Robust min–max portfolio strategies for rival forecast and risk scenarios

Rustem, Berç; Becker, Robin; Marty, Wolfgang

doi:10.1016/s0165-1889(99)00088-3

Cited by 68 publications

(37 citation statements)

References 23 publications

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“…Some are superimposed on top of factor models for returns (Goldfarb and Iyengar 2003b), while others consider confidence intervals for the individual covariance matrix entries (Tütüncü and Koenig 2004). Benefits for portfolio performance have been observed even when the uncertainty set is defined simply as a collection of several possible scenarios for the covariance matrix (Rustem et al 2000;Costa and Paiva 2002).…”

Section: Portfolio With Unknown Mean and Covariancementioning

confidence: 99%

“…This robust technique has obtained prodigious success since the late 1990s, especially in the field of optimization and control with uncertainty parameters Nemirovski 1998, 1999;El Ghaoui and Lebret 1997;Goldfarb and Iyengar 2003a). With respect to portfolio selection, the major contributions have come in the 21st century (see, for example, Rustem et al 2000;Costa and Paiva 2002;Ben-Tal et al 2002;Goldfarb and Iyengar 2003b;El Ghaoui et al 2003;Tütüncü and Koenig 2004;Pinar and Tütüncü 2005;Lutgens and Schotman 2006;Natarajan et al 2009;Garlappi et al 2007;Pinar 2007;Calafiore 2007;Huang et al 2008;Natarajan et al 2008a;Brown and Sim 2008;Natarajan et al 2008b;Shen and Zhang 2008;Elliott and Siu 2008;Zhu and Fukushima 2008). For a complete discussion of robust portfolio management and the associated solution methods, see Fabozzi et al (2007), Föllmer et al (2008), and the references therein.…”

Section: Introductionmentioning

confidence: 99%

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Robust portfolios: contributions from operations research and finance

2009

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In this paper we provide a survey of recent contributions to robust portfolio strategies from operations research and finance to the theory of portfolio selection. Our survey covers results derived not only in terms of the standard mean-variance objective, but also in terms of two of the most popular risk measures, mean-VaR and mean-CVaR developed recently. In addition, we review optimal estimation methods and Bayesian robust approaches.

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Section: Portfolio With Unknown Mean and Covariancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust portfolios: contributions from operations research and finance

2009

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“…Pastor (2000), Pástor (2000), Pástor and Stambaugh (2000)), Bayesian approaches with priors based on economic objectives (Tu and Zhou (2010)), shrinkage approaches (Ledoit and Wolf (2004b)), robust optimization methods (Cornuejols and Tutuncu (2007), Goldfarb and Iyengar (2003), Garlappi, Uppal and Wang (2007), Rustem, Becker and Marty (2000), Tutuncu and Koeing (2004)), Bayesian robust optimization (Wang (2005)), meanvariance timing rules (Kirby and Ostdiek (2012)) and methods based on imposing constraints (Best and Grauer (1992), Jagannathan and Ma (2003), and DeMiguel, Garlappi, Nogales and Uppal (2009)). Kan and Zhou (2007) characterize analytically the utility loss of a mean-variance investor who suffers from parameter uncertainty.…”

mentioning

confidence: 99%

Parameter Uncertainty in Multiperiod Portfolio Optimization with Transaction Costs

DeMiguel¹,

Martín-Utrera²,

Nogales³

2015

J. Financ. Quant. Anal.

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We study the impact of parameter uncertainty on the expected utility of a multiperiod investor subject to quadratic transaction costs. We characterize the utility loss associated with ignoring parameter uncertainty, and show that it is equal to the product between the single-period utility loss and another term that captures the effects of the multiperiod mean-variance utility and transaction cost losses. To mitigate the impact of parameter uncertainty, we propose two multiperiod shrinkage portfolios and demonstrate with simulated and empirical data sets that they substantially outperform portfolios that ignore parameter uncertainty, transaction costs, or both.

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“…It has been applied to portfolio optimization and asset management, allowing for the uncertainty that occurs due to estimation errors in the input parameters, e.g. Ben-Tal and Nemirovski (1999), Rustem et al (2000), Ceria and Stubbs (2006) and Bertsimas and Pachamanova (2008). Robust optimization recognises that the market parameters of an ALM model are stochastic, but lie within uncertainty sets (e.g.…”

Section: Introductionmentioning

confidence: 99%

Asset Liability Modelling and Pension Schemes: The Application of Robust Optimization to USS

Platanakis

Sutcliffe

2014

SSRN Journal

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Asset Liability Modelling and Pension Schemes: the Application of Robust Optimization to USS AbstractThis paper uses a novel numerical optimization technique -robust optimization -that is well suited to solving the asset-liability management (ALM) problem for pension schemes. It requires the estimation of fewer stochastic parameters, reduces estimation risk and adopts a prudent approach to asset allocation. This study is the first to apply it to a real-world pension scheme, and the first ALM model of a pension scheme to maximise the Sharpe ratio. We disaggregate pension liabilities into three components -active members, deferred members and pensioners, and transform the optimal asset allocation into the scheme's projected contribution rate. The robust optimization model is extended to include liabilities and used to derive optimal investment policies for the Universities Superannuation Scheme (USS), benchmarked against the Sharpe and Tint, BayesStein, and Black-Litterman models as well as the actual USS investment decisions. Over a 144 month out-of-sample period robust optimization is superior to the four benchmarks across 20 performance criteria, and has a remarkably stable asset allocation -essentially fix-mix. These conclusions are supported by six robustness checks.

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Robust min–max portfolio strategies for rival forecast and risk scenarios

Cited by 68 publications

References 23 publications

Robust portfolios: contributions from operations research and finance

Robust portfolios: contributions from operations research and finance

Parameter Uncertainty in Multiperiod Portfolio Optimization with Transaction Costs

Asset Liability Modelling and Pension Schemes: The Application of Robust Optimization to USS

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