Portfolio Optimization Using a Consistent Vector-Based MSE Estimation Approach

نویسندگان

چکیده

This paper is concerned with optimizing the weights of global minimum-variance portfolio (GMVP) in high-dimensional settings where both observation and population dimensions grow at a bounded ratio. Optimizing GMVP highly influenced by data covariance matrix estimation. In setting, it well known that sample not proper estimator true since invertible when we have fewer observations than dimension. Even more observations, may be well-conditioned. determines based on regularized to overcome abovementioned difficulties. Unlike other methods, selection regularization parameter achieved minimizing mean squared error an estimate noise vector accounts for uncertainty Using random-matrix-theory tools, derive consistent achievable allows us find optimal using simple line search. Simulation results demonstrate effectiveness proposed method dimension larger than, or same order of, number samples.

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ژورنال

عنوان ژورنال: IEEE Access

سال: 2022

ISSN: ['2169-3536']

DOI: https://doi.org/10.1109/access.2022.3197896