Lévy-Flow Models: Heavy-Tail-Aware Normalizing Flows for Financial Risk Management
We introduce Lévy-Flows, a class of normalizing flow models that replace the standard Gaussian base distribution with Lévy process-based distributions, specifically Variance Gamma (VG) and Normal-Inverse Gaussian (NIG). These distributions naturally capture heavy-tailed behavior while preserving exact likelihood evaluation and efficient reparameterized sampling. We establish theoretical guarantees on tail behavior, showing that for regularly varying bases the tail index is preserved under asymptotically linear flow transformations, and that identity-tail Neural Spline Flow architectures preserve the base distribution's tail shape exactly outside the transformation region. Empirically, we ...
| Authors | Rachid Drissi |
|---|---|
| Primary category | cs.LG |
| Published | 31 March 2026 |
| Last updated at source | 31 March 2026 |
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