High-Performance Computing for Asset-Liability Management

Abstract

Financial institutions require sophisticated tools for risk management. For companywide 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.