Log-robust portfolio management after transaction costs

Pae, Yuntaek; Sabbaghi, Navid

doi:10.1007/s00291-013-0322-y

Cited by 7 publications

(4 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Kawas & Thiele (2011a) proposed a Log-robust PSP where the scaled deviation belongs to a budget uncertainty set in two cases: correlated and uncorrelated assets. Kawas & Thiele (2011b) developed Log-robust PSP by considering short selling, while Pae & Sabbaghi (2014) added transaction cost constraint which increases the practical application of Log-robust PSP. Instead of using predefined uncertainty sets, Kawas & Thiele (2017) proposed data-driven Logrobust PSPs for two cases, correlated and uncorrelated assets.…”

Section: Robust Utility Function Pspmentioning

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

Section: Robust Utility Function Pspmentioning

confidence: 99%

Robust Portfolio Selection Problems: A Comprehensive Review

Ghahtarani¹,

Ahmed²,

Ghasemi³

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Now, the ARPO procedure resumes by starting an iterative improvement process (lines . As in most ILS frameworks, this process comprises three stages: (a) the perturbation stage (lines 12-15), which applies strong changes to the current base solution in order to increase exploration of the space of solutions; (b) the local search stage (lines [16][17][18][19][20][21][22], which tries to perform a quick improvement of the current base solution by applying some operators-in our case, it is based on the combined use of quadratic programming and a cache memory; and (c) the acceptation stage (lines 23-33), which makes use of a credit-based system in order to allow accepting, under certain restrictive conditions, a new base solution even when it offers a slightly higher risk than the current base solution-this 'degradation' of the base solution is allowed in order to reduce the probabilities of getting trapped in a local minimum during the searching process.…”

Section: The Arpo Metaheuristicmentioning

confidence: 99%

“…Second, the current base solution is partially destroyed according to some random criterion-in our case, a randomly selected number of assets are deleted from the portfolio-(lines 01-09). Third, the destroyed solution is re-constructed (completed) by adding new assets to the portfolio (lines [10][11][12][13][14][15][16][17][18][19].…”

Section: The Arpo Metaheuristicmentioning

confidence: 99%

“…The third path involves incorporation of the additional criteria and constraints [12,13]. Traditional optimization methods include, among others, linear programming [14][15][16][17][18][19][20], quadratic programming [21][22][23] and stochastic programming [24]. However, traditional optimization methods (notably, linear and quadratic programming) are plagued by a number of caveats.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Biased-Randomized Iterated Local Search Algorithm for Rich Portfolio Optimization

et al. 2019

View full text Add to dashboard Cite

This research develops an original algorithm for rich portfolio optimization (ARPO), considering more realistic constraints than those usually analyzed in the literature. Using a matheuristic framework that combines an iterated local search metaheuristic with quadratic programming, ARPO efficiently deals with complex variants of the mean-variance portfolio optimization problem, including the well-known cardinality and quantity constraints. ARPO proceeds in two steps. First, a feasible initial solution is constructed by allocating portfolio weights according to the individual return rate. Secondly, an iterated local search framework, which makes use of quadratic programming, gradually improves the initial solution throughout an iterative combination of a perturbation stage and a local search stage. According to the experimental results obtained, ARPO is very competitive when compared against existing state-of-the-art approaches, both in terms of the quality of the best solution generated as well as in terms of the computational times required to obtain it. Furthermore, we also show that our algorithm can be used to solve variants of the portfolio optimization problem, in which inputs (individual asset returns, variances and covariances) feature a random component. Notably, the results are similar to the benchmark constrained efficient frontier with deterministic inputs, if variances and covariances of individual asset returns comprise a random component. Finally, a sensitivity analysis has been carried out to test the stability of our algorithm against small variations in the input data.

show abstract

A robust asset–liability management framework for investment products with guarantees

2016

View full text Add to dashboard Cite

This paper suggests a robust asset-liability management framework for investment products with guarantees, such as guaranteed investment contracts and equity-linked notes. Stochastic programming and robust optimization approaches are introduced to deal with data uncertainty in asset returns and interest rates. The statistical properties of the probability distributions of uncertain parameters are incorporated in the model through appropriately selected symmetric and asymmetric uncertainty sets. Practical data-driven approaches for implementation of the robust models are also discussed. Numerical results using generated and real market data are presented to illustrate the performance of the robust asset-liability management strategies. The robust investment strategies show better performance in unfavorable market regimes than traditional stochastic programming approaches. The effectiveness of robust investment strategies can be improved by calibrating carefully the shape and the size of the uncertainty sets for asset returns.

show abstract

Log-robust portfolio management after transaction costs

Cited by 7 publications

References 11 publications

Robust Portfolio Selection Problems: A Comprehensive Review

Robust Portfolio Selection Problems: A Comprehensive Review

A Biased-Randomized Iterated Local Search Algorithm for Rich Portfolio Optimization

A robust asset–liability management framework for investment products with guarantees

Contact Info

Product

Resources

About