.png){kind=icon}
.png){kind=icon}
Return
Random Neural Networks for Rough Volatility
.png){kind=icon}
PRE
AI
J
Z
DOI：10.1007/s00245-026-10392-5.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
We construct a deep learning-based numerical algorithm to solve path-dependent partial differential equations arising in the context of rough volatility. Our approach is based on interpreting the PDE as a solution to an BSDE, building upon recent insights by Bayer, Qiu and Yao, and on constructing a neural network of reservoir type as originally developed by Gonon, Grigoryeva, Ortega. The reservoir approach allows us to formulate the optimisation problem as a simple least-square regression for which we prove theoretical convergence properties.
Keywords:
Rough volatility
SPDEs
Neural networks
Reservoir computing
Journal
A
IF:
1.7
Papers:
106
Citations:
0
Organization
Cited Papers