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

Phillip Murray, Ben Wood, Hans Buehler, Magnus Wiese and 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.

Date: 2022-07
New Economics Papers: this item is included in nep-cmp, nep-rmg and nep-upt
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