Deep Hedging: Continuous Reinforcement Learning for Hedging of General Portfolios across Multiple Risk Aversions

2022/07/15 by Phillip Murray, Ben Wood, Murray, Phillip +7 · 1 voice · 4 citations
Decision Sciences · Economics, Econometrics and Finance · Mathematics · #Computational Finance (q-fin.CP) #FOS: Computer and information sciences #FOS: Economics and business #Financial Markets and Investment Strategies #Machine Learning (stat.ML) #Risk Management (q-fin.RM) #Risk and Portfolio Optimization #Stochastic processes and financial applications #q-fin.CP #q-fin.RM #stat.ML

paper · pdf · doi:10.48550/arxiv.2207.07467

openalex publication_date 2022/07/15 · arxiv published 2022/07/15 · arxiv updated 2022/07/15 · openalex created_date 2022/07/19 · openalex updated_date 2026/07/28

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.

Cited by

• Robust Hedging Valuation Adjustment for Deep Hedging Policies under Market Frictions
• The Formalism-Implementation Gap in Reinforcement Learning Research
• Application of Deep Reinforcement Learning to At-the-Money S&P 500 Options Hedging
• Uncertainty-Aware Strategies: A Model-Agnostic Framework for Robust Financial Optimization through Subsampling
• Risk-Averse Reinforcement Learning with Itakura-Saito Loss

Discussions

• This is in implemented in production at JPMorgan arxiv.org/abs/2207.07467 [bsky, 4 points, 0 comments]

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