Quant GANs: deep generation of financial time series

Abstract

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.

Keywords:

• Financial modeling
• Generative adversarial networks
• Machine learning
• Risk neutral simulation
• Temporal convolutional networks
• Time series

JEL Classification:

• C45
• C63

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 Note that using a $1\times 1$ convolution is equivalent to applying an affine transformation along the time dimension of X.

2 The subscript θ of ${\tilde{X}}_{\theta }$ represents the dependency with respect to the neural operator's parameters θ.

3 Note that preprocessing and approximation of the Lambert W transform relevant parameters can be equivalently done by using the package pytorch.