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CorrGAN: Sampling Realistic Financial Correlation Matrices Using Generative Adversarial Networks

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  • Gautier Marti

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.

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  • Gautier Marti, 2019. "CorrGAN: Sampling Realistic Financial Correlation Matrices Using Generative Adversarial Networks," Papers 1910.09504, arXiv.org, revised Dec 2019.
    • Handle: RePEc:arx:papers:1910.09504
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    References listed on IDEAS

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    1. R. Mantegna, 1999. "Hierarchical structure in financial markets," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 11(1), pages 193-197, September.
    2. Adriano Koshiyama & Nick Firoozye & Philip Treleaven, 2021. "Generative adversarial networks for financial trading strategies fine-tuning and combination," Quantitative Finance, Taylor & Francis Journals, vol. 21(5), pages 797-813, May.
    3. M. Tumminello & F. Lillo & R. N. Mantegna, 2007. "Shrinkage and spectral filtering of correlation matrices: a comparison via the Kullback-Leibler distance," Papers 0710.0576, arXiv.org.
    4. Joël Bun & Jean-Philippe Bouchaud & Marc Potters, 2017. "Cleaning large correlation matrices: tools from random matrix theory," Post-Print hal-01491304, HAL.
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