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Impact of the Reference Covariance Matrix in Portfolio Optimization Techniques with Shrinkage

De Saint Moulin, Antoine
(2022)

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DeSaintMoulin_48951400_2022.pdf
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Details

Supervisors
Frédéric Vrins
Faculty
Louvain School of Management
Degree label
Master [120] : ingénieur de gestion, à finalité spécialisée
Abstract
This work focuses on efficient portfolio computation methods and more specifically on the estimation of the covariance matrix. First, theoretical reminders about portfolio optimization are presented. A special focus is made on shrinkage-based method (i.e. Ledoit \& Wolf) and principal component analysis (PCA). Next, different strategies are compared and new shrinkage- and PCA-based portfolio optimization methods are presented. Then, the performance achieved with these methods and those from the literature are compared and discussed.