  Generative Adversarial Net (GAN) has been proven to be a powerful machine
learning tool in image data analysis and generation. In this paper, we propose
to use Conditional Generative Adversarial Net (CGAN) to learn and simulate time
series data. The conditions can be both categorical and continuous variables
containing different kinds of auxiliary information. Our simulation studies
show that CGAN is able to learn different kinds of normal and heavy tail
distributions, as well as dependent structures of different time series and it
can further generate conditional predictive distributions consistent with the
training data distributions. We also provide an in-depth discussion on the
rationale of GAN and the neural network as hierarchical splines to draw a clear
connection with the existing statistical method for distribution generation. In
practice, CGAN has a wide range of applications in the market risk and
counterparty risk analysis: it can be applied to learn the historical data and
generate scenarios for the calculation of Value-at-Risk (VaR) and Expected
Shortfall (ES) and predict the movement of the market risk factors. We present
a real data analysis including a backtesting to demonstrate CGAN is able to
outperform the Historic Simulation, a popular method in market risk analysis
for the calculation of VaR. CGAN can also be applied in the economic time
series modeling and forecasting, and an example of hypothetical shock analysis
for economic models and the generation of potential CCAR scenarios by CGAN is
given at the end of the paper.
