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

  title={cCorrGAN: Conditional Correlation GAN for Learning Empirical Conditional Distributions in the Elliptope},
  author={Gautier Marti and Victor Goubet and Frank Nielsen},
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. 

Theoretically and computationally convenient geometries on full-rank correlation matrices

In contrast to SPD matrices, few tools exist to perform Riemannian statistics on the open elliptope of full-rank correlation matrices. The quotient-affine metric was recently built as the quotient of



CORRGAN: Sampling Realistic Financial Correlation Matrices Using Generative Adversarial Networks

  • G. Marti
  • Computer Science
    ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • 2020
Experiments demonstrate that generative adversarial networks are able to recover most of the known stylized facts about empirical correlation matrices estimated on asset returns for the first time.

Quant GANs: deep generation of financial time series

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Generative adversarial networks for financial trading strategies fine-tuning and combination

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Time Series Simulation by Conditional Generative Adversarial Net

This paper proposes to use Conditional Generative Adversarial Net (CGAN) to learn and simulate time series data and provides an in-depth discussion on the rationale behind GAN and the neural networks as hierarchical splines to establish a clear connection with existing statistical methods of distribution generation.

Modeling financial time-series with generative adversarial networks

Building Diversified Portfolios that Outperform Out of Sample

The Hierarchical Risk Parity approach is introduced to address three major concerns of quadratic optimizers, in general, and Markowitz’s critical line algorithm (CLA), in particular: instability, concentration, and underperformance.

Riemannian batch normalization for SPD neural networks

A Riemannian batch normalization (batch- norm) algorithm is introduced, which generalizes the one used in Euclidean nets and derives a new manifold-constrained gradient descent algorithm working in the space of SPD matrices, allowing to learn the batchnorm layer.

Simulating realistic correlation matrices for financial applications: correlation matrices with the Perron–Frobenius property

A simulation algorithm for random correlation matrices satisfying the Perron-Frobenius property is presented, which can be augmented to take into account a realistic eigenvalue structure.

A Riemannian Network for SPD Matrix Learning

A Riemannian network architecture is built to open up a new direction of SPD matrix non-linear learning in a deep model and it is shown that the proposed SPD matrix network can be simply trained and outperform existing SPD matrix learning and state-of-the-art methods in three typical visual classification tasks.