BazEkon - Biblioteka Główna Uniwersytetu Ekonomicznego w Krakowie

BazEkon home page

Meny główne

Al Wakil Anmar (University of Paris-Est, France)
A Probabilistic-Based Portfolio Resampling Under the Mean-Variance Criterion
Econometric Research in Finance, 2021, vol. 6, nr 1, s. 45-56, rys., wykr., bibliogr. 17 poz.
Słowa kluczowe
Metody portfelowe, Teoria portfelowa Markowitza, Estymacja
Portfolio methods, Markowitz portfolio theory, Estimation
JEL classification: G11, C14, C15
An abundant amount of literature has documented the limitations of traditional unconstrained mean-variance optimization and Efficient Frontier (EF) considered as an estimation-error maximization that magnifies errors in parameter estimates. Originally introduced by Michaud (1998), empirical superiority of portfolio resampling supposedly lies in the addressing of parameter uncertainty by averaging forecasts that are based on a large number of bootstrap replications. Nevertheless, averaging over resampled portfolio weights in order to obtain the unique Resampled Efficient Frontier (REF, U.S. patent number 6,003,018) has been documented as a debated statistical procedure. Alternatively, we propose a probabilistic extension of the Michaud resampling that we introduce as the Probabilistic Resampled Efficient Frontier (PREF). The originality of this work lies in addressing the information loss in the REF by proposing a geometrical three-dimensional representation of the PREF in the mean-variance-probability space. Interestingly, this geometrical representation illustrates a confidence region around the naive EF associated to higher probabilities; in particular for simulated Global-Mean-Variance portfolios. Furthermore, the confidence region becomes wider with portfolio return, as is illustrated by the dispersion of simulated Maximum-Mean portfolios.(original abstract)
Dostępne w
Biblioteka Szkoły Głównej Handlowej w Warszawie
Pełny tekst
  1. Breiman, L. (1996). Bagging Predictors. "Machine Learning", 24:123-140.
  2. Da Silva, A. S., Lee, W., and Pornrojnangkool, B. (2009). The Black-Litterman Model for Active Portfolio Management. "Journal of Portfolio Management", 35(2):61-70.
  3. Efron, B. (2005). Bayesians, Frequentists, and Scientists. "Journal of the American Statistical Association", 100(469):1-5.
  4. Frahm, G. (2015). A Theoretical Foundation of Portfolio Resampling. "Theory and Decision", 79(1):107-232.
  5. Hill, P. (1985). Kernel Estimation of a Distribution Function." Communications in Statistics - Theory and Methods", 14(3):605-620.
  6. Jobson, D. Korkie, B. (1981). Putting Markowitz Theory to Work. "Journal of Portfolio Management", 7(4):70-74.
  7. Knight, F. H. (1921). Uncertainty and Profit. Hart, Schaffner and Marx.
  8. Markowitz, H. (1952). Portfolio Selection." Journal of Finance", 7(1):77-91.
  9. Markowitz, H. Usman, N. (2003). Resampled Frontiers versus Diffuse Bayes. "Journal of Investment Management", 1:1-17.
  10. Michaud, R. O. (1989). The Markowitz Optimization Enigma: Is 'Optimized' Optimal? "Financial Analysts Journal", 45(1):31-42.
  11. Michaud, R. O. (1998). Efficient Asset Management: A Practical Guide to Stock Portfolio Optimization and Asset Allocation. McGraw-Hill.
  12. Michaud, R. O. Michaud, R. (2003). An Examination of Resampled Portfolio Efficiency: A Comment and Response. "Financial Analysts Journa"l, 59:15-16.
  13. Michaud, R. O. Michaud, R. (2008). Efficient Asset Management: A Practical Guide to Stock Portfolio Optimization and Asset-Allocation. Oxford University Press, New York, 2nd edition.
  14. Mossin, J. (1968). Optimal Multiperiod Portfolio Policies." The Journal of Business", 41:215-229.
  15. Politis, D. White, H. (2004). Automatic Block-Length Selection for the Dependent Bootstrap." Econometric Reviews", 23(1):53-70.
  16. Scherer, B. (2002). Portfolio Resampling: Review and Critique. "Financial Analysts Journal", 58(6):98-109.
  17. Wolf, M. (2007). Resampling vs. Shrinkage for Benchmarked Managers." Wilmott Magazine", 24:76-81.
Cytowane przez
Udostępnij na Facebooku Udostępnij na Twitterze Udostępnij na Google+ Udostępnij na Pinterest Udostępnij na LinkedIn Wyślij znajomemu