Robust portfolio optimization: a categorized bibliographic review

Xidonas, Panos; Steuer, Ralph E.; Hassapis, Christis

doi:10.1007/s10479-020-03630-8

Cited by 56 publications

(24 citation statements)

References 139 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…We believe this paper should increase the popularity of robust portfolio optimization among practitioners by tackling the major hurdles that may prevent them from using this technology. Let us mention that while this paper applies RO in portfolio construction, Fabozzi et al (2010), Gabrel et al (2014), Kim et al (2018), and more recently Xidonas et al (2020), provide comprehensive surveys in many other areas of application of RO in finance. Kim et al (2017) discusses the use of robust factor investing in portfolio management.…”

Section: Introductionmentioning

confidence: 99%

A practical guide to robust portfolio optimization

Yin

Perchet

Soupé

2021

Quantitative Finance

View full text Add to dashboard Cite

Robust optimization takes into account the uncertainty in expected returns to address the shortcomings of portfolio mean-variance optimization, namely the sensitivity of the optimal portfolio to inputs. We investigate the mechanisms by which robust optimization achieves its goal and give practical guidance when it comes to the choice of uncertainty in form and level. We explain why the quadratic uncertainty set should be preferred to box uncertainty based on the literature review, we show that a diagonal uncertainty matrix with only variances should be used, and that the level of uncertainty can be chosen as a function of the asset Sharpe ratios. Finally, we use practical examples to show that, with the proposed parametrization, robust optimization does overcome the weaknesses of mean-variance optimization and can be applied in real investment problems such as the management of multi-asset portfolios or in robo-advising.

show abstract

Section: Introductionmentioning

confidence: 99%

A practical guide to robust portfolio optimization

Yin

Perchet

Soupé

2021

Quantitative Finance

View full text Add to dashboard Cite

show abstract

“…Under the ideal conditions defined by Markowitz, this optimization problem can be solved using quadratic programming. However, once we start considering additional real-world constraints, the problem becomes more mathematically complex, and tackling it using traditional methods requires increasingly sophisticated instruments, like conic optimization or sparse mean–variance portfolio modeling, and simplifying assumptions [ 3 , 4 , 5 , 6 ]. At one point, the problem becomes so complicated that both researchers and practitioners often start using metaheuristics from the field of evolutionary computation like genetic algorithms, differential evolution or evolution strategies, to name a few.…”

Section: Introductionmentioning

confidence: 99%

Resampled Efficient Frontier Integration for MOEAs

Quintana

Moreno

2021

Entropy

View full text Add to dashboard Cite

Mean-variance portfolio optimization is subject to estimation errors for asset returns and covariances. The search for robust solutions has been traditionally tackled using resampling strategies that offer alternatives to reference sets of returns or risk aversion parameters, which are subsequently combined. The issue with the standard method of averaging the composition of the portfolios for the same risk aversion is that, under real-world conditions, the approach might result in unfeasible solutions. In case the efficient frontiers for the different scenarios are identified using multiobjective evolutionary algorithms, it is often the case that the approach to averaging the portfolio composition cannot be used, due to differences in the number of portfolios or their spacing along the Pareto front. In this study, we introduce three alternatives to solving this problem, making resampling with standard multiobjective evolutionary algorithms under real-world constraints possible. The robustness of these approaches is experimentally tested on 15 years of market data.

show abstract

“…Another review by Kim et al (2018a) focused on worst-case frameworks in bond portfolio construction, currency hedging, and option pricing, while covering a small number of references on robust asset-liability management problems, log-robust models, and robust multi-period problems. Finally, Xidonas et al (2020) provides a categorized bibliographic review which has a broad scope, yet is limited in technical details.…”

Section: Introductionmentioning

confidence: 99%

Robust Portfolio Selection Problems: A Comprehensive Review

Ghahtarani¹,

Ahmed²,

Ghasemi³

2021

Preprint

View full text Add to dashboard Cite

In this paper, we provide a comprehensive review of recent advances in robust portfolio selection problems and their extensions, from both operational research and financial perspectives. A multi-dimensional classification of the models and methods proposed in the literature is presented, based on the types of financial problems, uncertainty sets, robust optimization approaches, and mathematical formulations. Several open questions and potential future research directions are identified.

show abstract

Robust portfolio optimization: a categorized bibliographic review

Cited by 56 publications

References 139 publications

A practical guide to robust portfolio optimization

A practical guide to robust portfolio optimization

Resampled Efficient Frontier Integration for MOEAs

Robust Portfolio Selection Problems: A Comprehensive Review

Contact Info

Product

Resources

About