Metrika

, Volume 55, Issue 1–2, pp 139–149 | Cite as

Robust portfolio optimization

  • G. J. Lauprete
  • A. M. Samarov
  • R. E. Welsch

Abstract.

We address the problem of estimating risk-minimizing portfolios from a sample of historical returns, when the underlying distribution that generates returns exhibits departures from the standard Gaussian assumption. Specifically, we examine how the underlying estimation problem is influenced by marginal heavy tails, as modeled by the univariate Student-t distribution, and multivariate tail-dependence, as modeled by the copula of a multivariate Student-t distribution. We show that when such departures from normality are present, robust alternatives to the classical variance portfolio estimator have lower risk.

Key words: Portfolio Optimization Robustness Shortfall Copula Dependence 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • G. J. Lauprete
    • 1
  • A. M. Samarov
    • 2
  • R. E. Welsch
    • 2
  1. 1.Deutsche Bank, 5th floor, Winchester House, 1 Great Winchester Street, LONDON EC2N2EQ, UKGB
  2. 2.Massachusetts Institute of Technology, Sloan School of Management, 50 Memorial Drive, E53-383, Cambridge, MA 02142 USAUS

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