Multi-Period Mean CVAR Asset Allocation: Is it Advantageous to be Time Consistent?

Forsyth, Peter

doi:10.2139/ssrn.3340194

Cited by 5 publications

(13 citation statements)

References 42 publications

(56 reference statements)

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“…However, as noted in the introduction, this chapter focuses on a theoretical comparison of optimal terminal wealth distributions in the particular select cases where the optimal investment strategies to dynamic MV optimization problems can be expressed analytically. The two main consequences of Assumption 5.3.1 are therefore that it (i) ensures that an additional perspective on the implications of the various approaches to dynamic MV optimization can be presented in this chapter that is currently missing from the literature, and (ii) assists in explaining some of the numerical results reported in literature (for example Forsyth andVetzal (2017a,b, 2019a,b); Forsyth et al (2019)).…”

Section: Selected Analytical Resultsmentioning

confidence: 96%

“…The notion of optimization naturally raises questions regarding the choice of an objective function to be used for optimization purposes. Unsurprisingly, there are many objectives used in portfolio optimization, including minimizing the probability of ruin/shortfall of wealth upon retirement or death (see for example Bayraktar and Young (2009); Young (2004)), different measures of the portfolio risk and return incorporated in objective functions (for example, Bi and Cai (2019); Forsyth (2019); Kryger and Steffensen (2010); Miller and Yang (2017)), and so on.…”

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: Selected Analytical Resultsmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Numerical methods for mean-risk portfolio optimization

Staden¹,

Marais²

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Discrete-time mean-CVaR portfolio selection and time-consistency induced term structure of the CVaR

Strub

Cui

et al. 2019

Journal of Economic Dynamics and Control

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Optimal Control of the Decumulation of a Retirement Portfolio With Variable Spending and Dynamic Asset Allocation

Vetzal¹,

Westmacott²

2021

ASTIN Bull.

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We extend the Annually Recalculated Virtual Annuity (ARVA) spending rule for retirement savings decumulation (Waring and Siegel (2015) Financial Analysts Journal, 71(1), 91–107) to include a cap and a floor on withdrawals. With a minimum withdrawal constraint, the ARVA strategy runs the risk of depleting the investment portfolio. We determine the dynamic asset allocation strategy which maximizes a weighted combination of expected total withdrawals (EW) and expected shortfall (ES), defined as the average of the worst 5% of the outcomes of real terminal wealth. We compare the performance of our dynamic strategy to simpler alternatives which maintain constant asset allocation weights over time accompanied by either our same modified ARVA spending rule or withdrawals that are constant over time in real terms. Tests are carried out using both a parametric model of historical asset returns as well as bootstrap resampling of historical data. Consistent with previous literature that has used different measures of reward and risk than EW and ES, we find that allowing some variability in withdrawals leads to large improvements in efficiency. However, unlike the prior literature, we also demonstrate that further significant enhancements are possible through incorporating a dynamic asset allocation strategy rather than simply keeping asset allocation weights constant throughout retirement.

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Multi-Period Mean CVAR Asset Allocation: Is it Advantageous to be Time Consistent?

Cited by 5 publications

References 42 publications

Numerical methods for mean-risk portfolio optimization

Numerical methods for mean-risk portfolio optimization

Discrete-time mean-CVaR portfolio selection and time-consistency induced term structure of the CVaR

Optimal Control of the Decumulation of a Retirement Portfolio With Variable Spending and Dynamic Asset Allocation

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