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1	Optimal Linear Combinations of Portfolios Subject to Estimation Risk Jonsson, Robin January 2015 (has links) 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. 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