Deep Generative Modeling for Financial Time Series with Application in
  VaR: A Comparative Review
Lars Ericson Xuejun Zhu Xusi Han Rao Fu Shuang Li Steve Guo Ping Hu
In the financial services industry, forecasting the risk factor distribution
conditional on the history and the current market environment is the key to
market risk modeling in general and value at risk (VaR) model in particular. As
one of the most widely adopted VaR models in commercial banks, Historical
simulation (HS) uses the empirical distribution of daily returns in a
historical window as the forecast distribution of risk factor returns in the
next day. The objectives for financial time series generation are to generate
synthetic data paths with good variety, and similar distribution and dynamics
to the original historical data. In this paper, we apply multiple existing deep
generative methods (e.g., CGAN, CWGAN, Diffusion, and Signature WGAN) for
conditional time series generation, and propose and test two new methods for
conditional multi-step time series generation, namely Encoder-Decoder CGAN and
Conditional TimeVAE. Furthermore, we introduce a comprehensive framework with a
set of KPIs to measure the quality of the generated time series for financial
modeling. The KPIs cover distribution distance, autocorrelation and
backtesting. All models (HS, parametric and neural networks) are tested on both
historical USD yield curve data and additional data simulated from GARCH and
CIR processes. The study shows that top performing models are HS, GARCH and
CWGAN models. Future research directions in this area are also discussed.