This paper considers the problem of pricing options with early-exercise features whose pay-off depends on several sources of uncertainty. We propose a stochastic grid method for estimating the optimal exercise policy and using this policy to obtain a low-biased estimator for high-dimensional...

This paper describes a practical simulation-based algorithm, which we call the <I>Stochastic Grid Bundling Method </I> (SGBM) for pricing multi-dimensional Bermudan (i.e. discretely exercisable) options. The method generates a direct estimator of the option price, an optimal early-exercise policy as...</i>

We present a semi-static replication algorithm for Bermudan swaptions under an affine, multi-factor term structure model. In contrast to dynamic replication, which needs to be continuously updated as the market moves, a semi-static replication needs to be rebalanced on just a finite number of...

This paper presents a computationally efficient technique for the computation of exposure distributions at any future time under the risk-neutral and some observed real-world probability measures, needed for computation of credit valuation adjustment (CVA) and potential future exposure (PFE). In...

We present here a regress later based Monte Carlo approach that uses neural networks for pricing high-dimensional contingent claims. The choice of specific architecture of the neural networks used in the proposed algorithm provides for interpretability of the model, a feature that is often...

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...