We formulate the multi-period, time consistent mean-CVAR (Conditional Value at Risk) asset allocation problem in a form amenable to numerical computation. Our numerical algorithm can impose realistic constraints such as: no shorting, no-leverage, and discrete rebalancing. We focus on long term...

In contrast to single-period mean-variance portfolio allocation, optimal multi-period mean-variance allocation can be modified slightly to be effectively a down-side risk measure. With this in mind, we consider optimal multi-period mean-variance portfolio allocation in the presence of periodic...

A numerical technique based on the embedding technique proposed in [21, 33] for dynamic mean-variance (MV) optimization problems may yield spurious points, i.e. points which are not on the efficient frontier. In [27], it is shown that spurious points can be eliminated by examining the left upper...

We determine the optimal asset allocation to bonds and stocks using an Annually Recalculated Virtual Annuity (ARVA) spending rule for DC pension plan decumulation. Our objective function minimizes downside withdrawal variability for a given fixed value of total expected withdrawals. The optimal...

A data driven Neural Network (NN) optimization framework is proposed to determine optimal asset allocation during the accumulation phase of a defined contribution pension scheme. In contrast to parametric model based solutions computed by a partial differential equation approach, the proposed...

Members of defined contribution (DC) pension plans must take on additional responsibilities for their investments, compared to participants in defined benefit (DB) pension plans. The transition from DB to DC plans means that more employees are faced with these responsibilities. We explore the...

We investigate the time-consistent mean-variance (MV) portfolio optimization problem under a realistic context that involves the simultaneous application of different types of investment constraints and modelling assumptions, for which a closed-form solution is not known to exist. We develop an...

We consider optimal asset allocation for a long-term investor saving for retirement. The investment portfolio consists of a bond index and a stock index. Using multi-period mean variance criteria, we explore two types of strategies: deterministic strategies are based only on the time remaining...

We propose a data-driven Neural Network (NN) optimization framework to determine the optimal multi-period dynamic asset allocation strategy for outperforming a general stochastic target. We formulate the problem as an optimal stochastic control with an asymmetric, distribution shaping, objective...