Nonparametric sign prediction of high-dimensional correlation matrix coefficients - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue EPL - Europhysics Letters Année : 2021

Nonparametric sign prediction of high-dimensional correlation matrix coefficients

Résumé

We introduce a method to predict which correlation matrix coefficients are likely to change their signs in the future in the high-dimensional regime, i.e. when the number of features is larger than the number of samples per feature. The stability of correlation signs, two-by-two relationships, is found to depend on three-by-three relationships inspired by Heider social cohesion theory in this regime. We apply our method to US and Hong Kong equities historical data to illustrate how the structure of correlation matrices influences the stability of the sign of its coefficients .
Fichier principal
Vignette du fichier
HAL.pdf (1.53 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-02335586 , version 1 (28-10-2019)

Identifiants

Citer

Christian Bongiorno, Damien Challet. Nonparametric sign prediction of high-dimensional correlation matrix coefficients. EPL - Europhysics Letters, 2021, 133 (4), pp.48001. ⟨10.1209/0295-5075/133/48001⟩. ⟨hal-02335586⟩
101 Consultations
117 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More