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This article sets out principles and decision rules for setting appropriate drawdown and investment strategies during retirement given an individual’s objectives and risk tolerance. In particular, we highlight how the suitable drawdown strategy can relate to the objective, and how annuities might be combined with other investments to help manage risk. Our aim is to offer research-based guidance to product providers, financial advisers and individuals in formulating retirement strategies.

We deploy a stochastic life-cycle model to examine how differing levels of the superannuation guarantee (SG) impact on the welfare of individual Australians under existing superannuation, tax and pension eligibility rules. Our main focus is the effect of various assumptions on the optimal SG, emphasizing the role of income and the retirement objectives of the individual. The analysis supports estimating the gains and losses from changing the SG for various individuals, and associated impacts on net government revenue. We find the optimal SG to vary substantially with income and objectives. While our baseline analysis indicates a SG of below the current level of 9.5%, higher estimates emerge if access to the Age Pension is excluded, and if the SG is used as a mechanism to self-insure against living to a very old age, being forced into early retirement, or incurring lower investment returns. We conclude that the case for raising the SG above 9.5% depends on the underlying assumptions, with the policy objectives that the SG is intended to achieve being critical.

We analyze the ability of different investment strategies to give pre-retirees more certainty about their income in retirement, while allowing them to benefit from taking investment risk. Specifically, we look at the optimal strategies that maximize the expected values of a loss aversion utility function and a constant relative risk aversion (CRRA) utility function. We also include a typical lifestyle strategy. We find that the loss aversion utility function gives a high degree of certainty about its reference point, the latter chosen to give the desired replacement ratio. Adding in constraints on the income purchased at retirement does not improve the certainty of achieving the desired replacement ratio enough to counter-balance the increased chance of obtaining an income far below it. A CRRA utility function fails to get adequate certainty on the retirement income, and becomes too risk-averse when constraints are added. The lifestyle strategy performs well but is overall out-performed by the loss aversion-derived strategy. We include inflation in our model, and allow for inflation-indexed bonds in our investment universe. Our results are analyzed in both a Brownian motion-driven financial market model and using UK historical data.

We investigate the conditions under which life-cycle investment strategies based on age may be ‘near enough’ to optimal, focusing on the treatment of the pension account balance and assumptions about risk aversion. We show that dynamically adjusting the strategy in response to fluctuations in balance as well as age can lead to moderate improvements over product designs currently seen in the market; although most of the potential gains might be captured by specifying the glide path with reference to measures reflecting the projected balance over time. The risk aversion assumption emerges as a far more important consideration, with much greater reductions in expected utility arising from mismatches between the risk aversion of the investor and that underpinning the glide path design. Our analysis suggests possibilities for improving life-cycle or target date funds, and highlights the benefit of offering a suite of such funds that cater for members with differing risk aversion.

This paper considers an alternative way of structuring stochastic variables in a dynamic programming framework where the model structure dictates that numerical methods of solution are necessary. Rather than estimating integrals within a Bellman equation using quadrature nodes, we use nodes directly from the underlying data. An example of the application of this approach is presented using individual lifetime financial modelling. The results show that data-driven methods lead to the least losses in result accuracy compared to quadrature and Quasi-Monte Carlo approaches, using historical data as a base. These results hold for both a single stochastic variable and multiple stochastic variables. The results are significant for improving the computational accuracy of lifetime financial models and other models that employ stochastic dynamic programming.

More intensive copyright enforcement reduces piracy, raises prices, and lowers consumer surplus. We show that these results do not hold regarding the extent rather than intensity of enforcement. When enforcement is targeted at high-value buyers such as corporate and government users, the copyright holder has an incentive to charge super-monopoly prices, thereby encouraging piracy among low-value buyers. Extending enforcement down the demand curve broadens the copyright holder’s captive market, leading to lower prices and higher sales that can increase both profits and consumer surplus. The standard tradeoff between the incentive to generate intellectual property and the cost of monopoly power is therefore avoided. Private enforcement by copyright holders may be insuciently extensive since consumers can also benefit from more extensive enforcement. Similarly, new technologies which lead to stronger control over illicit use can paradoxically benefit consumers.