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Estimating the global Minimum Variance Portfolio

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Author Info

  • Alexander Kempf
  • Christoph Memmel

Abstract

According to standard portfolio theory, the tangency portfolio is the only efficient stock portfolio. However, empirical studies show that an investment in the global minimum variance portfolio often yields better out-of-sample results than does an investment in the tangency portfolio and suggest investing in the global minimum variance portfolio. But little is known about the distributions of the weights and return parameters of this portfolio. Our contribution is to determine these distributions. By doing so, we answer several important questions in asset management.

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Bibliographic Info

Article provided by LMU Munich School of Management in its journal Schmalenbach Business Review.

Volume (Year): 58 (2006)
Issue (Month): 4 (October)
Pages: 332-348

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Handle: RePEc:sbr:abstra:v:58:y:2006:i:4:p:332-348

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Related research

Keywords: Estimation Risk; Global Minimum Variance Portfolio; Weight Estimation;

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Citations

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Cited by:
  1. Alexander Bade & Gabriel Frahm & Uwe Jaekel, 2009. "A general approach to Bayesian portfolio optimization," Computational Statistics, Springer, vol. 70(2), pages 337-356, October.
  2. Bertrand Maillet & Sessi Tokpavi & Benoit Vaucher, 2013. "Minimum Variance Portfolio Optimisation under Parameter Uncertainty: A Robust Control Approach," EconomiX Working Papers 2013-28, University of Paris West - Nanterre la Défense, EconomiX.
  3. Fliege, Jörg & Werner, Ralf, 2014. "Robust multiobjective optimization & applications in portfolio optimization," European Journal of Operational Research, Elsevier, vol. 234(2), pages 422-433.
  4. Gabriel Frahm & Christoph Memmel, 2010. "Dominating Estimators for Minimum-Variance Portfolios," Post-Print hal-00741629, HAL.
  5. 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.
  6. Frahm, Gabriel, 2010. "An analytical investigation of estimators for expected asset returns from the perspective of optimal asset allocation," Discussion Papers in Statistics and Econometrics 1/10, University of Cologne, Department for Economic and Social Statistics.
  7. Gabriel Frahm, 2010. "Linear statistical inference for global and local minimum variance portfolios," Statistical Papers, Springer, vol. 51(4), pages 789-812, December.
  8. Hao Liu & Winfried Pohlmeier, 2013. "Risk Preferences and Estimation Risk in Portfolio Choice," Working Paper Series 47_13, The Rimini Centre for Economic Analysis.
  9. Gabriel Frahm & Tobias Wickern & Christof Wiechers, 2012. "Multiple tests for the performance of different investment strategies," AStA Advances in Statistical Analysis, Springer, vol. 96(3), pages 343-383, July.
  10. Frahm, Gabriel, 2007. "Linear statistical inference for global and local minimum variance portfolios," Discussion Papers in Statistics and Econometrics 1/07, University of Cologne, Department for Economic and Social Statistics.
  11. Taras Bodnar & Wolfgang Schmid & Taras Zabolotskyy, 2009. "Statistical inference of the efficient frontier for dependent asset returns," Statistical Papers, Springer, vol. 50(3), pages 593-604, June.
  12. Lan, Wei & Wang, Hansheng & Tsai, Chih-Ling, 2012. "A Bayesian information criterion for portfolio selection," Computational Statistics & Data Analysis, Elsevier, vol. 56(1), pages 88-99, January.
  13. Frahm, Gabriel & Wiechers, Christof, 2011. "On the diversification of portfolios of risky assets," Discussion Papers in Statistics and Econometrics 2/11, University of Cologne, Department for Economic and Social Statistics.
  14. Candelon Bertrand & Hurlin Christophe & Tokpavi Sessi, 2011. "Sampling Error and Double Shrinkage Estimation of Minimum Variance Portfolios," Research Memorandum 002, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
  15. Bade, Alexander & Frahm, Gabriel & Jaekel, Uwe, 2008. "A general approach to Bayesian portfolio optimization," Discussion Papers in Statistics and Econometrics 1/08, University of Cologne, Department for Economic and Social Statistics.
  16. Manfred Gilli & Enrico Schumann, 2009. "Robust regression with optimisation heuristics," Working Papers 011, COMISEF.
  17. Taras Bodnar & Nestor Parolya & Wolfgang Schmid, 2014. "Estimation of the Global Minimum Variance Portfolio in High Dimensions," Papers 1406.0437, arXiv.org.

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