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arXiv · 2207.07467

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

Phillip Murray·Ben Wood·Hans Buehler·Magnus Wiese·Mikko S. Pakkanen
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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.

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Phillip Murray, Ben Wood, Hans Buehler, Magnus Wiese, Mikko S. Pakkanen. 2022-07-15. Deep Hedging: Continuous Reinforcement Learning for Hedging of General Portfolios across Multiple Risk Aversions. https://arxiv.org/abs/2207.07467

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