Massively parallel asset and liability management

Abstract

Multistage Stochastic Programming is a popular method to solve financial planning problems such as Asset and Liability Management (ALM). The desirability to have future scenarios match static and dynamic correlations between assets leads to problems of truly enormous sizes (often reaching tens of millions of unknowns or more). Clearly parallel processing becomes mandatory to deal with such problems. Solution approaches for these problems include nested Decomposition and Interior Point Methods. The latter class in particular is appealing due to its flexibility with regard to model formulation and its amenability to parallelisation on massively parallel architectures. We review some of the results and challenges in this approach, demonstrate how popular risk measures can be integrated into the framework and address the issue of modelling for High Performance Computing. © 2011 Springer-Verlag Berlin Heidelberg.