Money-Back Tontines for Retirement Decumulation: Neural-Network Optimization under Systematic Longevity Risk
German Nova Orozco, Duy-Minh Dang, Peter A. Forsyth

TL;DR
This paper develops a neural-network based method to optimize retirement decumulation strategies in tontines with money-back guarantees, accounting for systematic longevity risk and international diversification, to improve the trade-off between expected withdrawals and wealth risk.
Contribution
It introduces a neural-network approach to solve high-dimensional, constrained control problems in retirement decumulation with MBGs under systematic longevity risk and diversification.
Findings
International diversification and longevity pooling improve the EW-CVaR trade-off.
Foreign equity acts as a state-dependent catch-up instrument.
Implied MBG loads are mainly driven by tail outcomes, not mean payouts.
Abstract
Money-back guarantees (MBGs) are features of pooled retirement income products that address bequest concerns by ensuring the initial premium is returned through lifetime payments or, upon early death, as a death benefit to the estate. This paper studies optimal retirement decumulation in an individual tontine account with an MBG overlay under international diversification and systematic longevity risk. The retiree chooses withdrawals and asset allocation dynamically to trade off expected total withdrawals (EW) against the Conditional Value-at-Risk (CVaR) of terminal wealth, subject to realistic investment constraints. The optimization is solved under a plan-to-live convention, while stochastic mortality affects outcomes through its impact on mortality credits at the pool level. We develop a neural-network based computational approach for the resulting high-dimensional, constrained…
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Taxonomy
TopicsInsurance, Mortality, Demography, Risk Management · Financial Literacy, Pension, Retirement Analysis · Economic Policies and Impacts
