Estimation Risk and Shrinkage in Vast-Dimensional Fundamental Factor Models

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Tinbergen Institute Discussion Paper ; No. TI 2018-099/III

Classification and Topics

• Classification: Wirtschaft ; Portfolio Choice; Investment Decisions ; Multiple or Simultaneous Equation Models: Classification Methods; Cluster Analysis; Principal Components; Factor Models ; Financial Econometrics ; Large Data Sets: Modeling and Analysis
• Subject: Portfolio allocation ; high dimensions ; linear and non-linear shrinkage ; factor models

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): van Vlodrop, Andries C. ; Lucas, André
• Event: Veröffentlichung
• (who): Tinbergen Institute
• (where): Amsterdam and Rotterdam
• (when): 2018

Further information

• Handle: http://hdl.handle.net/10419/205288
• Last update: 10.03.2025, 11:42 AM CET