Developing a target-driven life cycle strategy for DC schemes
Within defined contribution (DC) schemes an employee builds up capital for retirement in a personal account. In contrast, defined benefit schemes are based on a collective account for all employees. Recently, DC schemes are gaining more and more attention. We expect this trend to continue in the future. In a nutshell we can say an employee who saves via a DC-scheme puts money in his personal account every month. For a DC scheme, the cumulated wealth over the lifecycle will be uncertain caused by shocks in the financial assets in which the pension savings are invested. The vast majority of the participants invest the financial wealth according to a carefully designed default life cycle. The allocations within the default life cycle path to fixed income and risky assets are in general based on the median scenario for asset returns, (wage) inflation and career paths.
Most designs of the glide paths have no (automatic) adjustments of the allocations to deviations in the assumptions; e.g. if the stock markets have experienced high returns the probability of reaching a previously defined target replacement rate (i.e. the ratio of the retiree’s income to his last salary) will have increased. It could in those cases be preferable to eventually de-risk the investment strategy to ‘lock in’ the current wealth.
In this internship we would like to develop a life cycle strategy which can react to current or forward looking developments in the financial markets and/or the characteristics of the participants in the DC scheme. This strategy can be dynamic and the study should include a trade-off between the probability of attaining the required replacement ratio and the size of the losses if unsuccessful. The framework will be based on loss aversion utility functions instead of the more commonly used power utility functions.
Blake, D., D. Wright and Y. Zhang (2013), “Target-driven investing: Optimal investment strategies in defined contribution pension plans under loss aversion”, Journal of Economic Dynamics & Control, 37(1), 195-209