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A fast algorithm for solving large scale mean-variance models by compact factorization of covariance matrices. (English) Zbl 0763.90004
Summary: A fast algorithm for solving large scale MV (mean-variance) portfolio optimization problems is proposed. It is shown that by using \(T\) independent data representing the rate of return of the assets, the MV model consisting of \(n\) assets can be put into a quadratic program with \(n+T\) variables, \(T\) linear constraints and \(T\) quadratic terms in the objective function. As a result, the computation time required to solve this problem would increase very mildly as a function of \(n\). This implies that a very large scale MV model can now be solved in a practical amount of time.

MSC:
91B28 Finance etc. (MSC2000)
90C90 Applications of mathematical programming
90C06 Large-scale problems in mathematical programming
90C20 Quadratic programming
90-08 Computational methods for problems pertaining to operations research and mathematical programming
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