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Mean-Variance Portfolio Optimization: Eigendecomposition-Based MethodsPrimeFaces.cw("AccordionPanel","widget_formSmash_some",{id:"formSmash:some",widgetVar:"widget_formSmash_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_all",{id:"formSmash:all",widgetVar:"widget_formSmash_all",multiple:true});
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PrimeFaces.cw("AccordionPanel","widget_formSmash_responsibleOrgs",{id:"formSmash:responsibleOrgs",widgetVar:"widget_formSmash_responsibleOrgs",multiple:true}); 2015 (English)Licentiate thesis, comprehensive summary (Other academic)
##### Abstract [en]

##### Place, publisher, year, edition, pages

Linköping: Linköping University Electronic Press, 2015. , p. 51
##### Series

Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1717
##### National Category

Mathematics
##### Identifiers

URN: urn:nbn:se:liu:diva-118362DOI: 10.3384/lic.diva.118362ISBN: 978-91-7519-038-9 (print)OAI: oai:DiVA.org:liu-118362DiVA, id: diva2:814636
##### Presentation

2015-06-09, ACAS, A-huset, ingång 17, Campus Valla, Linköpings universitet, Linköping, 10:15 (English)
##### Opponent

PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt648",{id:"formSmash:j_idt648",widgetVar:"widget_formSmash_j_idt648",multiple:true});
##### Supervisors

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#####

PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt667",{id:"formSmash:j_idt667",widgetVar:"widget_formSmash_j_idt667",multiple:true});
Available from: 2015-05-27 Created: 2015-05-27 Last updated: 2015-05-27Bibliographically approved
##### List of papers

Modern portfolio theory is about determining how to distribute capital among available securities such that, for a given level of risk, the expected return is maximized, or for a given level of return, the associated risk is minimized. In the pioneering work of Markowitz in 1952, variance was used as a measure of risk, which gave rise to the wellknown mean-variance portfolio optimization model. Although other mean-risk models have been proposed in the literature, the mean-variance model continues to be the backbone of modern portfolio theory and it is still commonly applied. The scope of this thesis is a solution technique for the mean-variance model in which eigendecomposition of the covariance matrix is performed.

The first part of the thesis is a review of the mean-risk models that have been suggested in the literature. For each of them, the properties of the model are discussed and the solution methods are presented, as well as some insight into possible areas of future research.

The second part of the thesis is two research papers. In the first of these, a solution technique for solving the mean-variance problem is proposed. This technique involves making an eigendecomposition of the covariance matrix and solving an approximate problem that includes only relatively few eigenvalues and corresponding eigenvectors. The method gives strong bounds on the exact solution in a reasonable amount of computing time, and can thus be used to solve large-scale mean-variance problems.

The second paper studies the mean-variance model with cardinality constraints, that is, with a restricted number of securities included in the portfolio, and the solution technique from the first paper is extended to solve such problems. Near-optimal solutions to large-scale cardinality constrained mean-variance portfolio optimization problems are obtained within a reasonable amount of computing time, compared to the time required by a commercial general-purpose solver.

1. Eigendecomposition of the mean-variance portfolio optimization model$(function(){PrimeFaces.cw("OverlayPanel","overlay814620",{id:"formSmash:j_idt723:0:j_idt737",widgetVar:"overlay814620",target:"formSmash:j_idt723:0:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

2. Tight Upper Bounds on the Cardinality Constrained Mean-Variance Portfolio Optimization Problem Using Truncated Eigendecomposition$(function(){PrimeFaces.cw("OverlayPanel","overlay814624",{id:"formSmash:j_idt723:1:j_idt737",widgetVar:"overlay814624",target:"formSmash:j_idt723:1:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

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