In this paper, the authors propose and investigate a new method for the calibration to American option price data.
El Euch, Rosenbaum, Gatheral combine a rough volatility model with the classical Heston model
In this paper, the author describes a simple adaptive Filon method that performs better and more accurately than various popular alternatives for pricing options under the Heston model.
BAML quant proposes option pricing model that softens conflict between the two properties
New approach delivers quick and accurate computation of prices
In this paper, the authors develop a procedure to reduce the variance when numerically computing the Greeks obtained via Malliavin calculus for jump–diffusion models with stochastic volatility.
Hybrid finite-difference/pseudospectral methods for the Heston and Heston–Hull–White partial differential equations
In this paper, the authors propose a hybrid spatial finite-difference/pseudospectral discretization for European option-pricing problems under the Heston and Heston–Hull–White models.
Fiorin, Callegaro and Grasselli show how discretisation methods reduce computing time in high-dimensional problems
Marco de Innocentis and Sergei Levendorskiĭ describe a faster and more accurate method for market-implied calibration of the Heston model
Serguei Mechkov initialises Heston model’s parameters using probability distributions
A reduced basis method for parabolic partial differential equations with parameter functions and application to option pricing
The authors introduce an RB space–time variational approach for parametric PPDEs with coefficient parameters and a variable initial condition.
Stochastic volatility model combining Heston vol model and CIR++