Pith. sign in

REVIEW 1 cited by

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

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2207.07467 v1 pith:KY7DYU26 submitted 2022-07-15 q-fin.CP q-fin.RMstat.ML

Phillip Murray , Ben Wood , Hans Buehler , Magnus Wiese , Mikko S. Pakkanen This is my paper · ORCID
classification q-fin.CPq-fin.RMstat.ML
keywords hedgingacrossgeneralmultipleportfoliosriskstochasticactor-critic
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original 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.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Robust Hedging Valuation Adjustment for Deep Hedging Policies under Market Frictions

    q-fin.RM 2026-07 conditional novelty 6.0 of 10

    A common-stress reserve framework for deep hedgers shows classical trading bands usually beat learned policies, with sparse learned execution winning only under a strict low-liquidity budget.

Pith tools