Target date funds (TDFs) are becoming increasingly popular investment choices among investors with long-term prospects. Examples include members of superannuation funds seeking to save for retirement at a given age. TDFs provide efficient risk exposures to a diversified range of asset classes that dynamically match the risk profile of the investment payoff as the investors age. This is often achieved by making increasingly conservative asset allocations over time as the retirement date approaches. Such dynamically evolving allocation strategies for TDFs are often referred to as glide paths. We propose a systematic approach to the design of optimal TDF glide paths implied by retirement dates and risk preferences and construct the corresponding dynamic asset allocation strategy that delivers the optimal payoffs at minimal costs. The TDF strategies we propose are dynamic portfolios consisting of units of the growth-optimal portfolio (GP) and the risk-free asset. Here, the GP is often approximated by a well-diversified index of multiple risky assets. We backtest the TDF strategies with the historical returns of the S&P500 total return index serving as the GP approximation.

In recent years, Constant Proportion Portfolio Insurance (CPPI) has been the most widely recognised form of portfolio insurance among market practitioners, despite a lack of theoretical framework to support it. This paper presents a revised formulation of Option Based Portfolio Insurance (OBPI) and shows, through a case study, how it can be used as a structured product and applied in practice as a dynamic investment strategy for insurance and pensions funds such as with-profits funds. CPPI and the Revised Option Based Portfolio Insurance (ROBPI) technique adopted in this paper are similar in the sense that they rely on dynamic allocation between risky and risk-free assets to provide downside protection. Comparison between the two methods shows that ROPBI is more efficient and forward looking, giving more information about downside risk and producing less volatile asset allocation, which reduces transaction costs and any market impact.