Hedging longevity risk in defined contribution pension schemes

Ankush Agarwal (), Christian-Oliver Ewald and Yongjie Wang ()
Additional contact information
Ankush Agarwal: University of Glasgow
Yongjie Wang: University of Glasgow

Computational Management Science, 2023, vol. 20, issue 1, No 11, 34 pages

Abstract: Abstract Pension schemes all over the world are under increasing pressure to efficiently hedge longevity risk imposed by ageing populations. In this work, we study an optimal investment problem for a defined contribution pension scheme that decides to hedge longevity risk using a mortality-linked security, typically a longevity bond. The pension scheme promises a minimum guarantee which allows the members to purchase lifetime annuities upon retirement. The scheme manager invests in the risky and riskless assets available on the market, including the longevity bond. We transform the corresponding constrained optimal investment problem into a single investment portfolio optimization problem by replicating future contributions from members and the minimum guarantee provided by the scheme. We solve the resulting optimization problem using the dynamic programming principle. Through a series of numerical studies, we show that the longevity risk has an important impact on the investment strategy performance. Our results add to the growing evidence supporting the use of mortality-linked securities for efficient hedging of longevity risk.

Keywords: Defined contribution pension scheme; Longevity bond; Stochastic control; Dynamic programming principle (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10287-023-00440-8 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
Working Paper: Hedging longevity risk in defined contribution pension schemes (2020)
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:spr:comgts:v:20:y:2023:i:1:d:10.1007_s10287-023-00440-8

Ordering information: This journal article can be ordered from
http://www.springer. ... ch/journal/10287/PS2

DOI: 10.1007/s10287-023-00440-8

Access Statistics for this article

Computational Management Science is currently edited by Ruediger Schultz

More articles in Computational Management Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().