Algorithmic portfolio tilting to harvest higher moment gains

Boudt, Kris; Cornilly, Dries; Holle, Frederiek Van; Willems, Joeri

doi:10.1016/j.heliyon.2020.e03516

Cited by 19 publications

(2 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, in this section, we generalize the idea of the RFPA algorithm for solving another important high-order portfolio called the MVSK-Tilting problem with general deterioration measures. This MVSK-Tilting portfolio aims at improving a given portfolio that is not sufficiently optimal from the MVSK perspective by tilting it toward a direction that simultaneously ameliorates all the objectives [45], [46].…”

Section: Extension: Solving Mvsk-tilting Portfoliosmentioning

confidence: 99%

Efficient and Scalable High-Order Portfolios Design via Parametric Skew-t Distribution

Wang¹,

Zhou²,

Ying³

et al. 2022

Preprint

View full text Add to dashboard Cite

Since Markowitz's mean-variance framework, optimizing a portfolio that maximizes the profit and minimizes the risk has been ubiquitous in the financial industry. Initially, profit and risk were measured by the first two moments of the portfolio's return, a.k.a. the mean and variance, which are sufficient to characterize a Gaussian distribution. However, it is broadly believed that the first two moments are not enough to capture the characteristics of the returns' behavior, which have been recognized to be asymmetric and heavy-tailed. Although there is ample evidence that portfolio designs involving the third and fourth moments, i.e., skewness and kurtosis, will outperform the conventional mean-variance framework, they are non-trivial. Specifically, in the classical framework, the memory and computational cost of computing the skewness and kurtosis grow sharply with the number of assets. To alleviate the difficulty in high-dimensional problems, we consider an alternative expression for high-order moments based on parametric representations via a generalized hyperbolic skewt distribution. Then, we reformulate the high-order portfolio optimization problem as a fixed-point problem and propose a robust fixed-point acceleration algorithm that solves the problem in an efficient and scalable manner. Empirical experiments also demonstrate that our proposed high-order portfolio optimization framework is of low complexity and significantly outperforms the state-of-the-art methods by 2 ∼ 4 orders of magnitude.

show abstract

Section: Extension: Solving Mvsk-tilting Portfoliosmentioning

confidence: 99%

Efficient and Scalable High-Order Portfolios Design via Parametric Skew-t Distribution

Wang¹,

Zhou²,

Ying³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…where is Kronecker product (e.g. Martellini and Ziemann (2010); Boudt, Cornilly, Van Holle, and Willems (2020)).…”

Section: Cardinality-constrained Portfolio Optimization Problemmentioning

confidence: 99%

Cardinality-Constrained Higher-Order Moment Portfolios Using Particle Swarm Optimization

Khokhar

Boudt

Wan

2021

International Series in Operations Research &Amp; Management Science

Self Cite

View full text Add to dashboard Cite

General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal ? Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.

show abstract

Nonparametric portfolio efficiency measurement with higher moments

Krüger

2020

Empir Econ

View full text Add to dashboard Cite

The paper considers a nonparametric approach to determine portfolio efficiency using specific directions toward the portfolio frontier function. This approach allows for a straightforward incorporation of higher moments of the returns distribution beyond mean and variance. The nonparametric approach is extended by the computation of optimal directions endogenously by maximizing the distance toward the portfolio frontier as a novel methodological feature. An empirical application to Fama-French portfolios demonstrates the applicability of the nonparametric approach. The results show that the optimal directions to the frontier depend on the portfolio considered as well as on the period for which the moments are estimated. Skewness in particular plays a role in determining the optimal direction, whereas kurtosis seems to be less crucial.

show abstract

Algorithmic portfolio tilting to harvest higher moment gains

Cited by 19 publications

References 30 publications

Efficient and Scalable High-Order Portfolios Design via Parametric Skew-t Distribution

Efficient and Scalable High-Order Portfolios Design via Parametric Skew-t Distribution

Cardinality-Constrained Higher-Order Moment Portfolios Using Particle Swarm Optimization

Nonparametric portfolio efficiency measurement with higher moments

Contact Info

Product

Resources

About