  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 data-driven model which is inspired by the recent
success of generative adversarial networks (GANs). Quant GANs consist of a
generator and discriminator function, which utilize temporal convolutional
networks (TCNs) and thereby achieve to capture long-range dependencies such as
the presence of volatility clusters. The generator function is explicitly
constructed such that the induced stochastic process allows a transition to its
risk-neutral distribution. Our numerical results highlight that distributional
properties for small and large lags are in an excellent agreement and
dependence properties such as volatility clusters, leverage effects, and serial
autocorrelations can be generated by the generator function of Quant GANs,
demonstrably in high fidelity.
