  Financial time series simulation is a central topic since it extends the
limited real data for training and evaluation of trading strategies. It is also
challenging because of the complex statistical properties of the real financial
data. We introduce two generative adversarial networks (GANs), which utilize
the convolutional networks with attention and the transformers, for financial
time series simulation. The GANs learn the statistical properties in a
data-driven manner and the attention mechanism helps to replicate the
long-range dependencies. The proposed GANs are tested on the S&P 500 index and
option data, examined by scores based on the stylized facts and are compared
with the pure convolutional GAN, i.e. QuantGAN. The attention-based GANs not
only reproduce the stylized facts, but also smooth the autocorrelation of
returns.

Statistical Finance;Machine Learning;Computational Finance