Quant GANs: Deep Generation of Financial Time Series

Understand

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.