arXiv (All)
open access
Financial Tail Risk Beyond Lipschitz Continuity via Semi-Discrete Optimal Transport
Financial returns are heavy-tailed, and accurate tail risk estimation is central to portfolio risk management. Modern neural generators sample by pushing a simple base distribution through a learned map, and for training stability that map is built from Lipschitz components. This is the binding cons
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Entropic Value-at-Risk parity for tempered stable returns#999583
- Path-Space Robust Bayesian Portfolio Selection#1155242
- Forecasting Liquidity Withdraw with Machine Learning Models#1069678
- Sharp Minimax Regret for Infinite-Memory Logistic Prediction#985234
- On tail-robust autocovariance matrix estimation for high-dimensional and potentially nonstationary time series#927708
Record · ID 1064329
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive