Dominating estimators for the global minimum variance portfolio
AbstractIn this paper, we derive two shrinkage estimators for the global minimum variance portfolio that dominate the traditional estimator with respect to the out-of-sample variance of the portfolio return. The presented results hold for any number of observations n ≥ d + 2 and number of assets d ≥ 4 . The small-sample properties of the shrinkage estimators as well as their large-sample properties for fixed d but n → ∞ as well as n/d → ∞ but n/d → q ≤ ∞ are investigated. Furthermore, we present a small-sample test for the question of whether it is better to completely ignore time series information in favor of naive diversification. --
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Bibliographic InfoPaper provided by University of Cologne, Department for Economic and Social Statistics in its series Discussion Papers in Statistics and Econometrics with number 2/08.
Date of creation: 2008
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Covariance matrix estimation; Global minimum variance portfolio; James-Stein estimation; Naive diversification; Shrinkage estimator;
Other versions of this item:
- Frahm, Gabriel & Memmel, Christoph, 2009. "Dominating estimators for the global minimum variance portfolio," Discussion Paper Series 2: Banking and Financial Studies 2009,01, Deutsche Bundesbank, Research Centre.
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
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- Alexander Kempf & Christoph Memmel, 2006. "Estimating the global Minimum Variance Portfolio," Schmalenbach Business Review (sbr), LMU Munich School of Management, vol. 58(4), pages 332-348, October.
- Sharpe, William F., 1967. "Portfolio Analysis," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 2(02), pages 76-84, June.
- Okhrin, Yarema & Schmid, Wolfgang, 2006. "Distributional properties of portfolio weights," Journal of Econometrics, Elsevier, vol. 134(1), pages 235-256, September.
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