10 citations · 10 across the 3 of their papers we have counts for
5 papers
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…
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…