Adding a Conditional Value-at-Risk tail penalty to a Lipschitz-regularized divergence creates a bounded non-Lipschitz particle flow that improves heavy-tail accuracy of pre-trained generative models.
Expected Shortfall as a Tool for Financial Risk Management
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abstract
We study the properties of Expected Shortfall from the point of view of financial risk management. This measure --- which emerges as a natural remedy in some cases where Value at Risk (VaR) is not able to distinguish portfolios which bear different levels of risk --- is indeed shown to have much better properties than VaR. We show in fact that unlike VaR this variable is in general subadditive and therefore it is a Coherent Measure of Risk in the sense of reference (artzner)
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stat.ML 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
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Fine-Tuning Generative Models for Extreme Events via CVaR-Penalized Wasserstein Gradient Flows
Adding a Conditional Value-at-Risk tail penalty to a Lipschitz-regularized divergence creates a bounded non-Lipschitz particle flow that improves heavy-tail accuracy of pre-trained generative models.