CorrGAN: Sampling Realistic Financial Correlation Matrices Using Generative Adversarial Networks

Gautier Marti

Papers from arXiv.org

Abstract: 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.

Date: 2019-10, Revised 2019-12
New Economics Papers: this item is included in nep-rmg
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Published in ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1910.09504

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