Intergenerational risk sharing in a Defined Contribution pension system: analysis with Bayesian optimization

TL;DR

This paper explores a collective defined-contribution pension system with intergenerational risk sharing, optimized via Bayesian methods, demonstrating improved welfare for risk-averse participants under market volatility.

Contribution

It introduces a realistic IRS mechanism with adaptive indexation, optimized through Bayesian techniques, to enhance welfare without external financing or borrowing.

Findings

IRS improves participant welfare in volatile markets

Optimal adjustment parameter controls risk-benefit trade-off

Bayesian optimization effectively tunes investment and adjustment parameters

Abstract

We study a fully funded, collective defined-contribution (DC) pension system with multiple overlapping generations. We investigate whether the welfare of participants can be improved by intergenerational risk sharing (IRS) implemented with a realistic investment strategy (e.g., no borrowing) and without an outside entity (e.g., share holders) that helps finance the pension fund. To implement IRS, the pension system uses an automatic adjustment rule for the indexation of individual accounts, which adapts to the notional funding ratio of the pension system. The pension system has two parameters that determine the investment strategy and the strength of the adjustment rule, which are optimized by expected utility maximization using Bayesian optimization. The volatility of the retirement benefits and that of the funding ratio are analyzed, and it is shown that the trade-off between them can be controlled by the optimal adjustment parameter to attain IRS. Compared with the optimal individual DC benchmark using the life-cycle strategy, the studied pension system with IRS is shown to improve the welfare of risk-averse participants, when the financial market is volatile.