Robust optimal investment strategies of DC pension plan under limited attention allocation

Aiming Song and Dengsheng Chen

Communications in Statistics - Theory and Methods, 2025, vol. 54, issue 21, 6966-6987

Abstract: This article investigates the robust optimal investment strategies for defined contribution pension plan participants who obtain the interest rate and stock models under limited attention allocation. In addition, suppose that pension contribution rates are subjected by a stochastic differential equation whose volatility comes from the price process of risk assets and interest rates. By using Sims’ information channel capacity models, we first obtain the dynamic models of interest rates and stock prices in steady state, then the robust optimization problem is established. By applying Kalman filtering and robust optimization theories, the robust optimal investment strategy of pension managers is obtained. For comparison, we also consider two special cases that are without limited attentions and models uncertainty. In the end, some numerical examples are carried out, and find that the proportion of pension fund managers investing risk assets under limited attention allocation is higher than that under complete information.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2025.2464090 (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:21:p:6966-6987

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

DOI: 10.1080/03610926.2025.2464090

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 ().