Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/205288 
Year of Publication: 
2018
Series/Report no.: 
Tinbergen Institute Discussion Paper No. TI 2018-099/III
Publisher: 
Tinbergen Institute, Amsterdam and Rotterdam
Abstract: 
We investigate covariance matrix estimation in vast-dimensional spaces of 1,500 up to 2,000 stocks using fundamental factor models (FFMs). FFMs are the typical benchmark in the asset management industry and depart from the usual statistical factor models and the factor models with observed factors used in the statistical and finance literature. Little is known about estimation risk in FFMs in high dimensions. We investigate whether recent linear and non-linear shrinkage methods help to reduce the estimation risk in the asset return covariance matrix. Our findings indicate that modest improvements are possible using high-dimensional shrinkage techniques. The gains, however, are not realized using standard plug-in shrinkage parameters from the literature, but require sample dependent tuning.
Subjects: 
Portfolio allocation
high dimensions
linear and non-linear shrinkage
factor models
JEL: 
G11
C38
C58
C55
Document Type: 
Working Paper

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