Exact methods for large-scale multi-period financial planning problems

R. Baldacci, Marco A. Boschetti*, N. Christofides, S. Christofides

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

A relevant financial planning problem is the periodical rebalance of a portfolio of assets such that the portfolio's total value exhibits certain characteristics. This problem can be modelled using a transition graph G to represent the future state space evolution of the corresponding economy and mathematically formulated as a linear programming problem. We present two different mathematical formulations of the problem. The first considers explicitly the set of the possible scenarios (scenario-based approach), while the second considers implicitly the whole set of scenarios provided by the graph G (graph-based approach). Unfortunately, for both the formulations the size of the corresponding linear programs can be huge even for simple financial problems. However, the graph-based approach seems to be a more powerful model, since it allows to consider a huge number of scenarios in a very compact formulation. The purpose of this paper is to present both heuristic and exact methods for the solution of large-scale multi-period financial planning problems using the graph-based model. In particular, in this paper we propose lower and upper bounds and three exact methods based on column, row and column/row generation, respectively. Since the methods based on column/row generation exploits simultaneously both the primal and the dual structure of the problem we call it Criss-Cross generation method. Computational results are given to prove the effectiveness of the proposed methods.

Original languageEnglish
Pages (from-to)281-306
Number of pages26
JournalComputational Management Science
Volume6
Issue number3
DOIs
Publication statusPublished - 2009
Externally publishedYes

Keywords

  • Column/row generation
  • Large scale optimization
  • Linear programming
  • Portfolio management

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