Heat kernel expansion for a family of stochastic volatility models : delta-geometry
Bourgade Paul, Croissant Olivier
Abstract
In this paper, we study a family of stochastic volatility processes; this family features a mean reversion term for the volatility and a double CEV-like exponent that generalizes SABR and Heston's models. We derive approximated closed form formulas for the digital prices, the local and implied volatilities. Our formulas are efficient for small maturities. Our method is based on differential geometry, especially small time diffusions on riemanian spaces. This geometrical point of view can be extended to other processes, and is very accurate to produce variate smiles for small maturities and small moneyness.
Create a lesson
Related papers
Report of the 2026 Workshop on Next-Generation Ecosystems for Scientific Computing: Harnessing Community, Software, and AI for Cross-Disciplinary Team Science
Lois Curfman McInnes, Dorian Arnold, Prasanna Balaprakash et al.
Efficient tensor bases for pairwise comparisons
Konrad Kułakowski, Ryszard Smarzewski
Unlocking Multimodal Protein Language Models at Inference Time
Yi Zhou, Qipeng Wang, Yunqing Liu et al.
Forecasting Global Volatility Across Asynchronous Markets: Incremental Accuracy from Constrained Cross-Market Attention
Xinlin Zhao, Haotian Qiao, Ziyao Lin
RWA-PoB: A Credential-Based Proof-of-Backing Framework for Tokenized U.S. Treasury Products
Rischan Mafrur, Gun Gun Febrianza, Sean Foley
FABRICA: Agentic CUDA-to-CSL Translation and Optimization for Wafer-Scale Systems
Yuebo Luo, Eliu Huerta, Venkatram Vishwanath et al.