Quant GANs: Deep Generation of Financial Time Series
This work addresses the problem of accurate financial time series modeling for researchers and practitioners in financial mathematics, representing an incremental advancement by adapting GANs to this domain.
The paper tackles the challenge of modeling financial time series by introducing Quant GANs, a data-driven generative adversarial network model that captures long-range dependencies like volatility clusters and allows transition to risk-neutral distributions, with numerical results showing excellent agreement in distributional properties and high fidelity in generating dependence features.
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.