Work

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

Year: 2022

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.

Source: ACM International Conference on AI in Finance (ICAIF)

DOI: 10.1145/3533271.3561731

Authors: Phillip Murray, Ben Wood, Hans Buehler, Magnus Wiese, Mikko Pakkanen

Institutions: Imperial College London, Technical University of Munich, University of Kaiserslautern

Language: English

Topic: Stochastic processes and financial applications

Subfield: Finance

Field: Economics, Econometrics and Finance

Domain: Social Sciences

Open Access status: green

OpenAlex ID: W4306962685

FWCI: 20.06

Cites: 14

Cited by: 20

Related to: 10