The authors employ Euler-type methods to study the L¹ approximation of the log-Heston stochastic differential equation at equidistant time points.
New simulation scheme clears the way for broader application of the rough Heston model
Andersen's quadratic-exponential scheme is used for simulations of rough volatility models
Barclays quants extend Bergomi’s skew stickiness ratio to all strikes
This paper looks at ways of solving (decoupled) forward–backward stochastic differential equations numerically using regression trees.
Bergomi's skew-stickiness ratio is extended to the setting of variance swaps
Expansion method for pricing foreign exchange options under stochastic volatility and interest rates
This paper applies the smart expansion method to the Heston–Hull–White model, which admits stochastic interest rates to enhance the model, and obtains the expansion formula for pricing options in the model up to second order.
An algorithm for the market-making of options on different underlyings is proposed
This work presents an efficient computational framework for pricing a general class of exotic and vanilla options under a versatile stochastic volatility model.
In this paper, the authors discuss all aspects of derivative pricing under the Heston–CLV model: calibration with an efficient Fourier method; a Monte Carlo simulation with second-order convergence; and accurate partial differential equation pricing…
Quality of replicating portfolio is used to measure performance of a model
A combination of rough volatility and price-feedback effect allows for SPX-Vix joint calibration
This paper develops two local mesh-free methods for designing stencil weights and spatial discretization, respectively, for parabolic partial differential equations (PDEs) of convection–diffusion–reaction type.
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