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Corrgan: Sampling Realistic Financial Correlation Matrices Using Generative Adversarial Networks

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IEEE ICASSP 2020 Virtual Conference May 2020

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

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Corrgan: Sampling Realistic Financial Correlation Matrices Using Generative Adversarial Networks

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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