Improving portfolios global performance using a cleaned and robust covariance matrix estimate

Abstract : This paper presents how the most recent improvements made on covariance matrix estimation and model order selection can be applied to the portfolio optimization problem. The particular case of the Maximum Variety Portfolio is treated but the same improvements apply also in the other optimization problems such as the Minimum Variance Portfolio. We assume that the most important information (or the latent factors) are embedded in correlated Elliptical Symmetric noise extending classical Gaussian assumptions. We propose here to focus on a recent method of model order selection allowing to efficiently estimate the subspace of main factors describing the market. This non-standard model order selection problem is solved through Random Matrix Theory and robust covariance matrix estimation. Moreover we extend the method to non-homogeneous assets returns. The proposed procedure will be explained through synthetic data and be applied and compared with standard techniques on real market data showing promising improvements.
Complete list of metadatas

Cited literature [59 references]  Display  Hide  Download

https://halshs.archives-ouvertes.fr/halshs-02354596
Contributor : Lucie Label <>
Submitted on : Thursday, November 7, 2019 - 6:56:43 PM
Last modification on : Friday, November 29, 2019 - 4:13:46 PM

File

19022.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : halshs-02354596, version 1

Citation

Emmanuelle Jay, Thibault Soler, Eugénie Terreaux, Jean-Philippe Ovarlez, Frédéric Pascal, et al.. Improving portfolios global performance using a cleaned and robust covariance matrix estimate. 2019. ⟨halshs-02354596⟩

Share

Metrics

Record views

53

Files downloads

59