Robust mean-variance precommitment strategies of DC pension plans with ambiguity under stochastic interest rate and stochastic volatility

Hao Chang, Leilei Zhao and Xingjiang Chen

Communications in Statistics - Theory and Methods, 2025, vol. 54, issue 17, 5597-5625

Abstract: This article studies a robust optimal investment problem with an ambiguity-averse manager (AAM) for a defined contribution (DC) plan with multiple risks under the mean-variance criterion. In the pension accumulation stage, the interest rate, the volatility, and the salary level are considered to be stochastic. The financial market consists of a risk-free asset, a risky asset, and a rolling bond. We assume that the term structure of interest rates is driven by an affine interest rate model, while the stock price and the salary level are modeled by the stochastic volatility model with stochastic interest rate. The goal of an AAM is to find a robust optimal strategy to maximize the expectation of terminal wealth and minimize the variance of terminal wealth in the worst-case scenario. By applying the Lagrange dual theory and the robust optimal control approach, we obtain closed-form expressions of the robust precommitment strategy and the efficient frontier, and subsequently some special cases are derived. Finally, a numerical example is given to illustrate the results obtained.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2024.2440597 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:lstaxx:v:54:y:2025:i:17:p:5597-5625

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/lsta20

DOI: 10.1080/03610926.2024.2440597

Access Statistics for this article

Communications in Statistics - Theory and Methods is currently edited by Debbie Iscoe

More articles in Communications in Statistics - Theory and Methods from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().