Using Constraints to Improve the Robustness of Asset Allocation

Eichhorn, David; Gupta, Francis; Stubbs, Eric

doi:10.3905/jpm.1998.409642

Cited by 24 publications

(13 citation statements)

References 2 publications

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“…Using a simulation setup, Frost/Savarino (1998) show that short-selling and upper weight restrictions lead to an enhanced performance. Eichhorn et al (1998) confirm this result and furthermore come to the conclusion that constraints reduce volatility and shortfall risk. In an out-of-sample study, Grauer/Shen (2000) find realized volatility to decline, but only at the cost of realized return.…”

Section: Restricting Portfolio Weightssupporting

confidence: 74%

Portfolio Choice and Estimation Risk. A Comparison of Bayesian to Heuristic Approaches

Herold¹,

Maurer²

2006

ASTIN Bull.

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Estimation risk is known to have a huge impact on mean/variance optimized portfolios, which is one of the primary reasons to make standard Markowitz optimization unfeasible in practice. This issue has attracted new interest in the last years, and several approaches to incorporate estimation risk into portfolio selection have been developed only recently. In this article, we review these approaches as well as some older ones and compare them in an empirical outof-sample study. The approaches can be classified along two criteria. First, we can differentiate heuristic approaches (restricting portfolio weights and employing simulation techniques) and those based on Bayesian statistics (shrinking the portfolios towards a pre-determined target). Second, the assumptions about the return-generating process differ, either assuming returns to be IID distributed or to be partly predictable. The central result of our empirical study is that all of the IID approaches, whether they account for estimation risk or not, are not superior to simple investment strategies like holding the market portfolio. A riskadjusted outperformance is possible only if sample means are substituted with conditional expected return estimates. Furthermore, the Bayesian approaches reduce turnover and stabilize portfolio weights.

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Section: Restricting Portfolio Weightssupporting

confidence: 74%

Portfolio Choice and Estimation Risk. A Comparison of Bayesian to Heuristic Approaches

Herold¹,

Maurer²

2006

ASTIN Bull.

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“…Jorion, 1985;Garlappi et al, 2007;Black and Litterman, 1992;Da Silva et al, 2009), that should lead to reasonable portfolio compositions. On the other hand, heuristic methods have been developed to achieve this aim, for instance upper-bound constraints on portfolio weights (Frost and Savarino, 1988;Eichhorn et al, 1998) or the concept of Michaud (1998) as well as Michaud and Michaud (2008a). Michaud's "resampled efficiency" is based on a resampling of portfo-lio returns to reflect the uncertainty in the return process and has been widely discussed in the literature (e.g.…”

Section: Introductionmentioning

confidence: 99%

Markowitz versus Michaud: Portfolio Optimization Strategies Reconsidered

Becker¹,

Gürtler

Hibbeln

2009

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. by Franziska Becker*, Marc Gürtler**, and Martin Hibbeln*** Abstract. Several attempts have been made to reduce the impact of estimation errors on the optimal portfolio composition. On the one hand, improved estimators of the necessary moments have been developed and on the other hand, heuristic methods have been generated to enhance the portfolio performance, for instance the "resampled efficiency" of Michaud (1998). We compare the out-ofsample performance of traditional Mean-Variance optimization by Markowitz (1952) with Michaud's resampled efficiency in a comprehensive simulation study for a large number of relevant estimators appearing in the literature. In this context we consider different estimation periods as well as unconstrained and constrained portfolio optimization problems. The main finding of our simu-lation study concerning the optimization approach is that Markowitz outperforms Mi-chaud on average. Furthermore, the estimation strategy of Frost/Savarino (1988) proves to work excellent in all analyzed situations. Terms of use: Documents in

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“…Further, by placing a constraint of non-negative holding (or no short selling), MVO has been shown to result in a slight reduction in performance while stabilizing portfolio weights (Eichhorn et al [1998]; Jagannathan and Ma [2003]). Kolm et al [2014] go further, stating that "the inclusion of constraints in the mean-variance optimization problem can lead to better out-of-sample performance, compared to portfolios constructed without these constraints.…”

Section: E X H I B I T 3 Eta Profiles Of a Mean-variance Optimized Pomentioning

confidence: 97%