Performance Analysis and Optimal Selection of Large Minimum Variance Portfolios Under Estimation Risk

@article{Rubio2012PerformanceAA,
  title={Performance Analysis and Optimal Selection of Large Minimum Variance Portfolios Under Estimation Risk},
  author={Francisco Rubio and Xavier Mestre and Daniel P{\'e}rez Palomar},
  journal={IEEE Journal of Selected Topics in Signal Processing},
  year={2012},
  volume={6},
  pages={337-350}
}
We study the realized variance of sample minimum variance portfolios of arbitrarily high dimension. We consider the use of covariance matrix estimators based on shrinkage and weighted sampling. For such improved portfolio implementations, the otherwise intractable problem of characterizing the realized variance is tackled here by analyzing the asymptotic convergence of the risk measure. Rather than relying on less insightful classical asymptotics, we manage to deliver results in a practically… CONTINUE READING
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