Robustness properties of mean-variance portfolios

Schöttle, Katrin; Werner, Ralf

doi:10.1080/02331930902819220

Cited by 26 publications

(15 citation statements)

References 13 publications

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“…This approach is widely used to deal with sampling errors in portfolio management (see, e.g., Ben-Tal and Nemirovski, 1998;Fabozzi et al, 2007). Here we only focus on the expected returns as the fluctuations in the covariance matrix do not significantly influence the optimal solution (Chopra and Ziemba, 1993;Schöttle and Werner, 2009;Ziemba;2009).…”

Section: Data Heterogeneity: a Robust Counterpart Approachmentioning

confidence: 99%

“…Our second strand of research is the robust counterpart approach. Recent studies reveal that parameter estimates based on historical market data are subject to sampling errors (Chopra and Ziemba, 1993;Schöttle and Werner, 2009;Fernandes et al, 2012;de Jong, 2018). The robust counterpart approach is a useful alternative because it includes a wide range of possible input parameter values without complicated adjustment to the original optimisation framework (Ben-Tal and Nemirovski, 1998;Gregory et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

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Asset allocation with multiple analysts’ views: a robust approach

Tee

2019

J Asset Manag

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Retail investors often make decisions based on professional analysts' investment recommendations.Although these recommendations contain up-to-date financial information, they are usually expressed in sophisticated but vague forms. In addition, the quality differs from analyst to analyst and recommendations may even be mutually conflicting. This paper addresses these issues by extending the Black-Litterman (BL) method, and developing a multi-analyst portfolio selection method, balanced against any over-optimistic forecasts. Our methods accommodate analysts' ambiguous investment recommendations and the heterogeneity of data from disparate sources. We prove the validity of our model, using an empirical analysis of around 1000 daily financial newsletters collected from two top-10 Taiwanese brokerage firms over a two-year period. We conclude that analysts' views contribute to the investment allocation process and enhance the portfolio performance. We confirm that the degree of investors' confidence in these views influences the portfolio outcome, thus extending the idea of the BL model and improving the practicality of robust optimisation.

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Section: Data Heterogeneity: a Robust Counterpart Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Asset allocation with multiple analysts’ views: a robust approach

Tee

2019

J Asset Manag

View full text Add to dashboard Cite

show abstract

“…Through introducing uncertainty structures, the authors show that the robust counterpart is a second order cone program. This problem has been further discussed in [11] where two different uncertainty sets are introduced for the uncertainties of input parameters.…”

mentioning

confidence: 99%

“…In this joint set,ξ t andΣ t are calculated through the available samples. As in [11], we introduce a dummy variable κ and define the following two sets:…”

mentioning

confidence: 99%

Robust multi-period and multi-objective portfolio selection

Jiang¹,

Wang²

2021

Journal of Industrial &Amp; Management Optimization

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In this paper, a multi-period multi-objective portfolio selection problem with uncertainty is studied. Under the assumption that the uncertainty set is ellipsoidal, the robust counterpart of the proposed problem can be transformed into a standard multi-objective optimization problem. A weighted-sum approach is then introduced to obtain Pareto front of the problem. Numerical examples will be presented to illustrate the proposed method and validate the effectiveness and efficiency of the model developed.

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“…It has been applied with success to single-period portfolio optimization; see, e.g., [18][19][20][21]. The usual approach is to choose uncertainty sets that lead to tractable convex programming problems that are solved numerically.…”

Section: Introductionmentioning

confidence: 99%

Mean semi-deviation from a target and robust portfolio choice under distribution and mean return ambiguity

Pınar

Paç

2014

Journal of Computational and Applied Mathematics

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a b s t r a c tWe consider the problem of optimal portfolio choice using the lower partial moments risk measure for a market consisting of n risky assets and a riskless asset. For when the mean return vector and variance/covariance matrix of the risky assets are specified without specifying a return distribution, we derive distributionally robust portfolio rules. We then address potential uncertainty (ambiguity) in the mean return vector as well, in addition to distribution ambiguity, and derive a closed-form portfolio rule for when the uncertainty in the return vector is modelled via an ellipsoidal uncertainty set. Our result also indicates a choice criterion for the radius of ambiguity of the ellipsoid. Using the adjustable robustness paradigm we extend the single-period results to multiple periods, and derive closed-form dynamic portfolio policies which mimic closely the single-period policy.

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Robustness properties of mean-variance portfolios

Cited by 26 publications

References 13 publications

Asset allocation with multiple analysts’ views: a robust approach

Asset allocation with multiple analysts’ views: a robust approach

Robust multi-period and multi-objective portfolio selection

Mean semi-deviation from a target and robust portfolio choice under distribution and mean return ambiguity

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