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20182026
most citedQuantum Deep Hedging

33 citations · 37 across the 9 of their papers we have counts for

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7 papers · 1 filter

q-fin.CP2022★ 3 cited

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

Phillip Murray, Ben Wood, Hans Buehler +2

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-aver…

q-fin.CP2022

Deep Bellman Hedging

Hans Buehler, Phillip Murray, Ben Wood

We present an actor-critic-type reinforcement learning algorithm for solving the problem of hedging a portfolio of financial instruments such as securities and over-the-counter der…

q-fin.CP2021

Multi-Asset Spot and Option Market Simulation

Magnus Wiese, Ben Wood, Alexandre Pachoud +4

We construct realistic spot and equity option market simulators for a single underlying on the basis of normalizing flows. We address the high-dimensionality of market observed cal…

q-fin.CP2021★ 1 cited

Deep Hedging: Learning to Remove the Drift under Trading Frictions with Minimal Equivalent Near-Martingale Measures

Hans Buehler, Phillip Murray, Mikko S. Pakkanen +1

We present a machine learning approach for finding minimal equivalent martingale measures for markets simulators of tradable instruments, e.g. for a spot price and options written…

q-fin.CP2021

Deep Hedging: Learning Risk-Neutral Implied Volatility Dynamics

Hans Buehler, Phillip Murray, Mikko S. Pakkanen +1

We present a numerically efficient approach for learning a risk-neutral measure for paths of simulated spot and option prices up to a finite horizon under convex transaction costs…

q-fin.CP2019

Deep Hedging: Learning to Simulate Equity Option Markets

Magnus Wiese, Lianjun Bai, Ben Wood +1

We construct realistic equity option market simulators based on generative adversarial networks (GANs). We consider recurrent and temporal convolutional architectures, and assess t…

q-fin.CP2018

Deep Hedging

Hans Bühler, Lukas Gonon, Josef Teichmann +1

We present a framework for hedging a portfolio of derivatives in the presence of market frictions such as transaction costs, market impact, liquidity constraints or risk limits usi…