Abstract
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 approach.
Stochastic programming models suit these needs well and have already been
applied in the field of asset liability management to improve financial operations
and risk management.
The dynamic aspect of the financial planning problems 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.
We report on the solution of an asset liability management model for an actual
Dutch pension fund with 4,826,809 scenarios, 12,469,250 constraints and
24,938,502 variables, which is the largest stochastic linear program ever solved.
A closer look at the optimal decisions reveals that the initial asset mix is more stable
for larger models, demonstrating the potential benefits of the high-performance
computing approach for ALM.
Key words: Asset Liability Modeling, Decomposition, Interior Point Methods, High Performance Computing.