Large-scale MV efficient frontier computation via a procedure of parametric quadratic programming

Markus Hirschberger, Yue Qi and Ralph E. Steuer

European Journal of Operational Research, 2010, vol. 204, issue 3, 581-588

Abstract: Despite the volume of research conducted on efficient frontiers, in many cases it is still not the easiest thing to compute a mean-variance (MV) efficient frontier even when all constraints are linear. This is particularly true of large-scale problems having dense covariance matrices and hence they are the focus in this paper. Because standard approaches for constructing an efficient frontier one point at a time tend to bog down on dense covariance matrix problems with many more than about 500 securities, we propose as an alternative a procedure of parametric quadratic programming for more effective usage on large-scale applications. With the proposed procedure we demonstrate through computational results on problems in the 1000-3000 security range that the efficient frontiers of dense covariance matrix problems in this range are now not only solvable, but can actually be computed in quite reasonable time.

Keywords: Bi-criterion; Portfolio; selection; Parametric; quadratic; programming; Efficient; frontiers; Dense; covariance; matrices; Large-scale; Hyperbolic; segments (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (15)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:204:y:2010:i:3:p:581-588

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