Mean-variance optimization for life-cycle pension portfolios

In this thesis we discuss a framework for life-cycle construction. For the construction of life-cycles we use mean-variance optimization. Mean-variance optimization is a portfolio selection method used to find a combination of asset classes that has an optimal risk-return trade-off. We choose the replacement ratio, the pension income as fraction of labour income, as the quantity to be optimized. We find that using mean-variance optimization for the construction of deterministic life-cycles yields results that contradict conventional investment wisdom. It is mean-variance optimal to increase risk-taking as time passes, whereas conventional investment wisdom states that risk should decrease as time goes by. We introduce dynamic mean-variance optimization, where the asset allocation can adapt to changing circumstances, as an alternative to deterministic mean-variance optimization. We introduce an algorithm for dynamic mean-variance optimization of the replacement ratio, an extension of the dynamic mean-variance algorithm by Cong and Oosterlee. We show that dynamic mean-variance optimization can be used for life-cycle construction and that dynamic life-cycles outperform deterministic ones.Electrical Engineering, Mathematics and Computer ScienceDelft Institute of Applied Mathematic