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Optimal income drawdown and investment with longevity basis risk
Department of Statistical and Actuarial Sciences, University of Western Ontario, London, Canada.
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics. Adam Smith Business School, University of Glasgow, Glasgow, UK; Inland University of Applied Sciences, Business Analytics, Lillehammer, Norway.
Adam Smith Business School, University of Glasgow, Glasgow, UK.
2025 (English)In: Scandinavian Actuarial Journal, ISSN 0346-1238, E-ISSN 1651-2030, Vol. 2025, no 7, p. 680-717Article in journal (Refereed) Published
Abstract [en]

We investigate a utility maximisation problem for a pension scheme which offers an income drawdown policy. Apart from market risk, we specifically focus on longevity basis risk which arises when the forces of mortality of the scheme's target population and the reference population of a longevity bond used for hedging, are not perfectly correlated. By modelling the forces of mortality of the reference and target populations as stochastic affine processes, we derive analytic solutions for the relevant investment strategy and benefit withdrawal rate. Our model also accounts for dependencies between mortality rate fluctuations and stock prices. Our extensive numerical results demonstrate that the longevity bond acts as an effective hedging instrument, even in the presence of longevity basis risk.

Place, publisher, year, edition, pages
Taylor & Francis, 2025. Vol. 2025, no 7, p. 680-717
Keywords [en]
Stochastic optimal control, Hamilton–Jacobi–Bellman equation, portfolio optimisation, pension funds, longevity basis risk
National Category
Economics
Identifiers
URN: urn:nbn:se:umu:diva-238860DOI: 10.1080/03461238.2025.2455056ISI: 001407027400001Scopus ID: 2-s2.0-85216197791OAI: oai:DiVA.org:umu-238860DiVA, id: diva2:1958758
Funder
The Kempe Foundations, JCSMK22-0082Available from: 2025-05-16 Created: 2025-05-16 Last updated: 2025-12-12Bibliographically approved

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Ewald, Christian Oliver

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  • nn-NB
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