Optimal Linear Combinations of Portfolios Subject to Estimation Risk
Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
The combination of two or more portfolio rules is theoretically convex in return-risk space, which provides for a new class of portfolio rules that gives purpose to the Mean-Variance framework out-of-sample. The author investigates the performance loss from estimation risk between the unconstrained Mean-Variance portfolio and the out-of-sample Global Minimum Variance portfolio. A new two-fund rule is developed in a specific class of combined rules, between the equally weighted portfolio and a mean-variance portfolio with the covariance matrix being estimated by linear shrinkage. The study shows that this rule performs well out-of-sample when covariance estimation error and bias are balanced. The rule is performing at least as good as its peer group in this class of combined rules.
Place, publisher, year, edition, pages
2015. , 69 p.
Markowitz, Portfolio Optimization, Diversification, Convex Optimums, Linear Combinations, Estimation Risk, Parameter Uncertainty, Global Minimum Variance, Mean-Variance Analysis, Naive Diversification, Modern Portfolio Theory, Allocation, Shrinkage, Covariance Estimation
Mathematics Economics Probability Theory and Statistics
IdentifiersURN: urn:nbn:se:mdh:diva-28524OAI: oai:DiVA.org:mdh-28524DiVA: diva2:827368
Subject / course
2015-06-04, U3-083, Högskoleplan 1, Västerås, 18:15 (English)
Carlsson, Linus, Senior LecturerPettersson, Lars, Senior Lecturer
Malyarenko, Anatoliy, Professor