Deep Hedging: Continuous Reinforcement Learning for Hedging of General Portfolios across Multiple Risk Aversions
Computational Finance
2022-07-18 v1 Risk Management
Machine Learning
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.
Keywords
Cite
@article{arxiv.2207.07467,
title = {Deep Hedging: Continuous Reinforcement Learning for Hedging of General Portfolios across Multiple Risk Aversions},
author = {Phillip Murray and Ben Wood and Hans Buehler and Magnus Wiese and Mikko S. Pakkanen},
journal= {arXiv preprint arXiv:2207.07467},
year = {2022}
}