Dynamic discrete-time portfolio selection for defined contribution pension funds with inflation risk

Haixiang Yao, Ping Chen, Miao Zhang, Xun Li

Research output: Journal article publicationJournal articleAcademic researchpeer-review

4 Citations (Scopus)

Abstract

This paper investigates a multi-period asset allocation problem for a defined contribution (DC) pension fund facing stochastic inflation under the Markowitz mean-variance criterion. The stochastic inflation rate is described by a discrete-time version of the Ornstein-Uhlenbeck process. To the best of our knowledge, the literature along the line of dynamic portfolio selection under inflation is dominated by continuous-time models. This paper is the first work to investigate the problem in a discrete-time setting. Using the techniques of state variable transformation, matrix theory, and dynamic programming, we derive the analytical expressions for the efficient investment strategy and the efficient frontier. Moreover, our model’s exceptional cases are discussed, indicating that our theoretical results are consistent with the existing literature. Finally, the results established are tested through empirical studies based on Australia’s data, where there is a typical DC pension system. The impacts of inflation, investment horizon, estimation error, and superannuation guarantee rate on the efficient frontier are illustrated.

Original languageEnglish
Pages (from-to)511-540
Number of pages30
JournalJournal of Industrial and Management Optimization
Volume18
Issue number1
DOIs
Publication statusPublished - Jan 2022

Keywords

  • defined contribution pension fund
  • efficient frontier
  • multi-period mean-variance formulation
  • portfolio selection
  • Stochastic inflation rate

ASJC Scopus subject areas

  • Business and International Management
  • Strategy and Management
  • Control and Optimization
  • Applied Mathematics

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