AI Chat AI Image Generator AI Video Text to Speech

cCorrGAN: Conditional Correlation GAN for Learning Empirical Conditional Distributions in the Elliptope

07/22/2021

∙

by Gautier Marti, et al.

∙

∙

We propose a methodology to approximate conditional distributions in the elliptope of correlation matrices based on conditional generative adversarial networks. We illustrate the methodology with an application from quantitative finance: Monte Carlo simulations of correlated returns to compare risk-based portfolio construction methods. Finally, we discuss about current limitations and advocate for further exploration of the elliptope geometry to improve results.

Gautier Marti
9 publications
Victor Goubet
1 publication
Frank Nielsen
81 publications

research

∙ 10/21/2019

CorrGAN: Sampling Realistic Financial Correlation Matrices Using Generative Adversarial Networks

We propose a novel approach for sampling realistic financial correlation...

0 Gautier Marti, et al. ∙

research

∙ 08/16/2018

Improving Conditional Sequence Generative Adversarial Networks by Stepwise Evaluation

Sequence generative adversarial networks (SeqGAN) have been used to impr...

0 Yi-Lin Tuan, et al. ∙

research

∙ 11/29/2021

Generative Adversarial Networks with Conditional Neural Movement Primitives for An Interactive Generative Drawing Tool

Sketches are abstract representations of visual perception and visuospat...

1 Suzan Ece Ada, et al. ∙

research

∙ 07/31/2019

Generative Adversarial Networks (GAN) for compact beam source modelling in Monte Carlo simulations

A method is proposed and evaluated to model large and inconvenient phase...

0 David Sarrut, et al. ∙

research

∙ 09/05/2019

Using Wasserstein Generative Adversarial Networks for the Design of Monte Carlo Simulations

Researchers often use artificial data to assess the performance of new e...

0 Susan Athey, et al. ∙

research

∙ 04/19/2023

Conditional Generative Models for Learning Stochastic Processes

A framework to learn a multi-modal distribution is proposed, denoted as ...

0 Salvatore Certo, et al. ∙

research

∙ 04/03/2018

Correlated discrete data generation using adversarial training

Generative Adversarial Networks (GAN) have shown great promise in tasks ...

0 Shreyas Patel, et al. ∙