• Why Tinbergen Institute?
• Program Structure
• Courses
• Course Registration
• Facilities
• Students offices
• Location
• Housing
• Student Council
• Recent PhD Placements
• Research
• News
• Events
• Summer School
• Behavioral Macro and Complexity
• Econometrics and Data Science Methods for Business and Economics and Finance
• Inequalities in Health and Healthcare
• Introduction in Genome-Wide Data Analysis
• Research on Productivity, Trade, and Growth
• Summer School Business Data Science Program
• Events Calendar
• Tinbergen Institute Lectures
• Annual Tinbergen Institute Conference
• Events Archive
• Summer School
• Alumni
• PhD Theses
• Master Theses
• Selected Recent Placements
• Key alumni publications
• Alumni Community
• Times
Home | Events Archive | Optimal Shrinkage-Based Portfolio Selection in High-Dimensions
Seminar

# Optimal Shrinkage-Based Portfolio Selection in High-Dimensions

• Series
• Speaker
Nestor Parolya (TU Delft)
• Field
Econometrics
• Location
University of Amsterdam and online (hybrid seminar), room E5.22
Amsterdam
• Date and time

March 04, 2022
16:00 - 17:15

Abstract
In this talk we provide an overview of our recent findings in the high-dimensional mean- and minimum variance portfolio optimization using the theory of random matrices. We construct the regularized and non-regularized linear shrinkage estimators for the portfolio weights which are distribution-free and optimal in the sense of maximizing with probability one the asymptotic out-of-sample expected utility or minimization of the out-of-sample variance. The asymptotic properties of the new estimators are investigated when the number of assets $p$ and the sample size $n$ tend simultaneously to infinity such that $p/n \rightarrow c\in (0,+\infty)$. The results are obtained under weak assumptions imposed on the distribution of the asset returns, namely the existence of the $4+\varepsilon$ moments is only required. The suggested estimators show significant improvements over the existent approaches including the nonlinear shrinkage estimator and the three-fund portfolio rule, especially when the portfolio dimension is larger than the sample size. Moreover, they are robust to the deviations from normality.

If you are interested to join thisseminar online, please send an email to seminar@tinbergen.nl before Friday morning March 4.