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Copulas and deep learning: a review

Author

Listed:
  • Coblenz Maximilian

    (Department of Services and Consulting, Ludwigshafen University of Business and Society, Ernst-Boehe-Str. 4, 67059 Ludwigshafen, Germany)

  • Grothe Oliver

    (Institute of Operations Research, Analytics and Statistics, Karlsruhe Institute of Technology, Kaiserstr. 12, 76131 Karlsruhe, Germany)

  • Liu Bolin

    (Institute of Operations Research, Analytics and Statistics, Karlsruhe Institute of Technology, Kaiserstr. 12, 76131 Karlsruhe, Germany)

  • Weniger David

    (Department of Services and Consulting, Ludwigshafen University of Business and Society, Ernst-Boehe-Str. 4, 67059 Ludwigshafen, Germany)

Abstract

In the last two decades, there has been a surge in the research on neural networks, and particularly on deep learning. At the same time, copulas as a statistical modeling tool for multivariate distributions became more and more popular. We survey how copulas are used in neural networks and deep learning and vice versa how neural networks and deep learning are used in the copula domain. For example, we highlight that copulas can be constructed from generative deep learning models and that neural networks can help in goodness-of-fit assessment of copulas. Moreover, we discuss how copulas amplify specific deep learning models and how copulas are harnessed to combine neural network outputs.

Suggested Citation

  • Coblenz Maximilian & Grothe Oliver & Liu Bolin & Weniger David, 2026. "Copulas and deep learning: a review," Dependence Modeling, De Gruyter, vol. 14(1), pages 1-27.
    • Handle: RePEc:vrs:demode:v:14:y:2026:i:1:p:27:n:1001
    DOI: 10.1515/demo-2025-0017
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