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Robust Portfolio Optimization

In: Developments in Robust Statistics

Author

Listed:
  • G. J. Lauprete

    (Deutsche Bank)

  • A. M. Samarov

    (Sloan School of Management, Massachusetts Institute of Technology)

  • R. E. Welsch

    (Sloan School of Management, Massachusetts Institute of Technology)

Abstract

Summary 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.

Suggested Citation

  • G. J. Lauprete & A. M. Samarov & R. E. Welsch, 2003. "Robust Portfolio Optimization," Springer Books, in: Rudolf Dutter & Peter Filzmoser & Ursula Gather & Peter J. Rousseeuw (ed.), Developments in Robust Statistics, pages 235-245, Springer.
    • Handle: RePEc:spr:sprchp:978-3-642-57338-5_20
    DOI: 10.1007/978-3-642-57338-5_20
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