10 citations · 14 across the 7 of their papers we have counts for
Showing 2019Show all
3 papers · 1 filter
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…
cs.LG2019★ 10 cited
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…
q-fin.MF2019
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…