A log-robust optimization approach to portfolio management

Kawas, Ban; Thiele, Aurélie

doi:10.1007/s00291-008-0162-3

Cited by 31 publications

(10 citation statements)

References 15 publications

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“…Quaranta and Zaffaroni [43] present a similar model, based on ''box'' uncertainty sets for the expected returns, and study its performance in the Italian market. Kawas and Thiele [44] introduce a single-period logrobust portfolio optimization approach, in which uncertainty sets are considered for the continuously compounded rates of return.…”

Section: Robust Formulations Of Classical Portfolio Allocation Framewmentioning

confidence: 99%

Robust Portfolio Selection

Pachamanova

2011

Wiley Encyclopedia of Operations Research and Management Science

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Robust portfolio selection is the process of allocating assets in such a way that the resulting portfolio is less sensitive to errors in the predicted behavior of the assets in the portfolio. Tools from robust and Bayesian statistics, simulation, and stochastic and robust optimization are often used to improve the stability, theoretical properties, and computational tractability of models for portfolio selection.

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Section: Robust Formulations Of Classical Portfolio Allocation Framewmentioning

confidence: 99%

Robust Portfolio Selection

Pachamanova

2011

Wiley Encyclopedia of Operations Research and Management Science

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“…Also, Ben-Tal et al [17] studied and compared various robust optimization problems according to the theory of convex risk measure, which shows that different robustness settings can result in different performances. Kawas and Thiele [18] developed a log robust portfolio model to consider the heavytailed property of stock prices. However, these discussed literatures comparing a robust method with a nominal one are based on only one-time period analysis, in-sample analysis or simulation experiments which might mislead comparison result of two methods.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

A robust mean absolute deviation model for portfolio optimization

Moon

Yao

2011

Computers & Operations Research

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“…There is now an extensive volume of methods and applications available for portfolio optimization (e.g., Ehrgott et al 2004;Mulvey 2004;Boyle et al 2008;Anagnostopoulos and Mamanis 2010;Oliveira et al 2011;Mansini et al 2014;Bekiros et al 2015;Tofighian et al 2018). While some of the existing methods include robustness in the portfolio optimization framework (e.g., Kawas and Thiele 2008;DeMiguel and Nogales 2009;Delage and Ye 2010;Chen et al 2011;Zymler et al 2011), most of the existing methods for portfolio optimization can deal with only aleatory uncertainty. A few methods exist that deal with both aleatory uncertainty and epistemic uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Robustness-based portfolio optimization under epistemic uncertainty

Asadujjaman

Zaman

2018

J Ind Eng Int

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In this paper, we propose formulations and algorithms for robust portfolio optimization under both aleatory uncertainty (i.e., natural variability) and epistemic uncertainty (i.e., imprecise probabilistic information) arising from interval data. Epistemic uncertainty is represented using two approaches: (1) moment bounding approach and (2) likelihood-based approach. This paper first proposes a nested robustness-based portfolio optimization formulation using the moment bounding approachbased representation of epistemic uncertainty. The nested robust portfolio formulation is simple to implement; however, the computational cost is often high due to the epistemic analysis performed inside the optimization loop. A decoupled approach is then proposed to un-nest the robustness-based portfolio optimization from the analysis of epistemic variables to achieve computational efficiency. This paper also proposes a single-loop robust portfolio optimization formulation using the likelihood-based representation of epistemic uncertainty that completely separates the epistemic analysis from the portfolio optimization framework and thereby achieves further computational efficiency. The proposed robust portfolio optimization formulations are tested on real market data from five S&P 500 companies, and performance of the robust optimization models is discussed empirically based on portfolio return and risk.

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A log-robust optimization approach to portfolio management

Cited by 31 publications

References 15 publications

Robust Portfolio Selection

Robust Portfolio Selection

A robust mean absolute deviation model for portfolio optimization

Robustness-based portfolio optimization under epistemic uncertainty

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