Abstract
The main goal of this chapter is to formulate a robust mean-semivariance portfolio selection problem in terms of a linear matrix inequalities (LMI) optimization problem. We consider different forms of calculating the mean and semivariance of the tracking error. It is desired to minimize an objective function defined as a convex combination of the risk function minus the expected return of the tracking error.
Partial funding provided by FAPESP grant 97/04668-1, CNPq grant 305173/88 and PRONEX grant 015/98.
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Costa, O.L.V., de Barros Nabholz, R. (2002). A Linear Matrix Inequalities Approach to Robust Mean-Semivariance Portfolio Optimization. In: Kontoghiorghes, E.J., Rustem, B., Siokos, S. (eds) Computational Methods in Decision-Making, Economics and Finance. Applied Optimization, vol 74. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-3613-7_6
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DOI: https://doi.org/10.1007/978-1-4757-3613-7_6
Publisher Name: Springer, Boston, MA
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