• Corpus ID: 218581532

# Statistical inference for the EU portfolio in high dimensions

@article{Bodnar2020StatisticalIF,
title={Statistical inference for the EU portfolio in high dimensions},
author={Taras Bodnar and Solomiia Dmytriv and Yarema Okhrin and Nestor Parolya and Wolfgang Schmid},
journal={arXiv: Portfolio Management},
year={2020}
}
• Published 10 May 2020
• Computer Science, Mathematics
• arXiv: Portfolio Management
In this paper, using the shrinkage-based approach for portfolio weights and modern results from random matrix theory we construct an effective procedure for testing the efficiency of the expected utility (EU) portfolio and discuss the asymptotic behavior of the proposed test statistic under the high-dimensional asymptotic regime, namely when the number of assets $p$ increases at the same rate as the sample size $n$ such that their ratio $p/n$ approaches a positive constant $c\in(0,1)$ as \$n\to…

## References

SHOWING 1-10 OF 48 REFERENCES

Many studies demonstrate that inference for the parameters arising in portfolio optimization often fails. The recent literature shows that this phenomenon is mainly due to a high‐dimensional asset
• Mathematics
Journal of Business & Economic Statistics
• 2021
ABSTRACT In this article, we estimate the mean-variance portfolio in the high-dimensional case using the recent results from the theory of random matrices. We construct a linear shrinkage estimator
• Mathematics
• 2007
In this paper we consider the weights of the global minimum variance portfolio (GMVP). The returns are assumed to follow a matrix elliptically contoured distribution, i.e., the returns are assumed to
• Economics, Mathematics
• 2008
The paper discusses finite sample properties of optimal portfolio weights, estimated expected portfolio return, and portfolio variance. The first estimator assumes the asset returns to be
• Computer Science, Mathematics
IEEE Journal of Selected Topics in Signal Processing
• 2012
This work analyzes the asymptotic convergence of the risk measure of sample minimum variance portfolios of arbitrarily high dimension and proposes a generalized consistent estimator of the out-of-sample portfolio variance that only depends on the set of observed returns.
• Economics
• 2007
Abstract This paper proposes a multivariate shrinkage estimator for the optimal portfolio weights. The estimated classical Markowitz weights are shrunk to the deterministic target portfolio weights.
• Economics
AStA Advances in Statistical Analysis
• 2019
The problem of how to determine portfolio weights so that the variance of portfolio returns is minimized has been given considerable attention in the literature, and several methods have been
• Economics
Manag. Sci.
• 2007
Close-form non-Bayesian adjustments of classical estimates of portfolio mean and standard deviation are provided, which significantly reduce bias in international equity portfolios, increase economic gains, and are robust to sample size and to nonnormality.