Learning out-of-sample Expected Shortfall and Conditional Tail Moments with neural networks. Application to cryptocurrency data - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Pré-Publication, Document De Travail Année : 2023

Learning out-of-sample Expected Shortfall and Conditional Tail Moments with neural networks. Application to cryptocurrency data

Résumé

We propose new parameterizations for neural networks in order to estimate out-of-sample Expected Shortfall, and even more generally, out-of-sample conditional tail moments, in heavy-tailed settings as functions of confidence levels. The proposed neural network estimator is able to extrapolate in the distribution tails thanks to an extension of the usual extreme-value second-order condition to an arbitrary order. The convergence rate of the uniform error between the log-conditional tail moment and its neural network approximation is established. The finite sample performance of the neural network estimator is compared to bias-reduced extreme-value competitors on simulated data. It is shown that our method outperforms them in difficult heavy-tailed situations where other estimators almost all fail. Finally, the neural network estimator is tested on real data to investigate the behavior of cryptocurrency extreme loss returns.
Fichier principal
Vignette du fichier
Learning_of_extreme_Expected_Shortfall_with_neural_networks-6.pdf (757.16 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04347859 , version 1 (15-12-2023)
hal-04347859 , version 2 (06-03-2024)
hal-04347859 , version 3 (14-03-2024)

Licence

Paternité

Identifiants

  • HAL Id : hal-04347859 , version 3

Citer

Michaël Allouche, Stéphane Girard, Emmanuel Gobet. Learning out-of-sample Expected Shortfall and Conditional Tail Moments with neural networks. Application to cryptocurrency data. 2023. ⟨hal-04347859v3⟩
110 Consultations
67 Téléchargements

Partager

Gmail Facebook X LinkedIn More