33 citations · 37 across the 9 of their papers we have counts for
13 papers
Robust Control under Stationary Ambiguity
Konrad J. Mueller, Amira Akkari, Ben Wood +1
Control policies optimized in simulation can perform poorly in the real system when the parameters of the simulator are estimated from limited data but the resulting parameter…
Generating Financial Time Series by Matching Random Convolutional Features
Konrad J. Mueller, Nikita Zozoulenko, Ben Wood +2
Generating realistic financial time series is challenging as training data is often limited to a single historical path. With such scarce data, overfitting is hard to avoid, especi…
Fast Deep Hedging with Second-Order Optimization
Konrad Mueller, Amira Akkari, Lukas Gonon +1
Hedging exotic options in presence of market frictions is an important risk management task. Deep hedging can solve such hedging problems by training neural network policies in rea…
Quantum option pricing via the Karhunen-Loève expansion
Anupam Prakash, Yue Sun, Shouvanik Chakrabarti +8
We consider the problem of pricing discretely monitored Asian options over monitoring points where the underlying asset is modeled by a geometric Brownian motion. We provide tw…
Quantum Deep Hedging
El Amine Cherrat, Snehal Raj, Iordanis Kerenidis +12
Quantum machine learning has the potential for a transformative impact across industry sectors and in particular in finance. In our work we look at the problem of hedging where dee…
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…
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…
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…
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…
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…
A Data-driven Market Simulator for Small Data Environments
Hans Bühler, Blanka Horvath, Terry Lyons +2
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 dynam…
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…
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…