Estimation for Markowitz Efficient Portfolios

Jobson, J. D.; Korkie, Bob

doi:10.2307/2287643

Cited by 175 publications

(159 citation statements)

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“…While E(R t ) can be estimated accurately by the sample mean for the sample size of our interest, the sample covariance matrix can be a very inaccurate estimate of V ar(R t ) when the number of observation is not large enough relative to the number of portfolios, as pointed out by Jobson and Korkie (1980). In our case, with 25 portfolios, G has (26 × 25)/2 = 325 elements.…”

Section: Simulation Resultsmentioning

confidence: 84%

Improvement in finite sample properties of the Hansen–Jagannathan distance test

Ren

Shimotsu

2009

Journal of Empirical Finance

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Jagannathan and Wang (1996) derive the asymptotic distribution of the Hansen-Jagannathan distance (HJ-distance) proposed by Hansen and Jagannathan (1997), and develop a specification test of asset pricing models based on the HJ-distance. While the HJ-distance has several desirable properties, Ahn and Gadarowski (2004) find that the specification test based on the HJ-distance overrejects correct models too severely in commonly used sample size to provide a valid test. This paper proposes to improve the finite sample properties of the HJ-distance test by applying the shrinkage method (Ledoit and Wolf, 2003) to compute its weighting matrix. The proposed method improves the finite sample performance of the HJ-distance test significantly.

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Section: Simulation Resultsmentioning

confidence: 84%

Improvement in finite sample properties of the Hansen–Jagannathan distance test

Ren

Shimotsu

2009

Journal of Empirical Finance

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“…In an early study Jobson and Korkie (1980) Nevertheless, the results of Kirby and Ostdiek (2012) reveal that a high turnover mostly erodes the benefits of MV optimization when transaction costs are included. These findings might explain why the naïve diversification approach experiences an increasing interest among academicians and practitioners alike.…”

Section: Naïve Diversification: 1/nmentioning

confidence: 98%

Multi-Asset Portfolio Optimization and Out-of-Sample Performance: An Evaluation of Black-Litterman, Mean Variance and Naïve Diversification Approaches

2012

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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. The Black-Litterman (BL) model aims to enhance asset allocation decisions by overcoming the weaknesses of standard mean-variance (MV) portfolio optimization. In this study we implement the BL model in a multi-asset portfolio context. Using an investment universe of global stock indices, bonds, and commodities, we empirically test the out-of-sample portfolio performance of BL optimized portfolios and compare the results to mean-variance (MV), minimum-variance, and naïve diversified portfolios (1/N-rule) for the period from January 1993 to December 2011. We find that BL optimized portfolios perform better than MV and naïve diversified portfolios in terms of out-of-sample Sharpe ratios even after controlling for different levels of risk aversion, realistic investment constraints, and transaction costs. Interestingly, the BL approach is well suited to alleviate most of the shortcomings of MV optimization. The resulting portfolios are less risky, are more diversified across asset classes, and have less extreme asset allocations. Sensitivity analyses indicate that the outperformance of the BL model is due to the consideration of the reliability of return estimates and a lower portfolio turnover. Terms of use: Documents in

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“…The new estimator is in general much better than the plug-in rule. Jobson et al (1979) and Jobson and Korkie (1980) also analyze shrinkage estimators for the mean-variance frontier. Ledoit and Wolf (2003) develop shrinkage covariance matrix estimators that are useful for a large number of assets.…”

Section: Optimal Estimationmentioning

confidence: 99%

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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Estimation for Markowitz Efficient Portfolios

Cited by 175 publications

References 0 publications

Improvement in finite sample properties of the Hansen–Jagannathan distance test

Improvement in finite sample properties of the Hansen–Jagannathan distance test

Multi-Asset Portfolio Optimization and Out-of-Sample Performance: An Evaluation of Black-Litterman, Mean Variance and Naïve Diversification Approaches

Robust portfolios: contributions from operations research and finance

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