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| Type | Bachelor's Thesis |
| Scope | Discipline-based scholarship |
| Title | Conditions for the application of a geometric-mean portfolio optimization framework subjected to a risk restriciton in contrast to the conventional arithmetic-mean-variance portfolio optimization framework |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Number of Pages | 83 |
| Date | 2017 |
| Abstract Text | This thesis inquires into conditions under which the application of geometric-mean portfolio optimization framework results in different optimal portfolio choices in con trast to the arithmetic-mean-variance portfolio optimization framework and therefore may offer additional benefit to the investor over the mean-variance approach. lt is shown, that the difference in results arises out of choosing or forgoing to subject the geometric-mean optimization to a particular measure of risk. An analysis of equiv alence between the mean-variance and the geometric-mean optimization is clone for subjection of the latter to variance, value-at-risk and expected shortfall. The analysis results in conditions of equivalence for each of these three cases. Violation of these conditions are cause for the difference in optimal portfolio choices. Furthermore it is shown, that a trade-off formulation of the respective optimization exists in some cases, a formulation that may allow for better accounting of an investor's risk profile. |
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