Unified framework of mean-field formulations for optimal multi-period mean-variance portfolio selection

Xiangyu Cui, Xun Li, Duan Li

Research output: Journal article publicationJournal articleAcademic researchpeer-review

52 Citations (Scopus)


When a dynamic optimization problem is not decomposable by a stage-wise backward recursion, it is nonseparable in the sense of dynamic programming. The classical dynamic programming-based optimal stochastic control methods would fail in such nonseparable situations as the principle of optimality no longer applies. Among these notorious nonseparable problems, the dynamic mean-variance portfolio selection formulation had posed a great challenge to our research community until recently. Different from the existing literature that invokes embedding schemes and auxiliary parametric formulations to solve the dynamic mean-variance portfolio selection formulation, we propose in this paper a novel mean-field framework that offers a more efficient modeling tool and a more accurate solution scheme in tackling directly the issue of nonseparability and deriving the optimal policies analytically for the multi-period mean-variance-type portfolio selection problems.
Original languageEnglish
Article number6767103
Pages (from-to)1833-1844
Number of pages12
JournalIEEE Transactions on Automatic Control
Issue number7
Publication statusPublished - 1 Jan 2014


  • intertemporal restrictions
  • mean-field formulation
  • multi-period mean-variance (MV) portfolio selection
  • risk control over bankruptcy
  • Stochastic optimal control

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering


Dive into the research topics of 'Unified framework of mean-field formulations for optimal multi-period mean-variance portfolio selection'. Together they form a unique fingerprint.

Cite this