Parallel Computing for Dynamic Asset Allocation Based on the Stochastic Programming

Abstract

In this paper, a multi-stage stochastic programming model is constructed, for the dynamic asset allocation with the transaction cost constraints. In the mean time in order to improve the performance, the Conditional Value-at-Risk as the risk measure, which is a very important concept in the modern risk management field, is also contained. However, with the increase of the number of scenarios, the number of constrains and decisions variable is increasing dramatically. It turns out that the memory management is a major bottleneck when solving planning problems. For this reason, this paper shows that the dedicated model generations, and the specialized solution techniques based on high performance computing, are the essential elements to tackle this large-scale financial planning. The parallel code is programmed by the C language, and the Message Passing Interface (MPI) for communication is utilized. The parallel and financial performance is performed on the DeepComp7000.