Time Series Simulation by Conditional Generative Adversarial Net

TL;DR

This paper introduces a Conditional Generative Adversarial Network (CGAN) approach for simulating and modeling time series data, capturing complex distributions and dependencies, with applications in financial risk management and economic forecasting.

Contribution

It presents a novel application of CGANs to time series data, demonstrating their ability to learn diverse distributions and dependencies, and showcases practical applications in risk analysis and economic modeling.

Findings

CGAN effectively learns various distributions and dependencies in time series.

CGAN outperforms traditional methods like Historic Simulation in VaR backtesting.

CGAN provides realistic scenario generation for risk and economic analysis.

Abstract

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.