arXiv (All)
open access
Universal Diffusion Models for Implied Volatility Surfaces: Learning Shared Dynamics Across Stocks
Modeling the dynamics of option implied volatility surface (IVS) is crucial for pricing, hedging, and risk-managing option portfolios. We develop a universal conditional diffusion model that learns to jointly generate next-day IVS increments and the underlying stock's returns. The model is trained o
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
- Calibrating the Heston model with deep differential networks#1011973
- Hierarchical Multi-Task Learning with Liquidity-Aware Signals for Stock Forecasting#1061254
- Explainable Deep Learning for Price-Trade Dynamics: From Black-Box Forecasts to Effective Parametric Models#973692
- CARVE: Verified Expansion for Variable-Length Generation in Diffusion Language Models#818956
- CARVE: Verified Expansion for Variable-Length Generation in Diffusion Language Models#808964
Record · ID 1037576
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