We study the use of sparse grids methods for the scenario generation (or discretization) problem in stochastic programming problems where the uncertainty is modeled using a continuous multivariate distribution. We show that, under a regularity assumption on the random function, the sequence of optimal solutions of the sparse grid approximations, as the number of scenarios increases, converges t...

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In this paper we develop and test scenario generation methods for asset liability management models. We propose a multi-stage stochastic programming model for a Dutch pension fund. Both randomly sampled event trees and event trees tting the mean and the covariance of the return distribution are used for generating the coeecients of the stochastic program. In order to investigate the performance...