In this paper, we will present a multiple time-step Monte Carlo simulation technique for pricing options under the (Stochastic Alpha Beta Rho (SABR)) model. The proposed method is an extension of the one time-step Monte Carlo method that we proposed in an accompanying paper, for pricing European...

In this work, a parallel GPU version of the Monte Carlo Stochastic Grid Bundling Method (SGBM) for pricing multi-dimensional early-exercise options is presented. To extend the method's applicability, the problem dimensions and the number of bundles will be increased drastically. This makes SGBM...

In this work, we propose a one time-step Monte Carlo method for the SABR model. We base our approach on an accurate approximation of the cumulative distribution function of the integrated variance (conditional on the SABR volatility process), using Fourier techniques and a copula. Resulting is a...

In this paper we extend the stochastic grid bundling method (SGBM), a regress-later based Monte Carlo scheme for pricing early-exercise options, with an adjoint method to compute in a highly efficient manner sensitivities along the paths, with reasonable accuracy. With the ISDA standard initial...

In this paper, we present the data-driven COS method, ddCOS. It is a Fourier-based financial option valuation method which assumes the availability of asset data samples: a characteristic function of the underlying asset probability density function is not required. As such, the method...