Deep hedging: continuous reinforcement learning for hedging of general portfolios across multiple risk aversions
File(s)deep_bellman_hedging.pdf (764.11 KB)
Accepted version
Author(s)
Murray, Phillip
Wood, Ben
Buehler, Hans
Wiese, Magnus
Pakkanen, Mikko S
Type
Conference Paper
Abstract
We present a method for finding optimal hedging policies for arbitrary initial portfolios and market states. We develop a novel actor-critic algorithm for solving general risk-averse stochastic control problems and use it to learn hedging strategies across multiple risk aversion levels simultaneously. We
demonstrate the effectiveness of the approach with a numerical example in a stochastic volatility environment.
demonstrate the effectiveness of the approach with a numerical example in a stochastic volatility environment.
Date Issued
2022-10-26
Date Acceptance
2022-09-01
Citation
2022, pp.361-368
Publisher
ACM
Start Page
361
End Page
368
Copyright Statement
© 2022 Association for Computing Machinery. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in https://doi.org/10.1145/3533271.3561731
Identifier
https://dl.acm.org/doi/abs/10.1145/3533271.3561731
Source
3rd ACM International Conference on AI in Finance (ICAIF ’22)
Publication Status
Published
Start Date
2022-11-02
Finish Date
2022-11-04
Coverage Spatial
New York, NY, USA
Date Publish Online
2022-10-26