthermore, we suggest parsimonious but suitable network architectures capable of capturing the non-Markoviantity of time-series. Secondly, we analyse the hedging behaviour in these models in terms of P&L distributions and draw comparisons to jump diffusion models if the the rebalancing frequency is realistically small.

Keywords: Imperfect Hedging, Derivatives Pricing, Derivatives Hedging, Deep Learning, Rough Volatility

JEL Classification: C61, C58, C45, G32

Suggested Citation

Horvath, Blanka and Teichmann, Josef and Zuric, Zan, Deep Hedging under Rough Volatility (February 2, 2021). Available at SSRN: https://ssrn.com/abstract=3778043 or http://dx.doi.org/10.2139/ssrn.3778043

Blanka Horvath (Contact Author)

ETH Zürich - Department of Mathematics ( email )

R¨amistrasse 101
Raemistr. 101
Z¨urich, 8092
Switzerland

Josef Teichmann

ETH Zurich ( email )

Rämistrasse 101
ZUE F7
Zürich, 8092
Switzerland

HOME PAGE: http://www.math.ethz.ch/~jteichma

Swiss Finance Institute ( email )

c/o University of Geneva
40 Bd du Pont-d'Arve
CH-1211 Geneva 4
Switzerland

Zan Zuric

Imperial College London - Department of Mathematics ( email )

South Kensington Campus
Imperial College
LONDON, SW7 2AZ
United Kingdom

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
766
Abstract Views
1,958
rank
40,819
PlumX Metrics