Multi-period mean-variance portfolio optimization based on Monte-Carlo simulation

Fei Cong, Kees Oosterlee

Research output: Contribution to journalArticleScientificpeer-review

23 Citations (Scopus)

Abstract

We propose a simulation-based approach for solving the constrained dynamic mean-variance portfolio management problem. For this dynamic optimization problem, we first consider a sub-optimal strategy, called the multi-stage strategy, which can be utilized in a forward fashion. Then, based on this fast yet sub-optimal strategy, we propose a backward recursive programming approach to improve it. We design the backward recursion algorithm such that the result is guaranteed to converge to a solution, which is at least as good as the one generated by the multi-stage strategy. In our numerical tests, highly satisfactory asset allocations are obtained for dynamic portfolio management problems with realistic constraints on the control variables.

Original languageEnglish
Pages (from-to)23-38
Number of pages16
JournalJournal of Economic Dynamics and Control
Volume64
DOIs
Publication statusPublished - 2016

Keywords

  • Constrained optimization
  • Dynamic portfolio management
  • Least squares regression
  • Mean-variance optimization
  • Simulation method

Fingerprint Dive into the research topics of 'Multi-period mean-variance portfolio optimization based on Monte-Carlo simulation'. Together they form a unique fingerprint.

Cite this