Synthetic Data: A New Regulatory Tool

Machine learning tools have been developed to generate synthetic data sets that are indistinguishable from available historical data. In this paper, we investigate whether the tools can be used for stress testing. In particular we test whether synthetic data can be used to provide reliable risk measures when the confidence levels are high. Our results are encouraging and suggest that synthetic data produced from the most recent 250 days of historical data are potentially useful for determining regulatory market risk capital requirements.