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Statistical Learning of Value‐at‐Risk and Expected Shortfall

David Barrera et al.

Mathematical Finance2025https://doi.org/10.1111/mafi.70000article
AJG 3ABDC A
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0.37

Abstract

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‐ toy model and a financial case study where the objective is to learn a dynamic initial margin.

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https://doi.org/https://doi.org/10.1111/mafi.70000

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@article{david2025,
  title        = {{Statistical Learning of Value‐at‐Risk and Expected Shortfall}},
  author       = {David Barrera et al.},
  journal      = {Mathematical Finance},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1111/mafi.70000},
}

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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