High Performance Computing for Asset Liability Management
Jacek Gondzio and Roy Kouwenberg
Financial institutions require sophisticated tools for risk management.
For company-wide risk management both sides of the balance sheet should
be considered, resulting in an integrated asset liability management
Stochastic programming models suit well these needs and have already
been applied in the field of asset liability management to improve
financial operations and risk management.
The need of dealing with a long term planning horizon inevitably
leads to multiple decision stages (trading dates) in the stochastic program
and results in an explosion of dimensionality.
In this paper we show that dedicated model generation, specialized solution
techniques based on decomposition and high performance computing are the
essential elements to tackle these large scale financial planning problems.
It turns out that memory management is a major bottleneck when solving
very large problems, given an efficient solution approach and a parallel
computing facility. In this paper we report on the solution of an asset
liability management model for an actual Dutch pension fund which includes
contribution policies and takes into account transaction costs.
The model allows for 6 portfolio rebalancing dates and up to 13
realizations to approximate the conditional return distributions.
This leads to a model with 4,826,809 scenarios, 12,469,250 constraints
and 24,938,502 variables, which is the largest stochastic linear program
Technical Report MS-99-004,
Department of Mathematics and Statistics,
The University of Edinburgh, Scotland.
May 21, 1999.