Statistical Learning of Value-at-Risk and Expected Shortfall - Centre de mathématiques appliquées (CMAP)
Pré-Publication, Document De Travail Année : 2024

Statistical Learning of Value-at-Risk and Expected Shortfall

Résumé

We propose a non-asymptotic convergence analysis of a two-step approach to learn a conditional value-at-risk (VaR) and a conditional expected shortfall (ES) using Rademacher bounds, in a non-parametric setup allowing for heavy-tails on the financial loss. Our approach for the VaR is extended to the problem of learning at once multiple VaRs corresponding to different quantile levels. This results in efficient learning schemes based on neural network quantile and least-squares regressions. An a posteriori Monte Carlo procedure is introduced to estimate distances to the ground-truth VaR and ES. This is illustrated by numerical experiments in a Student-$t$ toy model and a financial case study where the objective is to learn a dynamic initial margin.
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Dates et versions

hal-03775901 , version 1 (13-09-2022)
hal-03775901 , version 2 (13-08-2024)
hal-03775901 , version 3 (03-09-2024)
hal-03775901 , version 4 (18-09-2024)

Identifiants

Citer

D Barrera, S Crépey, E Gobet, Hoang-Dung Nguyen, B Saadeddine. Statistical Learning of Value-at-Risk and Expected Shortfall. 2024. ⟨hal-03775901v4⟩
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