  We propose a novel approach for sampling realistic financial correlation
matrices. This approach is based on generative adversarial networks.
Experiments demonstrate that generative adversarial networks are able to
recover most of the known stylized facts about empirical correlation matrices
estimated on asset returns. This is the first time such results are documented
in the literature. Practical financial applications range from trading
strategies enhancement to risk and portfolio stress testing. Such generative
models can also help ground empirical finance deeper into science by allowing
for falsifiability of statements and more objective comparison of empirical
methods.
