When do improved covariance matrix estimators enhance portfolio optimization? An empirical comparative study of nine estimators

Quantitative Finance

Volumn 11, Issue 7, 2011, Pages 1067-1080

  Pantaleo, Ester ^a   Tumminello, Michele ^b,c   Lillo, Fabrizio ^b,d,e   Mantegna, Rosario N ^a  

^a UNIVERSITY OF BARI   (Italy)

^b UNIVERSITY OF PALERMO   (Italy)

^c CARNEGIE MELLON UNIVERSITY   (United States)

^d SANTA FE INSTITUTE   (United States)

^e SCUOLA NORMALE SUPERIORE   (Italy)

Author keywords

Correlation structures; Econophysics; Portfolio optimization; Statistical methods

Indexed keywords

EID: 79960690675     PISSN: 14697688     EISSN: 14697696     Source Type: Journal    
DOI: 10.1080/14697688.2010.534813     Document Type: Article

References (33)

1. Anderberg, M.R., Cluster Analysis for Applications, 1973 (Academic Press: New York).
2. Basalto, N., Bellotti, R., De Carlo, F., Facchi, P., Pantaleo, E. and Pascazio, S., Hausdorff clustering. Phys. Rev. E, 2008, 78, 046112.
3. Best, M.J. and Grauer, R.R., Positively weighted minimumvariance portfolios and the structure of asset expected returns. J. Financial Quant. Anal., 1992, 27, 513-537.
4. Bouchaud, J.-P. and Potters, M., Theory of Financial Risk and Derivative Pricing, 2nd ed., 2003 (Cambridge University Press: Cambridge).
5. Campbell, Y.J., Lo, A.W. and Mackinlay, A.C., The Econometrics of Financial Markets, 1997 (Princeton University Press: Princeton).
6. Elton, E.J. and Gruber, M.J., Modern Portfolio Theory and Investment Analysis, 1995 (Wiley: New York).
7. Green, R.C. and Hollifield, B., When will mean-variance efficient portfolios be well diversified? J. Finance, 1992, 47, 1785-1809.
8. Ingersoll, J.E., Theory of Financial Decision Making, 1987 (Rowman & Littlefield: Savage, MD).
9. Jagannathan, R. and Ma, T., Risk reduction in large portfolios: Why imposing the wrong constraints helps. J. Finance, 2003, 58, 1641-1684.
10. Jorion, P., International portfolio diversification with estimation risk. J. Business, 1985, 58, 259-278.
11. Kondor, I., Pafka, S. and Nagy, G., Noise sensitivity of portfolio selection under various risk measures. J. Bank. Finance, 2007, 31, 1545-1573.
12. Laloux, L., Cizeau, P., Bouchaud, J.-P. and Potters, M., Noise dressing of financial correlation matrices. Phys. Rev. Lett., 1999, 83, 1467-1470.
13. Ledoit, O. and Wolf, M., Improved estimation of the covariance matrix of stock returns with an application to portfolio selection. J. Empir. Finance, 2003, 10, 603-621.
14. Ledoit, O. and Wolf, M., A well-conditioned estimator for large-dimensional covariance matrices. J. Mult. Anal., 2004a, 88, 365-411.
15. Ledoit, O. and Wolf, M., Honey, I shrunk the sample covariance matrix. J. Portfol. Mgmt, 2004b, 30, 110-119.
16. Mantegna, R.N., Hierarchical structure in financial markets. Eur. Phys. J. B, 1999, 11, 193-197.
17. Mardia, K.V., Kent, J.T. and Bibby, J.M., Multivariate Analysis, 1979 (Academic Press: San Diego, CA).
18. Markowitz, H., Portfolio selection. J. Finance, 1952, 7, 77-91.
19. Markowitz, H., Portfolio Selection: Efficient Diversification of Investment, 1959 (Wiley: New York).
20. Metha, M.L., Random Matrices, 1990 (Academic Press: New York).
21. Pafka, S. and Kondor, I., Noisy covariance matrices and portfolio optimization. Eur. Phys. J. B, 2002, 27, 277-280.
22. Pafka, S. and Kondor, I., Noisy covariance matrices and portfolio optimisation II. Physica A, 2003, 319, 487-494.
23. Papp, G., Pafka, S., Nowak, M. and Kondor, I., Random matrix filtering in portfolio optimization. Acta Phys. Pol. B, 2005, 36, 2757-2765.
24. Plerou, V., Gopikrishnan, P., Rosenow, B., Amaral, L.A.N. and Stanley, H.E., Universal and nonuniversal properties of cross correlations in financial time series. Phys. Rev. Lett., 1999, 83, 1471-1474.
25. Potters, M., Bouchaud, J.-P. and Laloux, L., Financial applications of random matrix theory: Old laces and new pieces. Acta Phys. Pol. B, 2005, 36, 2767-2784.
26. Rosenow, B., Plerou, V., Gopikrishnan, P. and Stanley, H.E., Portfolio optimization and the random magnet problem. Europhys. Lett., 2002, 59, 500-506.
27. Schäfer, R., Nilsson, N.F. and Guhr, T., Power mapping with dynamical adjustment for improved portfolio optimization. Quant. Finance, 2010, 10, 107-119.
28. Schäfer, J. and Strimmer, K., Ashrinkage approach to large-scale covariance matrix estimation and implications for functional genomics. Stat. Appl. Gen. Mol. Biol., 2005, 4, Art. 32.
29. Stein, C., Inadmissibility of the usual estimator for the mean of a multivariate normal distribution, in Proceedings of the Third Berkley Symposium on Mathematical Statististical Probability, Vol. 1, 1956, pp. 197-206.
30. Tola, V., Lillo, F., Gallegati, M. and Mantegna, R.N., Cluster analysis for portfolio optimization. J. Econ. Dynam. Control, 2008, 32, 235-258.
31. Tumminello, M., Coronnello, C., Lillo, F., Miccichè, S. and Mantegna, R.N., Spanning trees and bootstrap reliability estimation in correlation-based networks. Int. J. Bifurcation Chaos, 2007a, 17, 2319-2329.
32. Tumminello, M., Lillo, F. and Mantegna, R.N., Hierarchically nested factor model from multivariate data. EPL, 2007b, 78, 30006.
33. Tumminello, M., Lillo, F. and Mantegna, R.N., Correlation, hierarchies, and networks in financial markets. J. Econ. Behav. Organiz., 2010, 75, 40-58.