267 citations · 281 across the 8 of their papers we have counts for
10 papers
GROMM: Generative Risk-neutral Option Market Models
Magnus Wiese, Hans Buehler, Blanka Horvath
We present a statically and dynamically arbitrage-free European option market model with smooth interpolation between option prices, based on the SANOS framework, and prove that it…
Signature Trading: A Path-Dependent Extension of the Mean-Variance Framework with Exogenous Signals
Owen Futter, Blanka Horvath, Magnus Wiese
In this article we introduce a portfolio optimisation framework, in which the use of rough path signatures (Lyons, 1998) provides a novel method of incorporating path-dependencies…
Sig-Splines: universal approximation and convex calibration of time series generative models
Magnus Wiese, Phillip Murray, Ralf Korn
We propose a novel generative model for multivariate discrete-time time series data. Drawing inspiration from the construction of neural spline flows, our algorithm incorporates li…
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…
Risk-Neutral Market Simulation
Magnus Wiese, Phillip Murray
We develop a risk-neutral spot and equity option market simulator for a single underlying, under which the joint market process is a martingale. We leverage an efficient low-dimens…
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…
Sig-Wasserstein GANs for Time Series Generation
Hao Ni, Lukasz Szpruch, Marc Sabate-Vidales +3
Synthetic data is an emerging technology that can significantly accelerate the development and deployment of AI machine learning pipelines. In this work, we develop high-fidelity t…
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…
Copula & Marginal Flows: Disentangling the Marginal from its Joint
Magnus Wiese, Robert Knobloch, Ralf Korn
Deep generative networks such as GANs and normalizing flows flourish in the context of high-dimensional tasks such as image generation. However, so far exact modeling or extrapolat…
Quant GANs: Deep Generation of Financial Time Series
Magnus Wiese, Robert Knobloch, Ralf Korn +1
Modeling financial time series by stochastic processes is a challenging task and a central area of research in financial mathematics. As an alternative, we introduce Quant GANs, a…