Abstract
Abstract We show how to formulate and solve robust portfolio selection problems. The objective of these robust formulations is to systematically combat the sensitivity of the optimal portfolio to statistical and modeling errors in the estimates of the relevant market parameters. We introduce “uncertainty structures” for the market parameters and show that the robust portfolio selection problems corresponding to these uncertainty structures can be reformulated as second‐order cone programs and, therefore, the computational effort required to solve them is comparable to that required for solving convex quadratic programs. We also survey recent literature on robust portfolio selection.
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