Focusing on (strictly) convex multiobjective programs (MOPs), we review some well-established scalarizations in multiobjective programming from the perspective of parametric optimization and propose a modified hybrid scalarization suitable for a class of specially structured convex MOPs. Since multiobjective quadratic programs are a prominent class of convex MOPs due to their broad applicability, we review the state-ofthe-art algorithms for computing their efficient solutions. These two lines of investigation are merged to solve multiobjective portfolio optimization problems with three or more quadratic objective functions, a class of problems that has not been solved before. Computational examples are provided.

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