3 papers
cs.LG2026
Physics-Informed Neural Networks for Solving Derivative-Constrained PDEs
Kentaro Hoshisashi, Carolyn E Phelan, Paolo Barucca
Physics-Informed Neural Networks (PINNs) recast PDE solving as an optimisation problem in function space by minimising a residual-based objective, yet many applications require add…
q-fin.CP2024
Whack-a-mole Online Learning: Physics-Informed Neural Network for Intraday Implied Volatility Surface
Kentaro Hoshisashi, Carolyn E. Phelan, Paolo Barucca
Calibrating the time-dependent Implied Volatility Surface (IVS) using sparse market data is an essential challenge in computational finance, particularly for real-time applications…
q-fin.CP2023
No-Arbitrage Deep Calibration for Volatility Smile and Skewness
Kentaro Hoshisashi, Carolyn E. Phelan, Paolo Barucca
Volatility smile and skewness are two key properties of option prices that are represented by the implied volatility (IV) surface. However, IV surface calibration through nonlinear…