Deep Hedging under Rough Volatility
Computational Finance
2021-02-04 v1 Computational Engineering, Finance, and Science
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.
Cite
@article{arxiv.2102.01962,
title = {Deep Hedging under Rough Volatility},
author = {Blanka Horvath and Josef Teichmann and Zan Zuric},
journal= {arXiv preprint arXiv:2102.01962},
year = {2021}
}