CorrGAN: Sampling Realistic Financial Correlation Matrices Using Generative Adversarial Networks

21 October 2019

Papers citing "CorrGAN: Sampling Realistic Financial Correlation Matrices Using Generative Adversarial Networks"

7 / 7 papers shown

Title
A Note on Statistically Accurate Tabular Data Generation Using Large Language Models Andrey Sidorenko 41 0 0 05 May 2025
Universal randomised signatures for generative time series modelling Francesca Biagini Lukas Gonon Niklas Walter 42 4 0 14 Jun 2024
Improved Data Generation for Enhanced Asset Allocation: A Synthetic Dataset Approach for the Fixed Income Universe Szymon Kubiak Tillman Weyde Oleksandr Galkin Daniel Philps Ram Gopal 24 1 0 27 Nov 2023
Scenario generation for market risk models using generative neural networks Solveig Flaig Gero Junike GAN 24 10 0 21 Sep 2021
High Fidelity Speech Synthesis with Adversarial Networks Mikolaj Binkowski Jeff Donahue Sander Dieleman Aidan Clark Erich Elsen Norman Casagrande Luis C. Cobo Karen Simonyan 223 239 0 25 Sep 2019
Cleaning large correlation matrices: tools from random matrix theory J. Bun J. Bouchaud M. Potters 32 262 0 25 Oct 2016
Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling Jiajun Wu Chengkai Zhang Tianfan Xue Bill Freeman J. Tenenbaum GAN 171 1,940 0 24 Oct 2016