Quantum Deep Hedging

no code implementations • 29 Mar 2023 • El Amine Cherrat, Snehal Raj, Iordanis Kerenidis, Abhishek Shekhar, Ben Wood, Jon Dee, Shouvanik Chakrabarti, Richard Chen, Dylan Herman, Shaohan Hu, Pierre Minssen, Ruslan Shaydulin, Yue Sun, Romina Yalovetzky, Marco Pistoia

Quantum machine learning has the potential for a transformative impact across industry sectors and in particular in finance.

Quantum Machine Learning reinforcement-learning

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

no code implementations • 15 Jul 2022 • Phillip Murray, Ben Wood, Hans Buehler, Magnus Wiese, Mikko S. Pakkanen

We present a method for finding optimal hedging policies for arbitrary initial portfolios and market states.

Reinforcement Learning (RL)

Deep Bellman Hedging

no code implementations • 3 Jul 2022 • 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 derivatives using purely historic data.

reinforcement-learning Reinforcement Learning (RL)

Multi-Asset Spot and Option Market Simulation

no code implementations • 13 Dec 2021 • Magnus Wiese, Ben Wood, Alexandre Pachoud, Ralf Korn, Hans Buehler, Phillip Murray, Lianjun Bai

We construct realistic spot and equity option market simulators for a single underlying on the basis of normalizing flows.

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

no code implementations • 15 Nov 2021 • Hans Buehler, Phillip Murray, Mikko S. Pakkanen, Ben Wood

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 on the same underlying.

Deep Hedging: Learning Risk-Neutral Implied Volatility Dynamics

no code implementations • 22 Mar 2021 • Hans Buehler, Phillip Murray, Mikko S. Pakkanen, Ben Wood

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 and convex trading constraints.

A Data-driven Market Simulator for Small Data Environments

no code implementations • 21 Jun 2020 • Hans Bühler, Blanka Horvath, Terry Lyons, Imanol Perez Arribas, Ben Wood

Neural network based data-driven market simulation unveils a new and flexible way of modelling financial time series without imposing assumptions on the underlying stochastic dynamics.

Time Series Time Series Analysis

Deep Hedging: Learning to Simulate Equity Option Markets

1 code implementation • 5 Nov 2019 • Magnus Wiese, Lianjun Bai, Ben Wood, Hans Buehler

We construct realistic equity option market simulators based on generative adversarial networks (GANs).

Time Series Time Series Analysis

Deep Hedging

3 code implementations • 8 Feb 2018 • Hans Bühler, Lukas Gonon, Josef Teichmann, Ben Wood

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 using modern deep reinforcement machine learning methods.

Computational Finance Numerical Analysis Optimization and Control Probability Risk Management 91G60, 65K99