Nonlinear shrinkage of the covariance matrix for portfolio selection: Markowitz meets Goldilocks
AbstractMarkowitz (1952) portfolio selection requires estimates of (i) the vector of expected returns and (ii) the covariance matrix of returns. Many proposals to address the first question exist already. This paper addresses the second question. We promote a new nonlinear shrinkage estimator of the covariance matrix that is more flexible than previous linear shrinkage estimators and has 'just the right number' of free parameters (that is, the Goldilocks principle). In a stylized setting, the nonlinear shrinkage estimator is asymptotically optimal for portfolio selection. In addition to theoretical analysis, we establish superior real-life performance of our new estimator using backtest exercises.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Department of Economics - University of Zurich in its series ECON - Working Papers with number 137.
Date of creation: Jan 2014
Date of revision:
Large-dimensional asymptotics; Markowitz portfolio selection; nonlinear shrinkage;
Find related papers by JEL classification:
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
This paper has been announced in the following NEP Reports:
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Olivier Ledoit & Michael Wolf, 2013. "Spectrum estimation: a unified framework for covariance matrix estimation and PCA in large dimensions," ECON - Working Papers 105, Department of Economics - University of Zurich, revised Jul 2013.
- Oliver Ledoit & Michael Wolf, 2008.
"Robust Performance Hypothesis Testing with the Sharpe Ratio,"
IEW - Working Papers
320, Institute for Empirical Research in Economics - University of Zurich.
- Ledoit, Oliver & Wolf, Michael, 2008. "Robust performance hypothesis testing with the Sharpe ratio," Journal of Empirical Finance, Elsevier, vol. 15(5), pages 850-859, December.
- Victor DeMiguel & Lorenzo Garlappi & Francisco J. Nogales & Raman Uppal, 2009. "A Generalized Approach to Portfolio Optimization: Improving Performance by Constraining Portfolio Norms," Management Science, INFORMS, vol. 55(5), pages 798-812, May.
- Gabriel Frahm & Christoph Memmel, 2010.
"Dominating Estimators for Minimum-Variance Portfolios,"
- Frahm, Gabriel & Memmel, Christoph, 2010. "Dominating estimators for minimum-variance portfolios," Journal of Econometrics, Elsevier, vol. 159(2), pages 289-302, December.
- Gabriel Frahm & Christoph Memmel, 2010. "Dominating Estimators for Minimum-Variance Portfolios," Post-Print peer-00741629, HAL.
- Michael W. Brandt & Pedro Santa-Clara & Rossen Valkanov, 2009.
"Parametric Portfolio Policies: Exploiting Characteristics in the Cross-Section of Equity Returns,"
Review of Financial Studies,
Society for Financial Studies, vol. 22(9), pages 3411-3447, September.
- Michael W. Brandt & Pedro Santa-Clara & Rossen Valkanov, 2004. "Parametric Portfolio Policies: Exploiting Characteristics in the Cross Section of Equity Returns," NBER Working Papers 10996, National Bureau of Economic Research, Inc.
- Olivier Ledoit & Michael Wolf, 2013. "Optimal estimation of a large-dimensional covariance matrix under Stein’s loss," ECON - Working Papers 122, Department of Economics - University of Zurich, revised Dec 2013.
- Tu, Jun & Zhou, Guofu, 2011. "Markowitz meets Talmud: A combination of sophisticated and naive diversification strategies," Journal of Financial Economics, Elsevier, vol. 99(1), pages 204-215, January.
- Ledoit, Olivier & Wolf, Michael, 2004. "A well-conditioned estimator for large-dimensional covariance matrices," Journal of Multivariate Analysis, Elsevier, vol. 88(2), pages 365-411, February.
- Jegadeesh, Narasimhan & Titman, Sheridan, 1993. " Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency," Journal of Finance, American Finance Association, vol. 48(1), pages 65-91, March.
- Olivier Ledoit & Michael Wolf, 2001.
"Improved estimation of the covariance matrix of stock returns with an application to portofolio selection,"
Economics Working Papers
586, Department of Economics and Business, Universitat Pompeu Fabra.
- Ledoit, Olivier & Wolf, Michael, 2003. "Improved estimation of the covariance matrix of stock returns with an application to portfolio selection," Journal of Empirical Finance, Elsevier, vol. 10(5), pages 603-621, December.
- Ravi Jagannathan & Tongshu Ma, 2002.
"Risk Reduction in Large Portfolios: Why Imposing the Wrong Constraints Helps,"
NBER Working Papers
8922, National Bureau of Economic Research, Inc.
- Ravi Jagannathan & Tongshu Ma, 2003. "Risk Reduction in Large Portfolios: Why Imposing the Wrong Constraints Helps," Journal of Finance, American Finance Association, vol. 58(4), pages 1651-1684, 08.
- Ross, Stephen A., 1976. "The arbitrage theory of capital asset pricing," Journal of Economic Theory, Elsevier, vol. 13(3), pages 341-360, December.
- Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, 03.
- Kan, Raymond & Zhou, Guofu, 2007. "Optimal Portfolio Choice with Parameter Uncertainty," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 42(03), pages 621-656, September.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Marita Kieser).
If references are entirely missing, you can add them using this form.