Management of Portfolio Depletion Risk through Optimal Life Cycle Asset Allocation

Vetzal, Kenneth R.; Westmacott, Graham

doi:10.1080/10920277.2019.1570469

Cited by 18 publications

(18 citation statements)

References 52 publications

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“…at the end of the accumulation phase when the employee becomes a retiree. As noted in Forsyth et al (2019), it is quite common in the literature to suggest or assume that upon retirement the retiree would purchase annuities. This simply means that the savings in the DC plan is used to purchase a stream of (possibly fixed) cash flows made at regular intervals by the annuitant, often an insurance company, to the retiree.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, if we reasonably assume as in Forsyth et al (2019) that the retiree does not intend to purchase annuities upon retirement, then the DC plan holder -who, it should be emphasized, is a retail investor -is effectively responsible to make asset allocation decisions over a very long investment time horizon indeed. If we conservatively assume an accumulation period (i.e.…”

Section: Introductionmentioning

confidence: 99%

“…We therefore follow for example Forsyth andVetzal (2017b, 2019a,b); Forsyth et al (2019); Hojgaard and Vigna (2007); Menoncin and Vigna (2013); Vigna (2014), among many others, in concluding that 1.2 Dynamic mean-variance (MV) portfolio optimization dynamic MV portfolio optimization is a reasonable and practical choice for the investor, such as a DC plan holder, wishing to derive optimal asset allocation strategies over a very long investment time horizon.…”

Section: Introductionmentioning

confidence: 99%

“…In the light of these considerations, it is therefore not surprising that there has been significant interest recently in different aspects of the terminal wealth distribution obtained under various investment strategies, including optimal strategies associated with approaches to dynamic MV portfolio optimization -see for example Forsyth andVetzal (2017a,b, 2019a,b); Forsyth et al (2019). These papers present a very realistic formulation of the underlying problems, including for example the treatment of withdrawals and contributions, investment constraints, and so on.…”

Section: Introductionmentioning

confidence: 99%

“…There has been significant interest recently in the literature (see for example Forsyth andVetzal (2017a,b, 2019a,b); Forsyth et al (2019)) regarding different aspects of the terminal wealth distributions under various investment strategies, including MV-optimal strategies. However, these papers present a very realistic formulation of the underlying problems, and by necessity therefore focus on the results obtained from the numerical solutions of the problems under consideration.…”

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confidence: 99%

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Numerical methods for mean-risk portfolio optimization

Staden¹,

Marais²

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We consider various aspects of the dynamic mean-risk portfolio optimization problem, with specific focus on the numerical methods to solve this problem in the case where the risk measure is either the variance of wealth outcomes or the quadratic variation of the wealth process. In the case where the risk measure is the variance of wealth outcomes, the mean-risk problem is known as the dynamic mean-variance (MV) portfolio optimization problem. Since variance is not separable in the sense of dynamic programming, the MV objective violates Bellman's principle of optimality. As a result, the possibility of the time-inconsistency of the associated MV-optimal investment strategies leads to a number of competing approaches to formulating and solving the dynamic MV problem. We primarily focus on the two main approaches, namely the time-consistent MV (TCMV) and the pre-commitment MV (PCMV) optimization problems. In addition, this investigation also leads us to consider related approaches which are not necessarily MV optimal in some sense, such as the Mean-Quadratic Variation (MQV) portfolio optimization problem.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%